Network Video Recorders

3xLOGIC, the foremost provider of integrated and intelligent security solutions, has launched VIGIL 13.5, the latest update to its VIGIL Video Management System (VMS). The new release introduces AI Bridge and Face Recognition, allowing organizations to deploy advanced AI-powered video analytics and security intelligence without replacing existing camera infrastructure. VIGIL 13.5 has been designed to support users who wish to improve operational awareness, automate security workflows, and modernise surveillance capabilities while maximising their existing investment. AI Bridge brings AI analytics to legacy ecosystems AI Bridge is a hardware layer that runs deep learning analytics on video from existing IP cameras. It applies edge-based deep learning analytics to existing IP camera deployments, enabling standard cameras to deliver intelligent detection and real-time alerts without the cost and disruption of a complete hardware refresh. For dealers and integrators, AI Bridge creates new opportunities to bring AI capabilities into existing customer environments while reducing barriers to adoption. Organizations can extend the life of their current camera infrastructure, accelerate AI adoption, and build a future-ready surveillance strategy without undertaking costly rip-and-replace projects. AI Bridge supports a broad range of AI-powered analytics, including person, vehicle, object, perimeter and loitering detection, dwell time, people counting, and fall detection. This makes it particularly attractive to retailers, educational institutions, healthcare, hospitality and entertainment, warehouses, and smart city leaders. Object detection, for example, can help operators track unexpected vehicles on an academic campus or warehouse yard.  Fall detection ensures a quick response if a person in a clinic or resident in a care home falls, while entry and exit detection will alert to possible absconding. This ensures individuals remain safe while staff can continue their duties with peace of mind that the system will alert them to any unusual behavior or emergencies.  In retail settings, people-counting can improve wait times and queue management in a store, ensuring a good customer experience. Intrusion detection can secure back offices and stockrooms, and dwell times can tell leaders what areas of a store are most popular.  Vendor-neutral offers greater flexibility The platform is vendor-neutral, supports mixed camera environments, and can be centrally configured through the VIGIL Server Management Utility (VSMU). Customers can also combine AI Bridge along with 3xLOGIC's Edge-Based Deep Learning cameras within a single VIGIL deployment, providing a flexible approach to modernisation across hybrid environments. Supporting proactive security VIGIL 13.5 also introduces Face Recognition capabilities to the VIGIL platform, helping security teams move beyond passive video monitoring to responsible, proactive awareness. Using edge-based AI processing, the feature allows organizations to create secure local watchlists, receive real-time alerts when persons of interest are detected, rapidly search recorded footage, and automate workflows based on facial recognition events. Designed with privacy in mind, facial recognition processing takes place at the edge, helping organizations maintain control of sensitive data while improving investigation speed, situational awareness, and employee efficiency. Part of the wider ecosystem The latest release integrates seamlessly with the wider 3xLOGIC ecosystem, including VIGIL CLOUD, PACOM VIGIL CORE and SONITROL CORE, inviting users to deploy AI analytics across on-premises, cloud and hybrid environments. AI Bridge is available as either a perpetual license for on-premise deployment or as a subscription through VIGIL CLOUD, giving customers the flexibility to align deployment with operation and budget requirements. Mike Poe, Director of Product Management at 3xLOGIC said: "AI is helping organizations unlock more value from the infrastructure they already own. Now, with VIGIL 13.5, customers can add powerful AI analytics and facial recognition to existing camera deployments without the cost and disruption of replacing their entire surveillance estate. Whether they're looking to improve security, increase efficiency or begin their AI journey, VIGIL 13.5 provides a practical and scalable path forward."

Inner Range releases Inception 7.3, delivering new wireless compatibility and a range of usability improvements that further strengthen Inception as a unified access control, video surveillance and security management platform. Inception 7.3 prepares the platform for the upcoming IR Wireless RF Gateway and Remote Controls, due for release before the end of Q3 2026. Security management platform Once available, the solution will enable seamless integration of wireless remote devices into Inception. Administrators will be able to configure remote templates, assign actions to programmable buttons and enroll remotes directly to individual users, providing a simple and flexible way to arm and disarm areas, trigger automation functions and manage site operations without wired infrastructure. The Area Test Walk and Hardware Test functions have been streamlined to improve accessibility, reducing permission-related barriers during testing and commissioning. Operational workflows have also been enhanced with improved progress feedback during firmware updates and protected execution of critical actions such as mobile credential management. Mobile credential management For customers using IR Video, Inception 7.3 introduces easier device management, including the ability to remove video devices directly from the gateway interface. Additional refinements improve event handling, camera health management and overall integration reliability. Inception 7.3 includes an extensive list of performance enhancements across the platform, covering network resilience, credential management, system warnings, review events, reporting, language localisation, touchscreen operation, mobile access terminology, firmware management and access control functionality. Together, these improve day-to-day system performance and the overall operator experience. Mobile access terminology Key Highlights of Inception 7.3 Support for the pending release of the IR Wireless RF Gateway and Remote Controls Simplified testing and commissioning workflows Improved firmware update visibility and reliability Enhanced IR Video management and monitoring Extensive security and usability improvements throughout the platform Inception 7.3 is available now through Wesco in ANZ, APAC, Europe, the Middle East, Africa, USA, Canada and South America.

Iluminar, Inc., a pioneer in infrared and white light illuminator manufacturing, is alerting organizations to time-limited federal tax credits available for its Power Stack vertical solar poles. These benefits will expire for solar infrastructure builds not concluded by December 31, 2027, giving businesses, schools, non-profits, and municipalities a limited window to capture incentives before the deadline. Under the federal Clean Electricity Investment Credit (26 USC § 48E), Power Stack’s vertical solar poles qualify as an eligible clean energy asset and meet the criteria for a standard 30% baseline credit. That baseline can be stacked with additional credits, bringing the total eligible return on investment up to 70% and transforming a standard security expense into a strategic capital asset. Underground grid infrastructure These federal incentives are accessible to organizations of every kind. Nonprofit organizations can use the Direct Pay provision (26 USC § 6417) to receive the full eligible credit value as a direct cash payment from the Internal Revenue Service (IRS). Taxable businesses can use the transferability clause under 26 USC § 6418 to sell eligible credits to a third-party taxpayer for cash, converting the credit directly into project value. Many states offer additional incentives that can be layered on top of federal benefits, stretching project budgets even further. The Iluminar Power Stack is also uniquely engineered to help organizations meet the 2027 placed-in-service deadline with minimal disruption. Most poles can be installed in under one hour and raised and serviced by a ground-level crew. This effectively prevents construction delays and eliminates dependence on underground grid infrastructure. Solar battery storage The Power Stack solar poles support Iluminar illuminators, as well as IP cameras, license plate recognition systems, wireless connectivity, access control, and other IoT sensors, with up to five days of dedicated solar battery storage. Each pole delivers uninterrupted power through localised grid failures and severe weather events, protecting organizations from the potential losses that follow security downtime. “The use of clean, reliable energy has historically been the smart choice for ensuring continuous security operations, but these federal incentives make it the smart financial choice too,” said Eddie Reynolds, president and CEO of Iluminar. “We want to make it as easy as possible for organizations to take advantage of these credits while they can.” Solar tax incentives are subject to federal and state law, IRS guidance, and project documentation. Organizations are advised to consult their tax advisor before relying on any credit estimate.

Hanwha Vision, a pioneer in video surveillance and security solutions, announces that selected Hanwha Vision Wisenet 9 network cameras have been granted the German Federal Office for Information Security (BSI) IT Security Label. The Label demonstrates Hanwha Vision's commitment to meeting recognized cybersecurity requirements for connected devices, supporting customers’ ESG objectives and preparations for the EU Cyber Resilience Act (CRA). The German Federal Office for Information Security (BSI) granted the IT Security Label to 41 Hanwha Vision network cameras based on the requirements of the European security standard ETSI EN 303 645 for smart consumer devices. The label applies to selected Wisenet 9 X and P Series products included within the approved BSI application. Informed purchasing decisions The IT Security Label enables manufacturers to transparently communicate the cybersecurity features of eligible products while helping customers make more informed purchasing decisions. As part of the label, Hanwha Vision has declared compliance with recognized cybersecurity requirements and commits to maintaining these requirements throughout the validity of the label, reporting newly identified vulnerabilities to the BSI and addressing them appropriately. As organizations prepare for the introduction of the EU Cyber Resilience Act, cybersecurity has become a vital consideration when selecting security technology partners. The principles underpinning the BSI IT Security Label closely align with many of the requirements introduced through the forthcoming legislation, providing users with additional confidence when investing in secure, resilient video surveillance solutions. Maintaining product security For security installers, system integrators and end users, the label provides greater transparency into the cybersecurity measures implemented within eligible Hanwha Vision products, supporting buying decisions and demonstrating an ongoing commitment to maintaining product security throughout the product lifecycle. Cybersecurity has long been a core element of Hanwha Vision's product development strategy. Wisenet 9 cameras incorporate multiple security technologies designed to protect devices, data and communications throughout deployment and operation. This includes Secure Boot and Secure OS, and on-device Dynamic Privacy Masking (DPM). The granting of the BSI IT Security Label further demonstrates Hanwha Vision's continued investment in secure-by-design innovation, helping users deploy video surveillance solutions with confidence while supporting broader ESG goals.

For decades, surveillance intelligence meant faster detection, more cameras, sharper resolution, and quicker alerts. That equation is now being rewritten. As video systems move from recognizing objects to understanding context, the real differentiator is no longer accuracy in ideal conditions, but reliability amid chaos: monsoon distortion, fluctuating crowds, inconsistent infrastructure. In this conversation, Tuhin Bose, Senior Vice President and CTO at Videonetics, unpacks what “True AI” actually demands from an engineering standpoint: explainability built into the architecture, resilience designed in from day one, and lifecycle management at scale across 150+ cities and 80+ airports. He discusses why India’s unpredictable operating conditions have become an unlikely engineering advantage, and where video intelligence is headed next toward semantic convergence, multimodal fusion, and AI systems organizations can genuinely trust.” Deep learning systems CIO&Leader: You’ve described “True AI” as trained, contextual, and explainable rather than just another deep-learning buzzword. From an engineering standpoint, what specifically separates a context-aware model from a system that’s simply running pattern recognition at scale? Tuhin Bose: The distinction begins with the difference between recognition and understanding. Conventional deep learning systems are highly effective at recognizing objects or detecting predefined events: they can identify a person, vehicle, or object with impressive accuracy. However, recognizing an object is fundamentally different from understanding what is happening within a scene, why it matters, and whether it requires intervention. A context-aware AI model integrates spatial, temporal, and behavioral intelligence. Rather than analyzing individual frames in isolation, it understands relationships between objects, how activities evolve, and what constitutes normal versus anomalous behavior within a specific environment. This enables the system to generate actionable intelligence instead of simply triggering alerts based on predefined rules. Conventional analytics system Take crowd management as an example. A conventional analytics system may count the number of people in a particular area and generate an alert when a predefined threshold is exceeded. A context-aware AI model goes much further. It analyses behavioral patterns, identifies abnormal clustering, correlates the activity with historical crowd flow, and recommends preventive action before congestion escalates into a safety concern. That is the difference between reacting to predefined rules and deriving actionable intelligence from the scene. This philosophy underpins what they call True AI. Their AI-powered video analytics engine is designed not only to detect objects but also to interpret scenes, recognize activities, detect anomalies, and continuously learn and adapt to changing operational environments. Equally important is explainability. Whether AI is deployed in public safety, transportation, or enterprise operations, organizations need to understand why a recommendation was made before acting on it. This builds trust, accelerates decision-making, and enables businesses to realize greater operational value from AI while maintaining transparency and accountability. Unpredictable crowd density CIO&Leader: Videonetics builds for environments with fluctuating light, dust, monsoon distortion, and unpredictable crowd density. Walk them through how the architect models to stay reliable under those conditions rather than degrading the way many lab-trained systems do in the field. Tuhin Bose: One of the biggest misconceptions in AI is that high benchmark accuracy automatically translates into reliable field performance. Production environments are inherently unpredictable. India presents one of the most demanding operating environments for computer vision. Lighting changes rapidly, dust and monsoon rain distort images, crowd density fluctuates constantly, and camera quality, network availability, and infrastructure can vary significantly across deployments. Their engineering philosophy has therefore always been to optimize for consistent accuracy in chaotic conditions, rather than peak accuracy under ideal ones. Expected operating condition Architecturally, they approach this as a full-stack engineering problem rather than a model problem. Their pipeline begins with intelligent pre-processing to stabilise video streams and mitigate issues such as noise, motion blur, occlusions, and environmental distortions before they reach the inference engine. The deep learning models are trained on highly diverse, real-world datasets, so they learn to perform reliably under imperfect conditions rather than relying on pristine video quality. Finally, contextual post-processing correlates detections across time and multiple data points to refine outcomes, minimize false positives, and improve overall reliability. In their view, degraded video is an expected operating condition, not an exception. Broader operational context This intelligence is delivered through their AI-Enabled Video Analytics integrated with the Unified Video Management System, allowing video data to be interpreted within a broader operational context rather than as isolated events. The architecture also balances edge processing for low-latency decisions with centralized processing for analytics, storage, and model lifecycle management, ensuring reliable performance even across large, distributed deployments. This helps organizations improve operational efficiency, reduce manual intervention and false positives, and realize stronger ROI by maximising the value of their AI and surveillance investments. A good example of this is their statewide deployment in Andhra Pradesh, where the platform manages about 15,000 IP cameras across 28 districts. At that scale, environmental variability is a constant, from changing weather conditions and traffic density to heterogeneous camera infrastructure. It reinforces an important engineering principle: resilience is not something you add after building the model; it must be designed into every layer of the architecture from the outset. Influencing operational decisions CIO&Leader: Explainability and audit-readiness are central to your platform philosophy, especially given the growing regulatory scrutiny, such as the RBI’s data localisation and retention mandates. How do you actually engineer a deep learning system to be explainable, and what trade-offs does that impose on raw model performance? Tuhin Bose: For them, explainability is not something that gets added after a model is trained; it is built into the overall system architecture. Whether supporting enterprise campuses, manufacturing facilities, transportation hubs, or public infrastructure, explainability is essential because AI-generated insights must be trusted before they can influence operational decisions. Configurable retention policies That is why their approach goes beyond the AI model itself. Their Unified Video Management Platform combines AI-Enabled Video Analytics with Video Management, Face Recognition, and Traffic Management capabilities to correlate events, maintain contextual information, and provide a structured operational view rather than isolated detections. Instead of simply generating an alert, the platform supports behavior analysis, activity recognition, anomaly detection, and searchable event records, enabling operators to investigate incidents with greater confidence and traceability. Audit-readiness is equally dependent on platform engineering. Strong data governance, configurable retention policies, secure storage, role-based access controls, and comprehensive event records are essential to help organizations meet evolving regulatory and compliance requirements, including data localisation mandates where applicable. Particularly in regulated sectors such as banking and critical infrastructure, explainability is as much about demonstrating how evidence is managed as it is about how AI reaches a decision. Stronger operational efficiency There is naturally a trade-off. Pursuing increasingly complex models can sometimes deliver incremental improvements in benchmark accuracy while making systems more difficult to interpret, validate and maintain at scale. Their engineering philosophy has therefore been to balance model sophistication with transparency, operational reliability and long-term maintainability. In enterprise environments, long-term value comes not from marginal gains in benchmark accuracy alone, but from AI systems that organizations can trust, govern and scale confidently. That balance ultimately enables stronger operational efficiency, regulatory compliance and more consistent business outcomes. Recalibrating individual cameras CIO&Leader: With deployments spanning 150+ cities and 80+ airports, what does your retraining and lifecycle management process look like at that scale? How do you keep models accurate and up to date without constant manual recalibration across thousands of cameras? Tuhin Bose: Once AI is deployed at the scale of 150+ cities, over 80 airports, and more than 100 enterprise environments, the challenge is no longer training a model but ensuring that intelligence remains accurate, reliable, and operationally consistent as environments evolve. At that scale, manually recalibrating individual cameras is neither practical nor sustainable. Lifecycle management therefore becomes a disciplined engineering process rather than a maintenance exercise. Disrupting live operations Their approach is centered on continuously improving models through real-world operational learning. Data from diverse deployment environments is systematically collected, curated, annotated, and validated before being used to refine AI models. Every model update undergoes extensive testing across varied environmental conditions, camera configurations, and deployment scenarios to ensure consistent accuracy and prevent performance regressions. This rigorous validation process enables them to roll out enhancements in a controlled manner, allowing the platform to continuously evolve without disrupting live operations. Equally important is designing AI systems that are resilient to changing environments. Rather than relying on frequent manual recalibration, they build adaptive AI frameworks capable of handling variations in lighting, weather, camera angles and scene dynamics across large-scale deployments. Combined with continuous performance monitoring and structured model governance, this ensures the AI remains accurate, scalable and dependable throughout its operational lifecycle while reducing the need for manual intervention. AI-powered video intelligence CIO&Leader: You’ve spoken about future systems needing stronger defenses against tampering and adversarial manipulation as video intelligence becomes more mission-critical. What does that threat landscape look like today, and how is Videonetics’ R&D responding to it architecturally? Tuhin Bose: As businesses increasingly rely on AI-powered video intelligence to support operational decision-making, security, and business continuity, the threat landscape is evolving beyond traditional cybersecurity. Today, adversarial attacks are designed to exploit vulnerabilities in deep learning models by introducing subtle manipulations to input data that can influence AI predictions without being immediately apparent to human operators. Alongside concerns such as video tampering and data integrity, this makes trustworthiness a crucial design consideration for modern video intelligence systems. Video intelligence systems Their R&D approach is to build resilience into the architecture from the ground up. That means developing modular and interoperable systems with secure-by-design principles, robust data protection, disaster-recovery capabilities, and resilient AI models that can operate reliably in dynamic, real-world environments. Rather than treating security as an additional layer, they integrate it across the entire lifecycle: from data acquisition and model development to deployment and evidence management. Security, explainability, and operational reliability must work together because each reinforces trust in AI-driven decisions. Looking ahead, they believe the next generation of video intelligence will be defined not just by how intelligent AI becomes, but by how trustworthy it remains. As AI assumes a greater role in operational decision-making, their focus is on building systems that are secure, scalable, and resilient by design. Trustworthy AI is ultimately about protecting both operational integrity and business value. Organizations must have confidence that AI-driven insights remain secure, reliable, and resilient as adoption scales across increasingly complex environments. Edge-to-cloud intelligence CIO&Leader: India presents a uniquely difficult testbed for computer vision, yet you’ve built a platform competitive enough for global deployment. What capabilities did solving for India’s specific conditions force you to build that you might not have developed in a more controlled Western market context? Tuhin Bose: India has fundamentally shaped their engineering philosophy. Rather than building AI for predictable environments, they have had to engineer platforms that can operate reliably amid constant variability. This has pushed them to develop capabilities that go well beyond core computer vision models. For example, they have built highly adaptive AI that can generalise across diverse deployment scenarios without requiring extensive site-specific tuning, modular architectures that integrate seamlessly with heterogeneous camera and IT ecosystems, and edge-to-cloud intelligence that continues to deliver actionable insights even in environments with varying network conditions. Strong model governance Another key differentiator has been scalability. Supporting deployments across cities, airports and enterprise campuses has required us to design AI that is operationally resilient, with strong model governance, continuous validation and lifecycle management built into the platform. Equally important has been creating open, interoperable systems that allow organizations to modernise existing surveillance infrastructure rather than replace it. These capabilities are key in India, where infrastructure is rarely standardized, but they have also become a competitive advantage in international markets facing similar integration and scalability challenges. India’s rapidly expanding AI ecosystem has further accelerated this innovation. The country’s AI market is projected to reach US$17 billion by 2027, growing at 25-35% annually, while initiatives such as the IndiaAI Mission are strengthening access to compute infrastructure, research and indigenous AI development. Together, these factors have enabled us to build, validate and scale AI solutions in one of the world’s most demanding operating environments. As a result, the capabilities they have developed have become a competitive advantage not only in India but also in global markets, enabling organizations to benefit from faster deployments, lower implementation complexity, more consistent operational outcomes and a faster ROI from their AI investments. Isolated video events CIO&Leader: The industry is moving from siloed surveillance toward what you call “semantic convergence,” where video data across systems is correlated and queried in natural language. What’s the underlying technical shift that makes this possible now, and what infrastructure bottlenecks still stand in the way? Tuhin Bose: They use the term semantic convergence to describe the shift from analyzing isolated video events to understanding relationships across multiple systems. Instead of treating every camera or application as an independent source of information, AI can now correlate people, objects, behaviours, locations, and timelines to create a unified operational picture. Multiple video systems For years, surveillance systems generated vast amounts of video data, but each system largely operated in isolation. The fundamental shift today is that advances in deep learning, multimodal AI, and generative AI have enabled machines to understand context rather than detect objects. This allows semantically connected information from multiple video systems to be transformed into collective intelligence, enabling organizations to move beyond isolated alerts toward contextual, outcome-driven decision-making. Natural language interaction is another important enabler of this shift. Instead of manually reviewing footage or navigating multiple systems, operators can retrieve contextual information using conversational queries, significantly reducing investigation time and improving situational awareness. The emphasis is no longer on analyzing individual video streams but on extracting operational intelligence to help organizations respond faster and make better decisions. Unlocking semantic convergence The remaining challenge is infrastructure. Many organizations still operate in fragmented environments where surveillance, access control, traffic management, and other operational systems have been deployed independently over time. Unlocking semantic convergence requires interoperable platforms, standardized metadata, scalable computing infrastructure, and governance frameworks that allow contextual information to move securely across systems. As these foundations mature, video intelligence will evolve from a monitoring tool into an intelligent decision-support layer for enterprises and smart infrastructure. Default deployment model CIO&Leader: Looking at where video AI heads next, you’ve flagged edge-ready hardware, distributed inference, and fusion with other sensor modalities as the frontier. Which of these is closest to production-ready at Videonetics today, and which is still squarely in R&D? Tuhin Bose: The closest to production maturity is undoubtedly edge-ready AI combined with distributed inference. Customers today expect real-time intelligence, whether it is detecting a security incident, managing traffic, or monitoring critical infrastructure. Processing intelligence closer to where video is generated reduces latency, optimizes bandwidth usage, and enables faster decision-making without relying entirely on centralized computing. They see this becoming the default deployment model as organizations increasingly demand scalable, resilient, and always-on AI. Natural language interfaces The next frontier is multimodal intelligence. While video provides rich contextual information, its value increases significantly when it is correlated with data from access control systems, IoT devices, environmental sensors, traffic infrastructure, and enterprise applications. The objective is not simply to combine more data, but to enable AI to understand operational context and generate meaningful, cross-domain insights. Achieving that level of semantic understanding requires advances in sensor fusion, interoperability, common data models, and contextual reasoning, which remain active areas of R&D. Looking further ahead, the real transformation will come when these technologies converge with AI agents and natural language interfaces. The future is about AI becoming an intelligent collaborator that understands context, correlates information across systems, and intuitively presents actionable recommendations. That is where video intelligence evolves beyond surveillance into a true operational intelligence platform, and that is the direction of their R&D continues to pursue.

Artificial intelligence is transforming video surveillance from a passive recording system into an intelligent platform for real-time decision-making. Powered by deep learning, computer vision, and edge computing, AI-driven video intelligence enables organizations to move beyond post-incident investigation toward proactive security, operational efficiency, and predictive insights. As governments, enterprises, and critical infrastructure expand surveillance networks, the focus is shifting from deploying more cameras to extracting actionable intelligence from vast volumes of video data. This evolution demands scalable, interoperable, and privacy-conscious solutions capable of delivering real-time analytics that enhance safety, streamline operations, and support smarter, faster decision-making across diverse environments — not controlled demos. AI-powered video intelligence No Indian company has answered that call more decisively than Videonetics. Ranked India’s #1 Video Management Software provider by Omdia and recognized among the top players in Asia-Pacific, Videonetics has spent over 17 years engineering AI-powered video intelligence from the ground up — not adapting foreign technology, but building it indigenously, for the conditions the real world actually presents. Today, its solutions power 150+ cities, 80+ airports, critical infrastructure, manufacturing facilities, financial institutions, and enterprise campuses across India, Southeast Asia, and the Middle East. By combining advanced AI research with open architecture and customer-centric deployment strategies, Videonetics has positioned itself at the forefront of the industry’s transition toward intelligent video computing. Video intelligence platform Founded in 2008 by Dr. Tinku Acharya — an IEEE Fellow, holder of over 180 patents, and former Intel scientist — Videonetics began with a conviction that has never wavered: that video is one of the richest, most data-dense IoT sensors available, and that unlocking its potential requires purpose-engineered AI, not retrofitted analytics. That conviction produced India’s first indigenous Video Management Software. It has since produced a comprehensive AI-powered video intelligence platform serving smart cities, airports, critical infrastructure, manufacturing facilities, financial institutions, and enterprise campuses across 150+ cities in India, Southeast Asia, and the Middle East. According to Bhardwaj Naik, Senior Vice President and Chief Revenue Officer, the company’s founding insight was rooted in a clear market failure. “Organizations were deploying thousands of cameras, yet the intelligence extractable from footages remained practically zero. Surveillance was fundamentally reactive; a forensic tool used only after a failure occurred. Videonetics was built to change that equation.” Conventional facial recognition At the heart of the platform is “Deeper Look” — Videonetics’ proprietary deep learning engine, and the technology that separates the company from the field. Rather than limiting AI capabilities to conventional facial recognition or license plate identification, the platform analyses behavioral patterns, crowd dynamics, unusual activities, and structural anomalies, enabling organizations to detect risks before they escalate into incidents. One of the platform’s defining strengths is its fog computing architecture, which enables AI inference directly at the edge. Processing data closer to the source significantly reduces latency, allowing critical decisions be made almost instantly while remaining integrated with centralized command-and-control systems. This architecture becomes particularly valuable across airports, transportation networks, industrial facilities, and urban environments where rapid response is essential. Proprietary hardware traps Equally important is what Videonetics deliberately refuses to do. The platform is ONVIF compliant and hardware and OS agnostic — meaning it integrates across diverse camera ecosystems without locking customers into proprietary hardware. As Naik puts it plainly: “We refuse to lock our customers into proprietary hardware traps.” “At Videonetics, we are not building cameras smarter; we are building cities, industries, and institutions more intelligent. That is the standard we are setting, and we intend to own it globally." Next-generation analytics Adoption of AI-based video intelligence faces three persistent industry challenges, and Videonetics has structured its technology strategy specifically around overcoming each one. The first is what Naik calls the ‘Garbage In, Garbage Out’ trap, AI models that perform flawlessly in pristine demo environments but collapse under poor lighting, extreme weather, or chaotic real-world scenes. “DeeperLook” is hardened on massive, complex datasets drawn from some of the most demanding environments in India and globally. It is built to perform precisely where standard AI systems fail. The second is infrastructure debt. Most enterprises are constrained by legacy surveillance hardware that cannot easily accommodate next-generation analytics. Videonetics’ open, hardware and OS agnostic platform layers advanced AI capabilities directly over existing infrastructure — eliminating the need for costly, disruptive rip-and-replace programmes. Intelligent surveillance deployments The third is trust and compliance. As AI surveillance scales, privacy must be foundational — not an afterthought. Videonetics embeds privacy-by-design principles at the code level, ensuring advanced analytics and data anonymisation operate in strict alignment with global governance and data protection frameworks. Videonetics’ technology is already delivering measurable impact across numerous sectors, with one of its most notable deployments being the Andhra Pradesh RTGS CCTV360 project. Integrating approximately 15,000 cameras into a unified AI-powered command-and-control framework, the initiative represents one of the world’s largest intelligent surveillance deployments, supporting public safety, rapid law enforcement response, and large-scale situational awareness. Beyond public infrastructure, the company’s solutions are deployed across airports, banking, manufacturing, energy, retail, and smart city ecosystems, where AI-powered video intelligence contributes to faster threat detection, improved operational efficiency, regulatory compliance, and enhanced workplace safety. Critical infrastructure management In the near future, Naik envisions video intelligence fully maturing from a security tool into an autonomous business intelligence layer spanning urban mobility, industrial safety, and critical infrastructure management. The category is expanding; the question is who defines it. For Videonetics, the answer is unambiguous. The company’s long-term vision is absolute global leadership in True AI-powered video computing — delivering technology powerful enough for the world’s most demanding environments, and deployable enough for the world’s fastest-growing cities and industries. The goal: to enable organizations to operate with greater visibility, faster decision-making, and enhanced resilience in an increasingly connected world. After 17 years of indigenous innovation, a #1 ranking in India, and deployments that set global benchmarks, Videonetics is not chasing that standard. It is setting it.

For years, innovation in physical security has focused on what systems can do: higher-resolution cameras, AI-driven analytics, smarter access control, and cloud-based management. But across deployments, from residential systems to enterprise campuses, a more fundamental issue continues to undermine performance: Connectivity. It is increasingly clear that the effectiveness of modern security systems is not limited by sensors or software, but by the reliability, reach, and architecture of the networks that connect them. As security systems evolve toward real-time detection, automated response, and distributed intelligence, connectivity is no longer a supporting layer. It is the foundation and, too often, the weakest link. When security design is constrained by connectivity In theory, security systems are designed to maximize visibility, coverage, and response time. In practice, they are often designed around the limitations of network infrastructure. Nowhere is this more visible than in video surveillance. Cameras are frequently positioned not where risk is highest, but where connectivity is available. Dead zones in garages, basements, or perimeter edges force compromises in placement, reducing the effectiveness of the system. In some real-world deployments, traditional Wi-Fi has been shown to fail at a meaningful percentage of intended installation points, particularly in challenging environments or at the edges of coverage. This creates a disconnect between security intent and system reality. Instead of enabling proactive deterrence, such as detecting activity at the perimeter, systems are often relegated to recording events after they occur. The same pattern appears in access control. Many deployments rely on fragmented connectivity stacks, combinations of wired links, proprietary wireless protocols, and gateways, each optimized for a specific constraint such as range or power. While functional, these architectures introduce complexity, increase deployment costs, and create additional points of failure. Over time, these workarounds have become normalized. But as systems scale and expectations rise, their limitations are becoming harder to ignore. The hidden cost of fragmentation The consequences of unreliable or overly complex connectivity extend beyond performance. They impact the entire lifecycle of a security system. Advanced security features need consistent, reliable connectivity—not just bandwidthFor integrators, poor connectivity can mean longer installation times, more troubleshooting, and increased reliance on workarounds such as mesh networks or additional infrastructure. For vendors, it can translate into higher product return rates, increased support costs, and reduced customer satisfaction. For end users, the impact is more direct: systems that fail to connect reliably are systems that fail to deliver security. This is particularly critical as security solutions move toward more advanced use cases, including real-time alerts, remote management, and edge-based analytics. These capabilities depend not just on bandwidth, but on consistent, predictable connectivity across the entire deployment environment. Why traditional approaches are reaching their limits The industry has historically relied on a mix of connectivity options, each with its own strengths and tradeoffs. Wired infrastructure, such as Ethernet and PoE, offers reliability and performance but comes with higher installation costs and limited flexibility, particularly in large or distributed environments. Conventional Wi-Fi provides high throughput and seamless integration with IP networks but is optimized for short-range indoor use, where walls, distance, and interference can quickly degrade performance. Low-Power Wide-Area Networks (LPWAN) and cellular solutions extend range and coverage but often sacrifice throughput, increase latency, or introduce recurring operational costs. To compensate, many systems combine multiple technologies—layering gateways, protocol translation, and mesh architectures to bridge gaps. While effective in the short term, this approach increases system complexity and reduces long-term scalability. A shift toward simpler, more unified architectures In response, the industry is beginning to rethink connectivity, not as a patchwork of solutions, but as a unified foundation for modern security systems. The goal is straightforward: deliver long-range, reliable, and secure connectivity without adding architectural complexity. An example of this shift: Extending Wi-Fi beyond traditional limits One example of this shift can be seen in emerging Wi-Fi technologies designed specifically for long-range, low-power environments. Rather than replacing existing wireless approaches, these solutions aim to extend the familiar Wi-Fi model into new deployment scenarios where traditional networks struggle. Wi-Fi HaLow, based on the IEEE 802.11ah standard, is one such approach. Operating in sub-GHz spectrum, it enables significantly greater range and signal penetration compared to conventional 2.4 GHz and 5 GHz Wi-Fi, while maintaining native IP networking and established security frameworks. Wi-Fi trends reduce fragmentation and support data-heavy security systemsIn practical terms, this allows security devices, such as cameras and access control systems, to connect reliably across large properties, multi-building campuses, and outdoor environments without requiring dense access-point deployments or complex mesh configurations. It also supports higher data rates than many low-power wide-area technologies, enabling capabilities such as over-the-air updates, diagnostics, and increasingly, edge-based intelligence. At the same time, no single connectivity approach is universally optimal. Cellular remains essential for mobility, while LPWAN technologies continue to serve ultra-low-power sensing applications. Emerging Wi-Fi-based approaches highlight a broader industry direction: reducing fragmentation while supporting more demanding, data-rich security systems. From coverage to deterrence: A new security model One of the most significant implications of improved connectivity is the ability to rethink how security systems are deployed in the first place. Historically, limitations in wireless performance have pushed devices inward, closer to access points, inside buildings, and away from the perimeter. As a result, many systems are optimized for detection after the fact, rather than prevention at the edge. With more reliable long-range connectivity, this model begins to shift. Cameras and sensors can be placed where they are most effective—at entry points, along property boundaries, and in previously hard-to-reach areas. Combined with edge-based analytics, this enables earlier detection, faster response, and more effective deterrence. In this context, connectivity is not just an enabler of performance, it is a driver of fundamentally different security outcomes. Designing for the next generation of security systems As the industry moves forward, organizations deploying security systems should reassess how connectivity is factored into system design. Several principles are emerging: Prioritize reliability over peak performance Design for the perimeter, not just the interior Reduce architectural complexity Validate real-world performance, not just lab specifications The next wave of innovation in security will not be defined solely by smarter devices or more advanced analytics. It will be defined by whether those systems can connect, reliably, consistently, and at scale. Connectivity has long been treated as an invisible layer in security architecture. Today, it is becoming clear that it deserves far greater attention. As the industry rethinks its approach, one thing is certain: solving the connectivity challenge is not just a technical upgrade. It is a prerequisite for delivering on the full promise of modern security systems.

A security camera installed today has more AI processing power than the systems that guided early autonomous vehicle prototypes. And yet the operator who mounts that camera on a wall will, in all likelihood, never use most of that capability.  Industry surveys bear this out: a wide gap persists between the number of security professionals who believe AI can improve outcomes and the much smaller share who have adopted it operationally. The reason has nothing to do with the silicon and everything to do with how the industry has asked people to configure these systems. The problem is not that the industry lacks algorithms. The problem is that physical security has never found a scalable way to personalize systems for each site. The personalisation dilemma hiding in plain sight The problem is that physical security has never found a scalable way to personalize systems for each site A surveillance deployment at an airport, a retail chain, a school campus, and a logistics yard can look strikingly similar in hardware terms. Each installation uses image sensors, edge processors, network connectivity, and a management layer. What changes is what the operator cares about. At a school entrance, the priority might be perimeter approach after hours and controlled access during the day. At a loading dock, the concern is tailgating, vehicle dwell time, and safety incidents near forklifts. At an airport, the operator may need queue-flow analytics one moment, unattended-item detection the next, and then a search for a specific person of interest carrying a particular bag. At a retail store, loss prevention teams want to correlate customer flow patterns with point-of-sale data and identify suspicious behavior near high-value merchandise. This range of needs forces a reality that the industry has acknowledged in principle but never resolved in practice: the application pool across the market is vast, yet each individual site typically requires only a narrow set of outcomes. Each deployment needs personalisation once, at commissioning, and then again whenever the environment or the risk profile shifts. The app store that never became a market For the better part of a decade, the industry’s most visible answer to the personalisation problem was the “app store” model. The logic was straightforward: curate a marketplace of trained neural network algorithms, let integrators browse a catalog, and download the right analytic for each job. Queue counting for a passport control hall. License plate recognition for a parking structure. Occupancy monitoring for a conference room. The concept borrowed directly from the consumer smartphone approach. In practice, it never matched physical security’s purchasing and operating rhythm. A phone owner discovers and downloads new apps continuously. A physical security deployment selects one or two analytics functions at installation and rarely revisits them. Another maintenance burden A queue-counting algorithm trained on airport data is excellent at queue counting The economic incentive to maintain, curate, and update a broad catalog across a fragmented ecosystem of camera OEMs, VMS platforms, and system integrators never materialised when the average buyer drew from only a thin slice of it. And the question of who would operate such a marketplace across that fragmented landscape was never satisfactorily answered. The deeper issue is that distribution was not the hard part. Personalisation was. A queue-counting algorithm trained on airport data is excellent at queue counting. It does not naturally become a general-purpose security tool for whatever the operator needs next. Once a model is trained for a narrow task, adaptation requires another project, another integration cycle, and another maintenance burden. AI-enabled cameras The examples that do exist are instructive. Schiphol Airport in the Netherlands has used trained camera systems for over a decade to measure queue length at passport control and alert staff when additional counters should open. Rome trailed AI-enabled cameras to track pedestrian wait times at crosswalks, measure bus queue length, and monitor parking occupancy to support active transport and reduce vehicle emissions. These are effective, well-regarded deployments. They also illustrate the limitation: each required its own trained model, its own integration effort, and its own maintenance cycle. The queue-counting camera at Schiphol cannot be redeployed to detect an abandoned bag. That is a separate algorithm, a separate procurement, and a separate project. What changes with agentic AI Applied to physical security, this translates into a simpler commissioning experience Agentic AI points to a fundamentally different approach. An agentic system can receive goals expressed in natural language, determine the appropriate actions to fulfill those goals, execute those actions using available tools, and verify the results. Applied to physical security, this translates into a simpler commissioning experience: the operator expresses intent in plain language, and the system configures itself to achieve that intent. Consider the practical implications. An installer commissioning cameras at a retail location could type or speak a set of instructions: “Alert the manager if more than five people are waiting at checkout for longer than two minutes.” A facilities director could ask the system to “Track vehicles that enter the east parking lot after 9 p.m. and flag any that remain for more than 30 minutes.” A school security coordinator might specify: “Notify campus police if anyone approaches the perimeter fence between midnight and 5 a.m.” Appropriate perception capabilities None of these instructions require the operator to select a specific analytic from a catalog, configure a detection model, or define pixel-level zones in a complex VMS interface. The system interprets the intent, selects the appropriate perception capabilities, configures thresholds and context, and validates behavior over time. When the operator’s needs change, a new instruction replaces the old one. The camera hardware stays the same. The AI adapts. This is the core of the shift: minimal user input, maximum flexibility, and a security system that personalises itself without requiring the operator to navigate the traditional customize-certify-deploy cycle. Vision language models make it practical A conventional neural network trained for people counting can count people The enabling technology is the vision language model, or VLM. A VLM combines visual encoders with language reasoning, allowing it to interpret images or video in the context of natural language prompts. This is a qualitative leap beyond traditional convolutional neural networks, which classify or detect predefined objects and have no mechanism for open-ended interpretation. A conventional neural network trained for people counting can count people. It cannot distinguish between a crowd of commuters exiting a train station and a crowd assembling in protest. A VLM, by integrating contextual reasoning with visual analysis, can draw inferences that a task-specific model cannot. It can assess behavioral patterns, interpret spatial relationships, and respond to queries about scenes it has never been explicitly trained to analyze. Where a neural network might register two people carrying objects, a VLM could infer whether the scene suggests travellers with luggage or workers transporting equipment, provided the visual context supports that inference. Supporting multimodal input This matters in physical security because operational questions are rarely phrased as taxonomy labels. Operators want to express outcomes. They want to say “show me anything unusual near the loading bay after hours,” and the system should be able to reason about what “unusual” means given the site context. VLMs also support multimodal input. Audio cues such as a raised voice, a scream, an alarm, or breaking glass can contribute to scene interpretation when paired with video. In security applications, where events routinely unfold across both visual and auditory channels, this capability adds a meaningful layer of situational awareness. The edge constraint that forces discipline Large language models in the cloud use hundreds of billions of parameters and consume hundreds of watts None of this works if the architecture assumes data center conditions. Most surveillance cameras operate under strict power and thermal limits. Power over Ethernet (PoE), the standard delivery mechanism, typically provides between 15 and 30 watts depending on the PoE class, and only a fraction of that budget is available for AI processing after the sensor, ISP, video encoder, and network stack have taken their share. In many installations, the AI workload must fit within a few watts. Large language models in the cloud use hundreds of billions of parameters and consume hundreds of watts. That scale does not translate to a camera mounted on a pole or embedded in a ceiling tile. For agentic AI to work at the edge of a physical security network, the models must be compact, efficient, and designed for the purpose. Neural network acceleration This is where smaller, domain-specific VLMs become essential. Models trained on industry-relevant image and text datasets, combined with techniques such as pruning, quantisation, and parameter-efficient fine-tuning, can deliver meaningful visual reasoning within the compute and memory constraints of an edge processor. The result is a VLM that fits inside a camera’s power budget and still responds to natural language instructions with useful accuracy. Ambarella’s CVflow AI architecture, now in its third generation, was designed for this class of workload. The architecture integrates advanced neural network acceleration with high-resolution image signal processing and video encoding on a single system-on-chip, allowing cameras to run complex AI inference alongside their core imaging functions without exceeding the thermal and power boundaries that define edge deployments. The company's latest addition to its portfolio, the 4-nanometer CV7, runs CNNs and vision language models concurrently across multiple video streams while consuming 20 percent less power than its predecessor. For infrastructure and robotic applications requiring heavier models, the 5-nanometer N1 family supports multimodal LLMs in multi-camera configurations. Distributing intelligence across far edge, near edge, and cloud This tier must respond in milliseconds and operate within a fixed power envelope A workable agentic architecture for physical security distributes intelligence across three tiers, each matched to the processing demands and latency requirements of its role. At the far edge, inside the camera itself, the processor handles real-time perception: object detection, tracking, zone logic, and initial event classification. This tier must respond in milliseconds and operate within a fixed power envelope. At the near edge, on a local gateway or network video recorder, a more capable processor orchestrates across multiple cameras, maintains state, correlates events, retrieves site-specific policies and procedures, and classifies incidents requiring more context than any single camera provides. At the cloud/server tier, available when connectivity permits, the system accesses heavier models for forensic analysis, fleet-wide analytics, model updates, and long-horizon reporting. Periodic cloud access This tiered approach keeps the most time-sensitive decisions local, where latency is lowest and data privacy is strongest. It also means agentic capabilities can scale incrementally. A small installation might run entirely at the far edge with periodic cloud access. A large campus might employ all three tiers, with near-edge orchestration coordinating PTZ patrol patterns across dozens of cameras while the cloud generates shift summaries and updates models based on fleet-wide telemetry. In practice, a security workflow built on this pattern often combines real-time detection at the far edge, behavior-tree orchestration at the near edge for multi-camera coordination, local retrieval over site playbooks, and conservative safe-mode escalation when system confidence is low. The discipline of deterministic guardrails and structured verification loops is essential in security operations, where unpredictable system behavior is not acceptable. A hybrid future, with VLMs orchestrating specialist models The transition to agentic AI does not eliminate specialized neural networks The transition to agentic AI does not eliminate specialized neural networks. Purpose-trained models will continue to deliver superior accuracy for well-defined, high-frequency tasks such as license plate recognition, face matching, and fire and smoke detection. In a mature agentic system, the VLM acts as an orchestrator. It handles open-ended perception and natural language interaction while routing to specialized models when a task demands their precision. A PTZ camera at a transportation hub might receive the instruction “monitor the west concourse for unattended items.” The VLM interprets the request, manages the interface, and reasons over broader scene context. Real-time video processing When it identifies a candidate object, it routes to a dedicated abandoned-item classifier optimized for that specific validation step. The VLM orchestrates. The specialist model validates. The operator receives a refined, actionable alert. That hybrid pattern places specific demands on the silicon. The processor must support both traditional CNN inference and generative AI workloads simultaneously while maintaining real-time video processing within the same power envelope. The value of a tightly integrated SoC, one that combines an advanced ISP, a deep learning accelerator, and a video encoder on a single die, is that it eliminates the multi-chip complexity and power overhead that would otherwise make this approach impractical at the edge. Making agentic AI deployable for the ecosystem Ambarella’s Developer Zone, launched at CES 2026, provides a centralized portal of tools Physical security is built on a broad ecosystem of camera OEMs, VMS providers, independent software vendors, module builders, and system integrators. For agentic AI to reach the market at scale, these participants need model-ready tooling, reference workflows, and a practical path from prototype to production. This is where developer ecosystems become part of the story. Ambarella’s Developer Zone, launched at CES 2026, provides a centralized portal of tools, optimized AI models, agentic blueprints, low-code templates, and documentation aimed at accelerating edge AI application development on Ambarella’s SoCs. Common software stack ISVs and integrators can evaluate models, prototype applications, and deploy using a common software stack that spans the company’s CV7 and N1 SoC families through the Cooper development platform. That consistency across the product range reduces per-project engineering cost and accelerates time-to-market for partners building perception and analytics solutions. The point is broader than any single portal: agentic systems require components that have already been tested and optimized for edge deployment, so that integrators can focus on solving their customers' problems rather than rebuilding the AI pipeline from scratch. The ecosystem participants who lead the transition to agentic AI in physical security will be the ones with access to tooling that fits into their existing development and deployment processes. What comes next Physical security has searched for years for a scalable answer to personalisation Physical security has searched for years for a scalable answer to personalisation. The app store model did not provide it. Manual configuration, while functional on a per-site basis, scales poorly across large portfolios of cameras and changing operational requirements. Agentic AI offers a credible path forward because it aligns with how operators actually think. They express outcomes, not model specifications. They want systems that adapt to new requirements without repeated engineering cycles. Traditional neural networks With VLMs as the interface layer, smaller domain-specific models at the far edge, orchestration at the near edge, and disciplined verification loops throughout, personalisation can become a standard part of deployment rather than a custom project. The building blocks are now in place. Power-efficient edge AI processors can run VLMs and traditional neural networks simultaneously. Developer ecosystems are maturing to support rapid prototyping and deployment. Reference architectures for distributing intelligence across far-edge, near-edge, and cloud tiers are solidifying. For an industry that already installs vast numbers of AI-capable cameras each year, the opportunity is to make the intelligence already embedded in those endpoints genuinely usable for the people who rely on them every day.

In security, vision is paramount. The surveillance camera serves as an unwavering digital eye—the foundational source of truth. Yet a critical vulnerability persists: what happens when these “eyes” falter without warning? Corrupted footage or unexplained gaps in recorded evidence can render an entire system unreliable, turning trusted video into questionable data. Relying on chance is no longer a viable strategy. Traditionally, ensuring the health of a video surveillance system has been a manual, reactive burden. Periodic human checks are inherently flawed—prone to fatigue, oversight, and inefficiency. By the time an issue is discovered—whether a disconnected camera, a defocused lens, or a corrupted video file—the damage may already be done, compromising investigations, regulatory compliance, and public safety. This paradigm is shifting. Modern surveillance platforms are now embedding proactive intelligence directly into the video infrastructure. As an illustrative example, consider a system like SVMSPro , which operates as an automated guardian through two core, continuously running capabilities: 1. Proactive System Health: Automated Video Quality Diagnostics Imagine a dedicated technician for every camera, working in real time. The Video Quality Diagnostics module on the video surveillance management system SVMSPro does precisely that—not by merely displaying video, but by actively analyzing the live incoming stream. Signal Integrity: Instantly detects cameras going offline or suffering connection failures. Image Clarity: Automatically identifies blurring caused by tampering, misalignment, or environmental factors such as lens condensation. Color Fidelity: Flags abnormal color casts that could obscure critical visual details—like the color of clothing or a vehicle. Upon detecting any anomaly, the system generates a precise diagnostic log. This enables operators to locate and resolve issues—a loose cable, a dirty lens, or a misconfigured encoder—often before end users even notice a problem. This is proactive surveillance health management at scale. 2. Verifiable Evidence Integrity: Automated Recording Integrity Check The reliability of recorded footage is non-negotiable. A complementary capability—such as the Automated Recording Integrity Check found in systems like SVMSPro—provides certified assurance by performing a daily, meticulous audit of all video recorded in the past 24 hours. It validates the front-end recording stream to ensure: Seamless Continuity: Confirms there are no hidden gaps, dropped frames, or missing segments in the timeline. Perfect Integrity: Verifies that every stored video file remains uncorrupted and fully playable, safeguarding each frame as potential evidence. The result is a clear, tamper-resistant audit trail. For compliance reviews, forensic analysis, or legal proceedings, organizations gain verifiable proof that their video evidence is complete, authentic, and court-ready. The Transformative Impact Unprecedented Reliability: Drastically reduce blind spots by ensuring your entire camera network remains fully operational. Operational Efficiency: Eliminate hundreds of hours spent on manual verification, freeing personnel for higher-value security tasks. Forensic Confidence: Build investigations on a solid foundation of verified, trustworthy video. Proactive Maintenance: Address minor anomalies before they escalate into system-wide failures. Maximised Investment: Extend the lifespan and performance of existing surveillance infrastructure through intelligent oversight. Move beyond uncertainty. With intelligent capabilities like those exemplified by SVMSPro—namely, real-time Video Quality Diagnostics and automated Recording Integrity Checks—organizations no longer need to wonder whether their surveillance system is functioning correctly. They can know, with confidence, that every frame counts.

Combining VIVOTEK’s branded video security business with March Networks paves the way for the combined brands to meet customers’ increasing appetite for integrated solutions rather than individual components. The merger brings VIVOTEK's branded video security business together under one operating structure with March Networks’ enterprise-grade video surveillance, AI analytics, and cloud-managed security solutions. Customers now have access to a broader, more integrated portfolio spanning enterprise video management, advanced cameras, edge artificial intelligence (AI), cloud services, recording platforms, and business intelligence, says Net Payne, Chief Sales & Marketing Officer at March Networks and VIVOTEK. Distinct product strategies Given the combined companies, product development, sales, marketing, and customer support can move in step rather than in parallel. March Networks and VIVOTEK were already both part of the Delta Electronics family, but they previously operated as separate businesses with distinct product strategies, teams, and routes to market. March Networks and VIVOTEK announced the merger of their branded video security businesses last spring following Delta Electronics' acquisition of remaining VIVOTEK shares. “Aligning our complementary capabilities lets us innovate faster, simplify engagement, and respond more quickly to a market moving toward AI, cloud and intelligent video,” says Payne. This merger reflects a broader shift across the physical security industry, adds Payne. “Customers no longer see video purely as a tool for reviewing incidents after the fact,” he says. “They expect it to help identify risks earlier, simplify investigations, improve operations, and support better decisions across the organization.” Cloud video management Meeting customer expectations requires more than bolting AI features onto existing systems Meeting customer expectations requires more than bolting AI features onto existing systems. Rather, it requires cameras, edge intelligence, recording, software, cloud services, and business data working together as one coordinated platform. “By bringing March Networks and VIVOTEK together, we combine deep expertise across the full video technology stack while preserving the relationships, sector knowledge and customer focus both companies have built over many years,” says Payne. “Our goal is to make intelligent video more integrated, scalable, and practical for customers and partners worldwide.” Both entities bring strengths to the combined companies. March Networks brings experience in enterprise software, cloud video management and large-scale deployments; while VIVOTEK brings strength in imaging, camera technology and edge intelligence. Together they seek to ensure more choice when designing systems, a clearer path for future expansion, and technologies that work together more effectively without compromising the reliability, cybersecurity, and support customers expect from both companies. Advantages for channel partners Channel partners gain a broader portfolio to address a wider range of customer requirements, combining components as each deployment demands. Channel partners also benefit from expanded geographic reach, deeper technical expertise, and greater investment in training and enablement. “Importantly, this is designed to be a seamless transition,” says Payne. “Partners keep working with the teams and relationships they already know, while gaining access to new capabilities and certification opportunities. Over time, that alignment should make it easier to design, deploy and support complete solutions.” Because they are established and respected brands, March Networks and VIVOTEK branding will continue to be used. Rebranding is not a goal, and neither identity is being replaced by the other. Coordinated decision-making “The immediate priority is organizational alignment by making it easier for customers and partners to benefit from the combined portfolio,” says Payne. Product branding will continue to reflect the relevant portfolio and market context. As product roadmaps become more closely coordinated, customers can expect greater interoperability and a more cohesive experience, says Payne. Any future branding changes will be managed carefully and communicated clearly. Combining the businesses reduces duplication and enables coordinated decision-making across product strategy, engineering, sales, marketing, operations, and customer support. “Instead of developing capabilities independently, teams can share their expertise, align roadmaps, and direct investment to where it has the greatest customer impact,” says Payne. Combining specialized knowledge globally The combined organization operates across six continents and more than 75 countries The combined organization includes more than 300 research and development (R&D) engineers across four Centres of Excellence in Canada, Taiwan, Italy and Poland, thus bringing together a substantial base of specialist knowledge. The unified network also sharpens the ability to prioritise global opportunities, respond to regional needs and bring innovations to market faster. The combined organization operates across six continents and more than 75 countries, supported by more than 1,100 certified channel partners. That footprint provides greater capacity to support multinational customers while staying responsive to local market needs. Looking ahead, future growth will increasingly come from helping customers turn video into useful, actionable intelligence. “The market is moving beyond conventional surveillance toward solutions combining AI, cloud services, edge processing, video management and operational data — and our new structure brings these together as one connected solution rather than several isolated technologies,” says Payne. Broader technical expertise There will also be closer collaboration among specialists in imaging, hardware, software, analytics, and cloud, which is important given how much customer infrastructure, regulatory, and deployment requirements vary. The companies can support cloud, hybrid, and on-premise environments, giving organizations a practical path toward more intelligent video. More choice comes from bringing together complementary technologies across product groups. Greater scale is achieved through a larger engineering organization, broader technical expertise and more capacity to invest in innovation. Challenges for the industry at large Looking to the future for the industry at large, the greatest challenge will be managing the rapid growth of AI Looking to the future for the industry at large, the greatest challenge will be managing the rapid growth of AI responsibly, securely and at scale, says Payne. Video systems will generate more data and automate more decisions, but organizations must be able to trust how that intelligence is created, protected, and applied, he adds. “Cybersecurity, privacy, data sovereignty, system interoperability and AI accuracy are more important than ever,” says Payne. Addressing these elements requires expertise across the entire technology stack: from camera and edge processing to recording, cloud infrastructure, video management and analytics. “Together, March Networks and VIVOTEK can coordinate development across all these layers, giving customers flexible architectures that balance innovation with security, transparency and operational control,” says Payne.

The security landscape is undergoing a profound transformation, as evidenced by the innovations showcased at the ISC West Expo 2026. From dismantling of silos between physical and digital security to the ongoing transition to cloud and hybrid platforms, much of the discussion on the show floor was familiar, if somehow more urgent than ever. Clearly the industry is moving toward unified, cloud-native, and highly automated ecosystems. In a series of deep-dive meetings and demonstrations at the recent show, I got a first-hand glimpse of the future of the physical security marketplace. Spoiler alert: The future is now! HID Global: Tearing down the silos HID Global is addressing the long-requested convergence of physical and digital security. Recognizing that the industry has historically operated in silos, HID is now leading with a combined implementation team to leverage the power of both sides. HID is supporting the transition by combining its logical and physical security teams to help end users design secure, end-to-end journeys for their employees. By leveraging its massive existing footprint in physical access, HID is uniquely positioned to help organizations upgrade their cybersecurity posture without a total "rip and replace" of their hardware. HID is seeing a surge in demand for converged credentials that unite building access with digital identity. On the digital front, HID has implemented FIDO (Fast Identity Online) and passkeys to replace traditional passwords with more secure, biometric-backed authentication methods. Acre Security: Bridging the gap to the cloud Acre Security previewed a cloud-native video solution, reinforcing its cloud-first security strategyAcre Security is currently consolidating brands, bringing established names like Feenics and AccessIt under a single corporate umbrella. A primary theme for the company at ISC West is the migration of legacy systems to modern infrastructure. To facilitate the transition, they are launching "The Bridge," a technology designed to help on-premise customers transition to the cloud at a pace that suits their operational needs. The company is also expanding its portfolio into video. Attendees at the show received a preview of a new cloud-native video solution slated for release later this year, signaling Acre’s commitment to a holistic, cloud-first security suite. Acoem: Real-time acoustic threat detection With a 35-year foundation in vibration and acoustics—including military sniper detection—Acoem is providing gunshot detection through edge processing. Unlike systems that rely on the cloud and may suffer from latency, Acoem’s technology processes data at the sensor level for instant alerts. Their sensors are engineered for massive outdoor spaces, covering a 500-foot radius (roughly 11 football fields) and can detect high-powered rifle shots from up to half a mile away. The system provides critical intelligence by distinguishing between muzzle blasts and "mach noise" from bullet travel, delivering an audio file to the end-user that maps the location and direction of the threat. They use AI to filter out environmental false positives, such as skateboards hitting metal rails. Alarm.com: The all-in-one dealer ecosystem Beyond hardware, Alarm.com is leveraging AI to streamline the sales process Alarm.com is doubling down on "giving tools to dealers to help them be successful," with a focus on unified commercial solutions. A highlight at ISC West is their new fire communicator, which integrates fire monitoring into their single app, completing what they call the "fourth leg of the stool." The product allows fire systems to benefit from the same real-time customer engagement and push notifications that have long been available for intrusion and video. The key products, such as commercial-grade hardware, are now available for immediate purchase. Beyond hardware, Alarm.com is leveraging AI to streamline the sales process. Their new AI Proposal Builder uses customer meeting notes to automatically generate customized, value-driven sales proposals for dealers. Allegion: A global shift in credentialing Following the acquisition of ELATEC, Allegion is adopting a more global market approach and expanding its OEM business. The company is involved in the development of Aliro, the newly unveiled mobile credentialing standard. Allegion’s strategy is built on versatility: While Aliro serves the burgeoning mobile market, they also offer PKOC for plastic cards, allowing them to provide solutions regardless of the user's preferred credential type. The ELATEC acquisition has effectively filled previous gaps in their use-case portfolio, positioning them as a comprehensive provider for both physical and digital access needs. AtlasIED: Engineering for life safety Transitioning from audio to a fully automated threat detection company, AtlasIED showcased its new IPX line at ISC West. This modular platform uses a single PoE++ port to power various "plug-and-play" modules, allowing for easy system upgrades. Their gun detection technology achieves a 99% confidence rating by "stacking" AI models; it cross-references audio data with infrared signatures to detect muzzle flashes and air quality sensors for particulates. With systems already running in 80% of U.S. international airports, the company maintains 24/7 domestic engineering support to manage these "life-safety adjacent" environments. Axis Communications: Unifying connectivity Axis Communications is advancing network video with 4G/5G surveillance, reducing cabling needs Axis Communications continues to lead in network video, with a new focus on offering surveillance through 4G and 5G networks to eliminate the need for extensive cabling in long-range applications. Axis also introduced several specialized hardware pieces, including the P1486-LE global shutter camera for high-speed traffic monitoring and the Q2802-TE, which integrates thermal and visual monitoring into a single unit.  To assist technicians, they launched a new installer app that uses Bluetooth for easier camera pairing and management directly from a mobile device. Axon: De-escalation through enterprise wearables Focusing on frontline safety, Axon is expanding from law enforcement into the enterprise sector with its retail and healthcare-focused body camera. Weighing only 0.25 lb, these cameras are designed to be less intimidating, using colorful designs and acting as a primary de-escalator for aggressive behavior. A clear front display signals when recording is active, striking a balance between safety and a non-intimidating appearance. These devices feature live-streaming and panic alarms that connect workers to a Global Security Operations Center (GSOC). To ensure the footage is legally viable, Axon maintains a rigorous audit trail that authenticates every action recorded. Genetec: The value of the unified platform Genetec is advocating for unified platforms that reduce the time between incident detection and resolution. They argue that AI is becoming a commodity and that the true value lies in unified platforms that reduce the time from incident to resolution. Their approach emphasises the "outcomes" customers need, such as allowing security teams to coalesce around an investigation quickly. Genetec remains a strong supporter of open systems for enterprise customers, ensuring they have the choice to integrate diverse technologies rather than being locked into a manufacturer’s “walled garden.” This flexibility is critical for high-end security environments that require tailored, responsive systems. Johnson Controls: Large-scale enterprise solutions Johnson Controls has made a significant push into the enterprise video market with C-Cure IQ, a standalone VMS designed for major hubs like airports and large campuses. Through a partnership with Scylla, they have integrated 11 advanced analytics—including facial recognition and weapons detection—directly into their cameras. They also showcased new multi-sensor panoramic cameras capable of "stitching" multiple images into a single 360-degree view. While innovating in video, Johnson Controls also confirmed modern upgrades to their intrusion line, including their legacy DSC Neo line. The new DSC PowerSeries Neo 5, a high-end residential and commercial platform, features an all-new user interface and built-in PowerG+. They have also begun shipping the IQ5 family lineup, which is powered by a Qualcomm DragonWing processor.  ONVIF: Standardising the future of AI and cloud ONVIF continues to lead the charge in establishing common languages for the security industry. A major focus is their new "Cloud Profile," which enables cameras to connect directly to the cloud through a standardized protocol. They are also expanding into audio standards to ensure IP-based speakers can seamlessly connect to Video Management Systems (VMS). To combat the rise of sophisticated digital manipulation, ONVIF is developing "video signing" standards. ONVIF is actively seeking more diverse enterprise participation to help shape these emerging AI standardsThis feature protects the integrity of video for evidentiary purposes by embedding a digital signature and timestamp directly into the footage at the camera level. Additionally, the organization has formed an AI Working Group to develop common communication methods for "AI agents" within physical security systems. ONVIF is actively seeking more diverse enterprise participation to help shape these emerging AI standards. Roberto Licari is the new ONVIF Ambassador, seeking to attract new companies (even outside the security sector) to ONVIF as they look to expand their offerings. SwiftConnect: Bridging digital and physical identity SwiftConnect is redefining access control by shifting the focus from physical cards to digital identity. As a cloud-based "connected access network," the platform bridges the gap between IT identity providers (like Okta or Azure AD) and legacy physical access control systems. At the show's Security Experience Center demonstrations, SwiftConnect showcased its ability to integrate mobile credentials within Apple and Google Wallets across multiple technology platforms. By aligning physical security with Zero Trust principles, they ensure that access is governed by the same rigorous standards as digital networks. Their software-centric approach allows for a more seamless employee experience while simplifying the management of complex, multi-site building security. This emphasises a future where the mobile device becomes the primary tool for navigating the physical workspace. Wasabi Technologies: Redefining cloud storage economics With cloud technology becoming a baseline requirement at every booth, Wasabi Technologies highlighted its "hot cloud storage" as a cost-effective alternative to major hyperscalers. Their model claims to be 80% less expensive than competitors, notably removing hidden fees. Wasabi is increasingly bundled at the "back end" of security solutions through partnerships with integrators and end users. A key feature for law enforcement and high-security sectors is their "Object Lock" capability, which ensures that video evidence remains untampered with and immutable during defined retention periods. By providing high-performance storage without the unpredictable costs typically associated with the cloud, Wasabi is positioning itself as the foundational layer for data-heavy video surveillance applications that require long-term, secure retention. Wasabi is positioning itself as the foundational layer for data-heavy video surveillance applications ZKTeco USA: Versatile and integrated access ZKTeco USA showcased a range of integrated solutions, including turnstiles, visitor management, and access control. Their Atlas access control series offers versatility, supporting everything from traditional readers and keypads to QR codes and fingerprint scanning, with the flexibility to operate on-premise or in the cloud. For tight environments, they introduced the Mars 100 turnstile, which features a compact 2x3-foot footprint. They also highlighted the Omni series, an all-in-one standalone reader-controller that manages access, intercom, and visitor functions in a single device. Innovation in portability was evident in their new walk-through metal detectors, which consist of two poles with an 8-hour battery life—ideal for schools and temporary events. While they offer their own Armatura readers, ZKTeco emphasized that their solutions can integrate with other industry leaders to fit diverse customer needs.

Artificial Intelligence (AI) had a major presence at the ISC West 2026 show in Las Vegas. Almost every booth offered some variation on AI and how intelligence is transforming the physical security industry. Several industry leaders led the way, showcasing how they are tackling complex security challenges with smarter, more efficient solutions. Obviously, the security industry will never be the same. Milestone Systems: Responsible AI and open platforms Milestone has declared 2026 the "year of delivery” on previously announced developments and enhancements. A standout feature is their new natural language AI search, which is being integrated across XProtect, Arcules, and Briefcam platforms to simplify how users interact with vast amounts of video data. A core pillar of their strategy is responsible AI development, which prioritises using licensed data over "scraped" data. They have introduced new anonymisation tools that protect privacy by replacing faces with AI-generated characteristics like hair color while keeping the person unrecognisable. Furthermore, Milestone is moving workloads to Linux to reduce costs, transitioning toward an app marketplace, and has partnered with NVIDIA for the "Hafnia" project to ensure high-quality data to train AI models. Ambarella: Powering the edge with AI chips With their presence at ISC West in a meeting room near the trade show floor, Ambarella is the "engine" behind many high-performance intelligent video products, specializing in low-power AI chips like the N1 and CV7 families. Their technology is moving beyond cameras, where they provide systems-on-chips [SOCs] to many manufacturers. They have expanded into "AI boxes" that can process 64+ video channels simultaneously. A standout feature is the Natural Language technology, which allows users to search for specific objects or abstract scenes using simple prompts. To address modern security concerns, Ambarella has implemented "agentic" programming for a no-code development of automated workflows and media signing to combat AI-generated deepfakes by verifying video integrity through metadata. Motorola Solutions: Simplifying intelligence Motorola Solutions shows physical security evolving into a real-time enterprise intelligence layerMotorola Solutions is prioritising user accessibility by integrating natural language processing into its system configuration. Motorola's portfolio demonstrates how physical security technology is evolving into a real-time operational intelligence layer across the enterprise. Instead of requiring custom coding, operators can now use simple commands—like asking the system to "show me when someone fell down"—to set up advanced analytics and search parameters. The company is also deploying AI agents designed to aggregate data and automatically flag safety or compliance risks, such as perimeter breaches or blocked fire exits. To ensure maximum utility of the hardware, Motorola Solutions’ system can ingest massive, complex physical manuals and automatically generate monitoring rules based on those standard operating procedures. Furthermore, Motorola is focusing on interoperability, using industry-standard interfaces to ingest data from various hardware brands and networks. This approach aims to provide operators with clear, recommended next steps during critical events, streamlining the decision-making process in high-pressure environments. i-PRO: Putting generative AI at the edge i-PRO is bringing generative AI directly to the edge with a new fisheye camera, which processes complex analytics locally rather than relying solely on the cloud. These cameras, on display at ISC West, have moved beyond simple attribute-based tracking to identify complex behaviors like aggression, fighting, and slip-and-fall incidents. By running on the latest Ambarella chips, i-PRO cameras can use generative AI to improve image quality and analyze scenes in real-time without external processing. This focus on edge-based intelligence ensures high-performance analytics are available even in environments where constant cloud connectivity might be a challenge. Brivo: The rise of agentic AI Eagle Eye Video Assistant (EEVA) acts as a natural language AI agent that can monitor for specific threats Brivo and Eagle Eye Networks have officially unified under the Brivo name, focusing on streamlining the experience for large integrators. They are pioneers in agentic AI, introducing features that allow users to talk to their mobile apps to initiate lockdowns or add users. Their Eagle Eye Video Assistant (EEVA) acts as a natural language AI agent that can monitor for specific threats, such as a brandished weapon or a specific vehicle, and automatically trigger access control responses. By treating access and video as a unified system, Brivo enables users to connect specific video skills—like detecting a red car or a brandished weapon—to automated access control actions. They are also using AI to achieve "zero-cost integration," linking disparate cloud systems using AI-generated instructions. Everon: Emphasis on video monitoring Everon is making a massive investment in active video monitoring, using AI-driven systems to deter crime even before it happens. “The market is ripe for it, and customers are demanding it,” says David Charney, Everon’s Sr. Vice President, Video Command Center. Employing virtual guards who can use voice commands, lights, and horns to "shoo away" unauthorized individuals. The company has implemented senior-level leadership that has facilitated more than 5,000 video-based apprehensions. Everon, specializing in integrated systems for multi-site businesses, uses a rigorous selection process to choose AI providers that can accurately filter alerts for specific applications, such as identifying when a fire door is blocked. As a "trusted advisor" to their 300,000 customers, the company focuses on delivering professional-level results that move beyond basic recording to proactive threat mitigation. Hanwha: New hybrid VMS combines cloud and on-prem Hanwha Vision highlighted the official public launch of Blaze, a hybrid video management system (VMS). The platform uses a hybrid architecture to manage on-premise and cloud-connected devices across multiple sites without complex port forwarding. Blaze features native AI capabilities, including semantic search that allows operators to find specific incidents using natural language queries, such as "person with a safety jacket." The AI security platform lets teams search surveillance footage the way they search Google. AI similarity detection allows operators to quickly trace a person’s movement and investigate incidents faster (useful in active shooter behavior, medical emergencies, or crowd panic scenarios, and more). The system also provides a "histogram" view of traffic patterns to help security professionals quickly identify anomalies in historical footage. OpenEye: Operational intelligence and shift to OpEx OpenEye unveiled AI-powered features such as visual chat and scene analysisOpenEye is redefining video surveillance by moving intelligence from the camera level to the cloud, significantly reducing the need for on-site server management. At ISC West, OpenEye introduced new AI-driven tools like visual chat and scene analysis. Beyond security, these tools provide valuable operational alerts, such as identifying overflowing dumpsters, dirty tables in restaurants, or vehicles blocking dock doors. The system also utilizes natural language for search, allowing users to find specific attributes like "people wearing backpacks" within designated timeframes. OpenEye also highlighted an industry-wide shift toward operational expenditures (OpEx) billing models, as users increasingly prefer predictable monthly or yearly payments over large upfront capital expenditures (CapEx). This model aligns with their channel-based operations, meeting customers where they currently operate without the burden of high fees. IQSIGHT: New name, same mission for Bosch video Formerly Bosch Video, the newly named IQSIGHT seeks to maintain the “Bosch pedigree” under the new name. It’s the same engineering, manufacturing, etc. IQSIGHT also pledges to be "easier to do business with" by improving user interfaces and end-to-end user experiences. Despite the transition, the company has increased their pace of innovation and reinforced the open systems philosophy with enhanced integrations with Milestone, Genetec, and others. The major product release at the show is IVA Pro Context, which uses generative AI to provide human-level scene understanding. Through a partnership with Laelaps AI, a robotics startup, IQSIGHT is exploring autonomous dispatch and response. When a camera identifies a scene, it triggers a robot to provide a tailored response. Commercialisation is expected in 12 to 18 months. Verkada: How their system can solve a crime But how does AI operate in the real world? Verkada had a “themed” exhibit demonstrating how their system could solve a hypothetical theft at the Louvre museum in Paris. The exhibit took attendees through and showed how Verkada technologies (e.g., license plate recognition and search capabilities) could provide fast results to solve the crime. AI-powered tools, like their newest unified timeline, demonstrate value in the real world of investigations.

As Missouri’s largest and busiest aviation facility, St. Louis Lambert International Airport (STL) welcomed more than 15 million passengers in 2025. To implement a digital 360-degree surveillance system and secure over 70 million square feet of airport infrastructure, STL required a solution capable of covering vast spatial areas while significantly reducing infrastructure costs and complexity. At the same time, the airport aims to implement AI-applications to optimize its operations. With Dallmeier’s Panomera® multifocal sensor technology, STL achieved comprehensive high-resolution coverage with approximately 90 % fewer cameras and 75 % less infrastructure compared to the original surveillance design. Video security system Due to the continuous growth in passenger numbers, the STL Operations and Security Teams decided to modernise the existing video security system in June of 2018 in two areas: Firstly, in surface parking areas to increase customer safety and eliminate criminal activity such as vandalism and theft. The second solution targeted the airport’s four runways, the taxiways, and all terminal buildings to monitor the activities of aircraft, ground vehicles, and airport personnel. STL Airport Operations Team contacted their security integrator, Tech Electronics, to design and deploy an updated camera solution. The initial design used a combination of PTZ and single sensor IP cameras and would have required more than 130 cameras in total. From an infrastructure perspective, this would have required the installation of more than 42 new camera poles and the corresponding infrastructure (cable, network, power, etc.). The airside solution required significantly more technology to achieve the desired surveillance coverage. So, STL and Tech Electronics decided to look for another, more economical solution. Desired surveillance coverage During their technology search, STL discovered the Dallmeier Panomera® multifocal sensor technology, which was developed specifically for surveillance of large spatial areas using the fewest number of cameras to achieve the required surveillance coverage.  David Kulinsky, Deputy Director of Operations & Maintenance at STL, recalls: “When we discovered Panomera® and saw that one Panomera® camera can capture large areas at high resolution and replace several single-sensor cameras, including the costly infrastructure, it became clear that Panomera® provided STL a more economical solution with higher quality images at a lower overall cost.” Intuitive user experience The test installation at the airport proved the image quality of the Panomera®. Kevin Hubble, Services Manager for STL with Tech Electronics, comments: “I was surprised at how few Panomera® cameras were needed to replace several PTZs and single sensor cameras and display the images in a much more user-friendly format. The SeMSy® Compact VMS software enables the user to view a single high-resolution image of the entire scene with simple digital PTZ controls to ‘zoom’ into any part of the image in greater detail. The result is an intuitive user experience resulting in greater operational efficiency – not to mention the superior high resolution video coverage.” To date, STL has installed more than 27 Panomera® cameras. Multiple IPS 10000 recording appliances and 5 user workstations with the SeMSy® Compact VMS client software complete the video system. Compared to the original planning, this resulted in approximately 90 % reduction in the number of cameras and approximately 75 % reduction in infrastructure required for the installation. Comprehensive situational awareness A particular highlight of the deployment is STL’s digital 360-degree surveillance concept. Using only ten Panomera® cameras, the airport achieves comprehensive situational awareness across large airside areas including runways, taxiways and apron operations.  Unlike conventional camera concepts that require numerous PTZ and single-sensor cameras, Panomera® delivers a complete high-resolution overview in a single seamless image. This enables operators to monitor critical airport activities more efficiently across large spatial areas, while significantly reducing infrastructure complexity, maintenance effort and overall operating costs. AI-based video analysis Beyond security applications, STL is also evaluating Panomera® technology for operational process optimization inside the terminals. As part of a planned deployment in the ticketing area, a Panomera® V8 system will support AI-based video analysis functions such as passenger flow and waiting time analysis. The objective is to improve operational efficiency, optimize check-in processes and enhance the passenger experience — while leveraging the same high-resolution surveillance infrastructure already deployed throughout the airport. Video information technology David Kulinsky with STL summarises: “Any technology project at an airport must deliver a solution that is operationally efficient, resilient, and built for long-term performance. Our decision to deploy Panomera® cameras significantly enhanced overall surveillance coverage across the airport while reducing the technology and infrastructure footprint required to support it.” “The system also provides the ability to zoom into specific areas without sacrificing full-scene situational awareness or recording capabilities—an operational advantage over traditional PTZ cameras, which only record the field of view being actively monitored and can limit broader operational visibility.”  In 1984, Dieter Dallmeier founded what is now Dallmeier electronic – not in the proverbial garage, but in a garden shed in Regensburg, Germany. Today, the company, which can justifiably call itself a hidden champion for video information technology "Made in Germany", has several hundred employees worldwide, more than 250 of them at the company headquarters in the center of Regensburg alone. Digital image storage system Dallmeier's camera, recording, software, and analysis solutions optimize security and processes for B2B end customers in a wide range of industries in over 60 countries. The focus is on users from the casino, smart city, airports, logistics, stadiums, and industrial sectors. But also, banks, critical infrastructure facilities as well as medium-sized companies from all sectors. With pioneering innovations, Dallmeier has succeeded time and again in placing itself at the forefront of technology: From the world's first digital image storage system with motion analysis in 1992, the patented "multifocal sensor technology" Panomera® with its "Mountera®" mounting system, to the latest Domera® camera family, which allows up to 300 camera variants with only 18 components. Complete manufacturing process These and many other innovations provide real, tangible customer benefits. And with a low Total Cost of Ownership (TCO) and a high Return on Investment (ROI) they can easily compete with systems produced and supplied from low-wage countries. With "Made in Germany", we also guarantee our customers the highest standards in data protection, cybersecurity, and ethical responsibility. With high quality and short supply chains, we also ensure – almost incidentally – sustainability and environmental protection. From our prestigious headquarters in the center of Regensburg, Dallmeier not only carries out its own research and development but also the complete manufacturing process – from component to product to solution.

Gozoki, a French family-owned company and pioneer in the freshly prepared meals market (Maison Briau, Maison Tino and other brands), has taken the step of investing to secure its industrial sites. At its facilities in Agen, the company now benefits from a state-of-the-art video surveillance system deployed by specialist integrator SDP, using Hanwha Vision hardware and software solutions. Production sites in the food manufacturing sector face stringent requirements for security, access control and traceability. Protecting staff, preventing intrusions, monitoring logistics flows and securing sensitive areas all present significant challenges. Present significant challenges As part of its ongoing commitment to improving its infrastructure, Gozoki began modernising its surveillance system in 2025 to enhance reliability, accuracy and responsiveness. “We needed a system capable of operating 24/7 in a demanding environment while delivering outstanding image quality. The Hanwha Vision solution meets these requirements perfectly,” says Nicolas Jeannesson, buildings and energy manager at Gozoki. To deliver the project, Gozoki selected SDP, a company renowned for its expertise in integrating electronic security solutions. SDP carried out the site survey, designed the network architecture, installed the equipment and completed its advanced configuration. The objective was to deliver a robust, scalable solution perfectly suited to the demands of a challenging industrial environment. “Our role is to provide a turnkey solution that is reliable and built to last. Hanwha Vision cameras enable us to deliver high levels of performance while simplifying maintenance,” says Pierre Albanese, managing director of SDP. Video analytics capabilities The cameras SDP and Gozoki chose to install were Hanwha Vision’s, and were selected for their exceptional image quality, even in challenging conditions, thanks to high-resolution sensors and WDR technologies. Advanced video analytics capabilities that enable intrusion detection, intelligent management of sensitive areas, and behavioral analysis, were also key features that Gozoki required. The reliability and durability of Hanwha Vision’s cameras are essential in industrial environments that are subject to fluctuations in temperature, humidity or lighting, such as Gozoki’s sites. Enhanced cybersecurity - a critical factor in preventing the risk of cyber attacks on connected systems - was also a vital element of the project’s requirements. The installed cameras cover all strategic areas, including vehicle entrances, loading bays, storage areas, internal traffic routes and external perimeters. “The integrated video analytics allow us to go far beyond simple monitoring. We now have a decision-support tool capable of anticipating risk situations,” adds Nicolas Jeannesson of Gozoki. Future regulatory requirements With the planned deployment of nearly 200 cameras across multiple sites around Agen, Gozoki now benefits from a modern video surveillance system that supports the company’s growth while meeting current and future regulatory requirements. “This project perfectly illustrates our commitment to supporting our customers over the long term with reliable, scalable technologies,” concludes Pierre Albanese. Together, SDP and Hanwha Vision delivered Gozoki a cohesive, high-performance and scalable solution, providing a clear, centralized view of security across its facilities.

Stadiums and arenas are no longer places that come to life only on an event day. Increasingly, they are becoming year-round hubs for concerts, conferences, hospitality, retail, and community activity. The Tottenham Hotspur Stadium is one such example of a venue generating additional revenue and audiences beyond football matches, with its 2025 commercial income rising from £255.2 million to £277.1 million, due to hosting four National Football League (NFL) franchises, high-profile boxing events, and a Beyoncé summer concert series. Positive visitor experience Yet, as these venues grow more complex and multifaceted, so does the challenge of keeping visitors and staff safe throughout the year. Security is no longer simply about responding to incidents.; Instead, it has become a strategic function that enables stadium leadership to tailor security to different event types and risk levels, keep on top of health and safety and maintenance, and add value to marketing, sales, operations, and more. More pointedly, venue operators will soon have to comply with the Terrorism (Protection of Premises) Act 2025, better known as Martyn's Law, which received Royal Assent in April 2025 and is expected to come into force in spring 2027. With less than a year before implementation is anticipated, organizations responsible for large publicly accessible venues are increasingly assessing how they can strengthen preparedness while maintaining a positive visitor experience. Wider readiness strategy Named in memory of Martyn Hett, a victim of the Manchester Arena attack in 2017, the legislation introduces new responsibilities for venue operators to consider the risk of terrorism and put appropriate measures in place to help prevent, respond to, and reduce the impact of a terrorist attack if it occurs. Notably, for venues with attendees of over 800 people, physical protection such as video surveillance, vehicle access control, ground security, and more, must be considered and evaluated on a regular basis. AI-powered video solutions can help organizations meet their obligation under Martyn’s Law as part of a wider readiness strategy. Video surveillance systems Traditional video surveillance systems have always played a critical role in venue security, but their effectiveness has often relied on human operators monitoring dozens, or even hundreds, of video feeds simultaneously. In busy environments, critical details can be missed, particularly when security teams are managing large crowds, multiple entry points, and fast-moving situations. With AI-powered cameras and video management systems (VMS), operators no longer need to continuously monitor multiple video, chat, and sensor data streams. Instead, AI does the monitoring, flagging any suspicious activity, unauthorized objects (people and vehicles), crowd congestion, and so on, for operators to respond to if needed. Operators are freed up to focus on other activities, while the system will quickly alert them to events that require intervention, helping them respond to events rapidly and prevent situations from escalating. Ground teams also benefit from contextual, AI-powered insights that can help them track and locate a person or vehicle of interest, such as clothing color, direction of travel, and if they are potentially carrying a weapon. Multi-directional models By enabling security teams to assess and respond to situations before they develop into something more serious, venues are better prepared for some of the objectives underpinning Martyn's Law. The legislation places particular emphasis on preparedness, requiring organizations to consider how they would respond to incidents through measures such as evacuation, lockdown procedures, and effective communication. Situational awareness is fundamental to all four. Modern-day cameras equip operators with high-definition footage of an event or object of interest. Most cameras available on the market start at 1080p definition, with 4MP, 6MP, 8MP or 4K resolution options. Camera form factors have also improved, with dome, flat eye, bullet, fish eye, pan-tilt-zoom (PTZ), thermal, and multi-directional models providing venues with a range of options to fit any sized area and surveillance need. Monitoring crowd movements Real-time intelligence from video surveillance provides operators with a clearer understanding of what is happening across a venue and its surrounding environment. Vitally, AI-powered video analytics and AI object detection help operators to avoid missing events that need their attention and action. During an emergency, security teams can monitor crowd movements, identify potential bottlenecks, move towards the last known location of a potential attacker, and direct the public away from dangerous areas. Information on an attacker’s location, physical appearance, and direction of travel, plus the location of any casualties or people hiding within the venue, can be shared with on-site personnel and emergency services, helping to coordinate a faster and more efficient response. Multiple analytics triggers To further refine operator response times and efficiency, alerts can be set based on multiple analytics triggers, for example, if a person is detected crossing a line and then remains in a pre-defined area for a set number of seconds. For post-event investigations, operators can search footage quickly using the metadata stored within video. For example, they can pull up all footage of all people wearing a grey top, or, to filter this further, a child wearing a grey top. This improves the efficiency and speed of carrying out investigations. Generative AI tool Moreover, if a video analytics tool supports it, operators can use an AI-powered search feature that enables them to input a phrase or sentence of what they are looking for, with the system showing them all relevant results. This feature, known as semantic search, is a more natural way of searching akin to prompting a generative AI tool to come to a result. Operators can also select a specific area of interest to view all footage related to that area, allowing them to quickly find motion within that area. They can see all motion within that section on all captured video footage, to see, for example, who left a suspicious package in a room. Some video analytics systems can also pull footage of similar objects across multiple cameras, which can help track the direction of a vehicle or person of interest as they move across a busy and large event space. Wider entertainment districts Modern venue security increasingly extends beyond the perimeter fence or turnstile. Many new stadium developments are embedded within wider entertainment districts, retail destinations, and mixed-use communities. As a result, operators are responsible for understanding activity not only inside a venue but also across surrounding public spaces where large crowds gather before and after events. AI-enabled video helps operators to monitor large areas efficiently, maintaining situational awareness across multiple locations and mixed-use premises (the arena, retail, transport links, storage and backroom areas, and so forth). Operators gain a comprehensive understanding of potential risks and a stronger ability to respond before issues escalate. AI-powered video analytics There are also value-added benefits to other departments, such as marketing, sales, maintenance, staffing, and cleaning. Understanding and predicting busy periods and crowd flow patterns, alerting to long queue lengths, identifying congestion points, and monitoring vehicle movements and parking spaces all help to improve the visitor experience and streamline event operations. This is particularly useful for multi-use venues where one day a football match may be hosted, the next day a corporate event, and the following day a concert, with different stadium areas being used each time. Operators need visibility of how people move through their spaces and where resources are best deployed. AI-powered video analytics can provide that intelligence in real time. Venue leaders have a short window of opportunity to strengthen their security strategies ahead of Martyn’s Law coming into force. Investing in AI-powered video security shifts security into a proactive, value-generating function that meets the needs of today’s multi-use event spaces and helps to deliver the visitor experience that attendees have become accustomed to.

Viação Santa Brígida partnered with Dahua Technology to improve the safety and efficiency of its bus fleet in São Paulo, Brazil. The integrated solution enables real-time monitoring, reduces blind spots, supports passenger counting and fare-evasion control, and helps operators respond to incidents more effectively. Overview - Viação Santa Brígida is a major operator serving São Paulo with a fleet of more than 650 buses. To improve fleet supervision, passenger and driver safety, and operational visibility, the client partnered with Dahua Technology to deploy an intelligent mobile video surveillance and fleet management solution featuring onboard cameras, passenger counting, vehicle tracking, and centralized real-time monitoring. Potential safety risks Challenges - Managing hundreds of buses operating across a densely populated metropolitan area requires continuous coordination and reliable access to operational information. Viação Santa Brígida needed a more effective way to monitor its vehicles in real time and respond rapidly to incidents. Limited visibility inside and around buses made it difficult for the company to verify events, identify potential safety risks, and support drivers during daily operations. Blind spots around large vehicles also presented a concern, particularly during passenger boarding, manoeuvring, and interactions with pedestrians and other road users. Assessing vehicle occupancy The company additionally needed better tools to address fare evasion and monitor passenger movement. Without centralized and accurate data, assessing vehicle occupancy, reviewing suspicious activity, and identifying possible revenue losses could be time-consuming. Another priority was improving the efficiency of fleet management. Information from individual vehicles had to be consolidated and made readily accessible to the operations team, enabling managers to make faster, better-informed decisions. Centralized management software Solutions - Working with Dahua Technology, Viação Santa Brígida deployed an integrated mobile video surveillance solution combining strategically positioned onboard cameras, intelligent analytics, vehicle information, and centralized management software. Cameras installed inside the buses provide continuous recording and real-time monitoring of passenger areas. This allows the operations team to review incidents, observe passenger movement, and obtain reliable video evidence when necessary. The system also helps identify cases of fare evasion, supporting the protection of company revenue and improving operational control. Passenger-counting capabilities External cameras give drivers a more complete view of the areas surrounding each vehicle. By reducing blind spots, the solution helps drivers see pedestrians, passengers, and other nearby road users more clearly throughout the journey. This is particularly valuable during boarding and disembarking, when people may be positioned close to the vehicle or outside the driver’s direct field of vision. The deployment also incorporates intelligent passenger-counting capabilities. By automatically analyzing boarding activity, the system provides more accurate information about passenger volumes, vehicle occupancy, and potential discrepancies associated with revenue collection. Comprehensive view of fleet activity All information is brought together at Viação Santa Brígida’s Integrated Control Center, where operators can monitor the fleet through a video wall and Dahua’s mobile management software. The platform consolidates live and recorded video with vehicle location and operational information, giving the control team a clearer and more comprehensive view of fleet activity. The centralized system also supports the identification and investigation of unusual events. When an incident occurs, operators can access the relevant vehicle information and video footage, helping the company evaluate the situation and take appropriate action more efficiently. Continuous video monitoring Results - Dahua’s integrated mobile video surveillance solution has significantly improved Viação Santa Brígida’s fleet visibility by enabling continuous video monitoring, more accurate passenger counting, stronger fare-evasion control, and faster incident verification. External cameras also reduce blind spots, helping drivers operate more safely during daily journeys and passenger boarding. By centralizing video, location, and operational data in the Integrated Control Center, the company can analyze events more quickly and make better-informed decisions. Overall, the collaboration between Viação Santa Brígida and Dahua Technology has delivered a safer, more transparent, and more efficient approach to public transportation management, benefiting the company, its employees, and the thousands of passengers who rely on its services each day. “Today, we can continuously monitor the cameras installed across our buses and provide our managers with more accurate operational information. Dahua’s technology helps us control fare evasion, improve safety for drivers and passengers, and manage our fleet with much greater efficiency and confidence,” says a representative from Viação Santa Brígida.

Genetec Inc., the global pioneer in enterprise physical security software, announces that SoFi Stadium and Hollywood Park have unified their physical security operations using Genetec™ Security Center. Home to the Los Angeles Rams and Los Angeles Chargers, SoFi Stadium is the premier sports and entertainment destination located at Hollywood Park. The stadium has established itself as a world-class venue, hosting record-breaking concerts and global sporting events while consistently ranking among the top venues in the world. Global sporting events To support events of this scale, the organization standardized on Security Center to unify video surveillance, access control, automatic license plate recognition (ALPR), and other physical security systems in the 3.1 million-square-foot venue and across the 300-acre Hollywood Park development. Operators manage more than 3,000 cameras, 700 readers, and numerous other systems and sensors from three security control rooms, helping teams maintain situational awareness while streamlining day-to-day operations. "Genetec Security Center has improved our response time efficiency by giving us all the information we need in one platform. We’re able to understand what's happening and make decisions quickly," said Nick Bermensolo, Vice President of Security and Safety at SoFi Stadium and Hollywood Park. Physical security systems Since deploying Security Center, the SoFi Stadium IT team has a reliable, scalable, and cybersecure platform that runs on an isolated network and connects back to centralized servers. During big events, they support security operations by monitoring system performance from the Network Operations Center, ensuring everything is working as it should. The security team can easily handle daily tasks, while IT keeps systems running smoothly on the backend. "The Genetec team doesn’t just understand physical security systems, but IT systems too. With their help, we’ve been able to deploy a reliable and high-performing security platform at scale," said Paul Maldonado, Vice President of IT Infrastructure and Operations at SoFi Stadium and Hollywood Park. AI-based tools Looking ahead, both teams are focused on getting more value out of Security Center, exploring AI-based tools and more automation. They know they have a flexible, scalable platform that can support evolving cybersecurity and network requirements. "Genetec has really helped us establish a technology standard. Security Center is reliable and cybersecure, integrates well with our network, and reduces overhead for both IT and security. That’s the type of technology we want to continue implementing across our business," said Maldonado.

Carrefour Brazil is one of the largest retail groups in Brazil and Latin America, operating more than 1,000 stores and serving a high volume of customers every day. Its Pamplona store in São Paulo, for example, located in one of the city’s busiest areas, greatly reflects the operational complexity faced by modern retailers: maintaining safety, reducing losses, improving efficiency, and delivering a better shopping experience at scale. Limited real-time visibility Challenges - Carrefour needed to address several key pain points. Manual processes and disconnected systems created risks of inaccurate alarms and inconsistent service. High customer traffic increased exposure to theft, fraud, and checkout losses, while traditional investigation methods made incident tracing slow and inefficient. At the same time, limited real-time visibility into customer flow, queues, inventory, and in-store behavior made it difficult to optimize staffing, replenishment, store layout, and checkout efficiency. Unified retail ecosystem Solutions - To meet these needs, Dahua provided an integrated smart retail solution combining video surveillance, AI analytics, alarm linkage, customer flow analysis, and digital in-store technologies. The solution was built around Dahua’s DSS PRO platform, Retail Loss Prevention IVD/IVSS, Retail NVR, AcuPick intelligent search, people-counting cameras, Electronic Article Surveillance (EAS) integration, alarm host, access control, electronic shelf labels, and digital signage. These systems work together to connect security, operations, and customer engagement within a unified retail ecosystem. Integration of video surveillance For loss prevention, Dahua’s video analytics can detect suspicious behaviors such as item concealment and issue real-time alerts to security staff. Integration with EAS and intrusion alarms provides immediate contextual information for each alarm, helping teams respond faster and more accurately. High-value merchandise is further protected with anti-theft tags, while restricted storage areas are secured through access control. In the event of an incident, AcuPick and intelligent video analysis can be used to track the movement paths of customers or employees, significantly reducing investigation time. The solution is also projected to checkout and self-checkout security. Cart analysis can help to identify unscanned items, while intelligent barriers at self-checkout stations prevent customers from exiting before completing payment. Looking ahead, the integration of video surveillance with transaction data can further support automated sales auditing, discrepancy detection, and faster resolution of operational variances. Data-driven store operations Beyond security, Dahua’s smart retail technologies support data-driven store operations. People-counting cameras, customer flow analysis, queue monitoring, and heatmap analysis provide Carrefour with insights into occupancy, peak hours, movement patterns, high-traffic areas, and dwell time. This can help to optimize staff scheduling, product placement, promotional planning, and checkout lane management. Shelf replenishment monitoring enable detection of low stock or abnormal product removal and triggers real-time alerts, helping prevent lost sales and improving product availability. Dahua also enhanced the in-store customer experience through electronic shelf labels and digital signage. Electronic shelf labels enable automatic price updates, reduce manual errors, and support promotional communication. Digital signage and point-of-sale displays provide product information, strengthen brand presence, and create a more interactive shopping environment. Interactive shopping environment Results - As a result, Carrefour Brazil now operates in a safer, more efficient, and more connected retail environment. The project improves operational visibility, strengthens loss prevention, accelerates incident analysis, supports better staffing and merchandising decisions, and enhances the customer journey from entrance to checkout. Through this collaboration, Dahua helped Carrefour transform traditional store management into an intelligent retail model powered by integrated security, analytics, and digital engagement.

Adequate video surveillance coverage depends entirely on a facility's layout, square footage, and security goals. Historically, better coverage required more cameras, but technology innovations are changing those expectations and undermining previously held rules of thumb. We asked our Expert Panel Roundtable: Do today's video systems generally require more cameras or fewer cameras, and why?

Access control, video surveillance, and intrusion detection systems evolved largely independently in the physical security industry. Integrating those systems to the benefit of an enterprise's overall security has traditionally fallen on security integrators working in cooperation with manufacturers. However, today's market has also evolved to include companies that provide "unified platforms,” bringing together various disparate security systems and functions under a single, centralized management interface. We asked our Expert Panel Roundtable: What are the advantages of installing physical security systems as a unified platform?

The role of audio in physical security is multifaceted and increasingly vital, offering a layer of intelligence and interaction that access control and/or video surveillance alone cannot provide. Sounds provide crucial context to visual events. For instance, someone running might appear suspicious on video, but audio could reveal they are shouting for help, changing the interpretation entirely. Even so, historically speaking, audio has been an underused component in physical security. How is the situation changing? We asked our Expert Panel Roundtable: Is audio an underused component in today’s physical security systems? What obstacles are keeping audio from expanding?