Avigilon Network Video Recorders (NVR) / Network DVRs(70)
Real Time / Timelapse / Event, 300, Hardware solution, Inbuilt multiplexer, 84 TB, Multiple NVR support, Microsoft Windows Server 2012 R2, 100 ~ 240 V AC, 429 W, 32.5, 716 x 482 x 87, 10 ~ 35 C (50 ~ 95 F), 5 ~ 95Add to Compare
Real Time / Timelapse / Event, 128, Hardware solution, Inbuilt multiplexer, HDD , 12 TB, Microsoft Windows Embedded Standard 7, 100 ~ 240 V AC, 350, 13.8, 675 x 482 x 43, 10 ~ 35 C (50 ~ 95 F), 10 ~ 80Add to Compare
Real Time / Timelapse / Event, 128, Hardware solution, Inbuilt multiplexer, 24 TB, Multiple NVR support, Microsoft Windows Embedded Standard 7, 100 ~ 240 V AC, 270 W, 22.7 , 716 x 482 x 87, 10 ~ 35 C (50 ~ 95 F), 5 ~ 95Add to Compare
Real Time / Timelapse / Event, 128, Hardware solution, Inbuilt multiplexer, HDD , 18 TB, Microsoft Windows Embedded Standard 7, 100 ~ 240 V AC, 350, 13.8, 675 x 482 x 43, 10 ~ 35 C (50 ~ 95 F), 10 ~ 80Add to Compare
Real Time / Timelapse / Event, 128, Hardware solution, Inbuilt multiplexer, Recording, playback, and live streaming, HDD , 6 TB, Microsoft Windows Embedded Standard 7, 100 ~ 240 V AC, 350 W, 13.8, 675 x 482 x 43, 10 ~ 35 C (50 ~ 95 F), 5 ~ 95Add to Compare
Real Time / Timelapse / Event, 300, Hardware solution, Inbuilt multiplexer, 48 TB, Multiple NVR support, Microsoft Windows Server 2012 R2, 100 ~ 240 V AC, 429 W, 32.5, 716 x 482 x 87, 10 ~ 35 C (50 ~ 95 F), 5 ~ 95Add to Compare
Real Time / Timelapse / Event, 128, Hardware solution, Inbuilt multiplexer, HDD , 3 TB, Microsoft Windows Embedded Standard 7, 100 ~ 240 V AC, 350, 13.8, 675 x 482 x 43, 10 ~ 35 C (50 ~ 95 F), 10 ~ 80Add to Compare
Real Time / Timelapse / Event, 2 (monitors), Hardware solution, Inbuilt multiplexer, DVD-RW, Microsoft® Windows Embedded Standard 7, 100 ~ 240 V AC, 290 , 9.24, 175 x 360 x 435, 5 ~ 35 C (41 ~ 95 F), 20 ~ 80Add to Compare
Real Time / Timelapse / Event, 4, Hardware solution, Inbuilt multiplexer, 2 TB, 30 fps, 7K (30 MP) , Client Server NVR Application, Multiple client support, Multiple NVR support, 48 ~ 54 V DC, 20 W min.; 90 W max., 3.39, 240 x 169 x 44, 0 ~ 50 C (32 ~ 122 F), 10 ~ 90Add to Compare
Real Time / Timelapse / Event, 300, Hardware solution, Inbuilt multiplexer, 32 TB, Multiple NVR support, Microsoft Windows Server 2012 R2, 100 ~ 240 V AC, 429 W, 32.5, 716 x 482 x 87, 10 ~ 35 C (50 ~ 95 F), 5 ~ 95Add to Compare
Real Time / Timelapse / Event, 128, Hardware solution, Inbuilt multiplexer, 16 TB, Multiple NVR support, Microsoft Windows Embedded Standard 7, 100 ~ 240 V AC, 270 W, 22.7 , 716 x 482 x 87, 10 ~ 35 C (50 ~ 95 F), 5 ~ 95Add to Compare
Real Time / Timelapse / Event, 4 (monitors), Hardware solution, Inbuilt multiplexer, DVD-RW, Microsoft® Windows Embedded Standard 7, 100 ~ 240 V AC, 685, 13.5, 471 x 173 x 417, 10 ~ 35 C (50 ~ 95 F), 20 ~ 80Add to Compare
Real Time / Timelapse / Event, 150 ~ 300, Hardware solution, Inbuilt multiplexer, Recording, playback and live streaming, HDD , 137 TB, Microsoft Windows Server 2012 R2, 100 ~ 240 V AC, 429 ~ 750 , 42.5, 684 x 444 x 87.3, 10 ~ 35 C (50 ~ 95 F), 5 ~ 95Add to Compare
Real Time / Timelapse / Event, 300, Hardware solution, Inbuilt multiplexer, 56 TB, Multiple NVR support, Microsoft Windows Server 2012 R2, 100 ~ 240 V AC, 429 W, 32.5, 716 x 482 x 87, 10 ~ 35 C (50 ~ 95 F), 5 ~ 95Add to Compare
Real Time / Timelapse, 64, Hardware solution, Inbuilt multiplexer, HDD, 1 TB, DVD-RW, 30 fps, Microsoft Windows XP, Windows Vista, Windows 7, 100 ~ 240 V AC, 825 W, 17.7, 171 x 448 x 471, 10 ~ 35, 20 ~ 80Add to Compare
Real Time / Timelapse / Event, 144, Software solution, Hardware solution, Inbuilt multiplexer, PTZ control, Microsoft Windows 7 Professional 64-bit, 100 ~ 240 V AC, 635, 14, 173 x 414 x 471, 10 ~ 35 C (50 ~ 95 F), 20 ~ 80Add to Compare
The Avigilon AI Appliance adds patented Avigilon self-learning video analytics and Avigilon Appearance Search technology to almost any IP camera. When connected to Avigilon Control Center (ACC) software, customers can evolve legacy camera systems into powerful AI solutions, with automatic alerts of potentially critical events and the ability to search for and locate a person or vehicle of interest across an entire site. Engineered for high performance, capacity and resiliency, the AI Appliance will be offered in two models and features leading CPU and GPU hardware that simultaneously support Avigilon video analytics and Avigilon Appearance Search technology for up to sixty 2 MP resolution cameras.Add to Compare
4, Software solution, Hardware solution, Inbuilt multiplexer, 8 GB, DVD-RW, Expandable to multi NVRs, Direct client image streaming, Microsoft Windows 10 IoT Enterprise LTSB, 100 ~ 240 V AC, 290, 13.50, 471 x 173 x 417, 10 ~ 35 C (50 ~ 95 F), 20 ~ 80Add to Compare
Browse Network Video Recorders (NVR) / Network DVRs
Network Video Recorder (NVR) products updated recently
In 2017, IoT-based cyberattacks increased by 600%. As the industry moves towards the mass adoption of interconnected physical security devices, end users have found a plethora of advantages, broadening the scope of traditional video surveillance solutions beyond simple safety measures. Thanks in part to these recent advancements, our physical solutions are at a higher risk than ever before. With today’s ever evolving digital landscape and the increasing complexity of physical and cyber-attacks, it’s imperative to take specific precautions to combat these threats. Video surveillance systems Cybersecurity is not usually the first concern to come to mind When you think of a video surveillance system, cybersecurity is not usually the first concern to come to mind, since digital threats are usually thought of as separate from physical security. Unfortunately, these two are becoming increasingly intertwined as intruders continue to use inventive methods in order to access an organization's assets. Hacks and data breaches are among the top cyber concerns, but many overlook the fact that weak cybersecurity practices can lead to physical danger as well. Organizations that deploy video surveillance devices paired with advanced analytics programs often leave themselves vulnerable to a breach without even realizing it. While they may be intelligent, IoT devices are soft targets that cybercriminals and hackers can easily exploit, crippling a physical security system from the inside out. Physical security manufacturers Whether looking to simply gain access to internal data, or paralyze a system prior to a physical attack, allowing hackers easy access to surveillance systems can only end poorly. In order to stay competitive, manufacturers within the security industry are trading in their traditional analog technology and moving towards interconnected devices. Due to this, security can no longer be solely focused on the physical elements and end users have taken note. The first step towards more secured solutions starts with physical security manufacturers choosing to make cybersecurity a priority for all products, from endpoint to edge and beyond. Gone are the days of end users underestimating the importance of reliability within their solutions. Manufacturers that choose to invest time and research into the development of cyber-hardening will be ahead of the curve and an asset to all. Wireless communication systems Integrators also become complicit in any issues that may arise in the future Aside from simply making the commitment to improve cyber hygiene, there are solid steps that manufacturers can take. One simple action is incorporating tools and features into devices that allow end users to more easily configure their cyber protection settings. Similarly, working with a third party to perform penetration testing on products can help to ensure the backend security of IoT devices. This gives customers peace of mind and manufacturers a competitive edge. While deficient cybersecurity standards can reflect poorly on manufacturers by installing vulnerable devices on a network, integrators also become complicit in any issues that may arise in the future. Just last year, ADT was forced to settle a $16 million class action lawsuit when the company installed an unencrypted wireless communication system that rendered an organization open to hacks. Cybersecurity services In addition, we’ve all heard of the bans, taxes and tariffs the U.S. government has recently put on certain manufacturers, depending on their country of origin and cybersecurity practices. Lawsuits aside, employing proper cybersecurity standards can give integrators a competitive advantage. With the proliferation of hacks, malware, and ransomware, integrators that can ease their client's cyber-woes are already a step ahead. By choosing to work with cybersecurity-focused manufacturers who provide clients with vulnerability testing and educate end users on best practices, integrators can not only thrive but find new sources of RMR. Education, collaboration and participation are three pillars when tackling cybersecurity from all angles. For dealers and integrators who have yet to add cybersecurity services to their business portfolios, scouting out a strategic IT partner could be the answer. Unlocking countless opportunities Becoming educated on the topic of cybersecurity and its importance for an organization is the first step Physical security integrators who feel uncomfortable diving headfirst into the digital realm may find that strategically aligning themselves with an IT or cyber firm will unlock countless opportunities. By opening the door to a partnership with an IT-focused firm, integrators receive the benefit of cybersecurity insight on future projects and a new source of RMR through continued consulting with current customers. In exchange, the IT firm gains a new source of clients in an industry otherwise untapped. This is a win for all those involved. While manufacturers, dealers and integrators play a large part in the cybersecurity of physical systems, end users also play a crucial role. Becoming educated on the topic of cybersecurity and its importance for an organization is the first step. Commonplace cybersecurity standards Below is a list of commonplace cybersecurity standards that all organizations should work to implement for the protection of their own video surveillance solutions: Always keep camera firmware up to date for the latest cyber protections. Change default passwords, especially those of admins, to keep the system locked to outside users. Create different user groups with separate rights to ensure all users have only the permissions they need. Set an encryption key for surveillance recordings to safeguard footage against intruders and prevent hackers from accessing a system through a backdoor. Enable notifications, whether for error codes or storage failures, to keep up to date with all systems happenings. Create/configure an OpenVPN connection for secured remote access. Check the web server log on a regular basis to see who is accessing the system. Ensure that web crawling is forbidden to prevent images or data found on your device from being made searchable. Avoid exposing devices to the internet unless strictly necessary to reduce the risk of attacks.
When a child goes missing in a large, crowded mall, we have a panicking mom asking for help from the staff, at least a dozen cameras in the area, and assuming the child has gone missing for only 15 minutes, about 3 hours’ worth of video to look through to find the child. Typical security staff response would be to monitor the video wall while reviewing the footage and making a verbal announcement throughout the mall so the staff can keep an eye out for her. There is no telling how long it will take, while every second feels like hours under pressure. As more time passes, the possible areas where the child can be will widen, it becomes more time-consuming to search manually, and the likelihood of finding the child decreases. What if we can avoid all of that and directly search for that particular girl in less than 1 second? Artificial neural networks are improving every day and now enable us to search for a person across all selected camera streamsWith Artificial intelligence, we can. Artificial neural networks are improving every day and now enable us to search for a person across all selected camera streams in a fraction of a second, using only one photo of that person. The photo does not even have to be a full frontal, passport-type mugshot; it can be a selfie image of the person at a party, as long as the face is there, the AI can find her and match her face with the hundreds or thousands of faces in the locations of interest. The search result is obtained in nearly real time as she passes by a certain camera. Distinguishing Humans From Animals And Statues The AI system continuously analyzes video streams from the surveillance cameras in its network, distinguishes human faces from non-human objects such as statues and animals, and much like a human brain, stores information about those faces in its memory, a mental image of the facial features so to speak. When we, the system user, upload an image of the person of interest to the AI system, the AI detects the face(s) in that image along with their particular features, search its memory for similar faces, and shows us where and when the person has appeared. We are in control of selecting the time period (up to days) and place (cameras) to search, and we can adjust the similarity level, i.e., how much a face matches the uploaded photo, to expand or fine-tune the search result according to our need. Furthermore, because the camera names and time stamps are available, the system can be linked with maps to track and predict the path of the person of interest. AI Face Search is not Face Recognition for two reasons: it protects people’s privacy, and it is lightweight Protecting People’s Privacy With AI Face Search All features of face recognition can be enabled by the system user, such as to notify staff members when a person of interest is approaching the store AI Face Search is not Face Recognition for two reasons: it protects people’s privacy, and it is lightweight. First, with AI Face Search, no names, ID, personal information, or lists of any type are required to be saved in the system. The uploaded image can be erased from the system after use, there is no face database, and all faces in the camera live view can be blurred out post-processing to guarantee GDPR compliance. Second, the lack of a required face database, a live view with frames drawn around the detected faces and constant face matching in the background also significantly reduces the amount of computing resource to process the video stream, hence the lightweight. Face Search Versus Face Recognition AI Face Search Face Recognition Quick search for a particular person in video footage Identify everyone in video footage Match detected face(s) in video stream to target face(s) in an uploaded image Match detected face(s) in video stream to a database Do not store faces and names in a database Must have a database with ID info Automatically protect privacy for GDPR compliance in public places May require additional paperwork to comply with privacy regulations Lightweight solution Complex solution for large-scale deployment Main use: locate persons of interest in a large area Main use: identify a person who passes through a checkpoint Of course, all features of face recognition can be enabled by the system user if necessary, such as to notify staff members when a person of interest is approaching the store, but the flexibility to not have such features and to use the search tool as a simple Google-like device particularly for people and images is the advantage of AI Face Search.Because Face Search is not based on face recognition, no faces and name identifications are stored Advantages Of AI Face Search Artificial Intelligence has advanced so far in the past few years that its facial understanding capability is equivalent to that of a human. The AI will recognise the person of interest whether he has glasses, wears a hat, is drinking water, or is at an angle away from the camera. In summary, the advantages of Face Search: High efficiency: a target person can be located within a few seconds, which enables fast response time. High performance: high accuracy in a large database and stable performance, much like Google search for text-based queries. Easy setup and usage: AI appliance with the built-in face search engine can be customised to integrate to any existing NVR/VMS/camera system or as a standalone unit depending on the customer’s needs. The simple-to-use interface requires minimal training and no special programming skills. High-cost saving: the time saving and ease of use translate to orders of magnitude less manual effort than traditionally required, which means money saving. Scalability: AI can scale much faster and at a wider scope than human effort. AI performance simply relies on computing resource, and each Face Search appliance typically comes with the optimal hardware for any system size depending on the customer need, which can go up to thousands of cameras. Privacy: AI Face Search is not face recognition. For face recognition, there are privacy laws that limits the usage. Because Face Search is not based on face recognition, no faces and name identifications are stored, so Face Search can be used in many public environments to identify faces against past and real-time video recordings. AI Face Search match detected face(s) in video stream to target face(s) in an uploaded image Common Use Cases Of AI Face Search In addition to the scenario of missing child in a shopping mall, other common use cases for the AI Face Search technology include: Retail management: Search, detect and locate VIP guests in hotels, shopping centres, resorts, etc. to promptly attend to their needs, track their behaviour pattern, and predict locations that they tend to visit. Crime suspect: Quickly search for and prove/disprove the presence of suspects (thief, robber, terrorist, etc.) in an incident at certain locations and time. School campus protection: With the recent increase in number of mass shootings in school campuses, there is a need to identify, locate and stop a weapon carrier on campus as soon as possible before he can start shooting. Face Search will enable the authorities to locate the suspect and trace his movements within seconds using multiple camera feeds from different areas on campus. Only one clear image of the suspect’s face is sufficient. In the race of technology development in response to business needs and security concerns, AI Face Search is a simple, lightweight solution for airports, shopping centres, schools, resorts, etc. to increase our efficiency, minimise manual effort in searching for people when incidents occur on site, and actively prevent potential incidents from occurring. By Paul Sun, CEO of IronYun, and Mai Truong, Marketing Manager of IronYun
With increased demands being placed on safety and security globally, and supported by advancements in IP cameras and 360-degree camera technology, the video surveillance industry is growing steadily. Market research indicates that this worldwide industry is expected to reach an estimated $39.3 billion in revenue by 2023, driven by a CAGR of 9.3 percent from 2018 to 2023. Video surveillance is not just about capturing footage (to review an event or incident when it occurs), but also about data analysis delivering actionable insights that can improve operational efficiencies, better understand customer buying behaviors, or simply just provide added value and intelligence. Growth of Ultra-HD Surveillance To ensure that the quality of the data is good enough to extract the details required to drive these insights, surveillance cameras are technologically evolving as well, not only with expanded capabilities surrounding optical zoom and motion range,4K Ultra HD-compliant networked cameras are expected to grow from 0.4 percent shipped in 2017, to 28 percent in 2021 but also relating to improvements in signal-to-noise (S2N) ratios, light sensitivities (and the minimum illumination needed to produce usable images), wide dynamic ranges (WDR) for varying foreground and background illumination requirements, and of course, higher quality resolutions. As such, 4K Ultra HD-compliant networked cameras are expected to grow from 0.4 percent shipped in 2017, to 28 percent in 2021, representing an astonishing 170 percent growth per year, and will require three to six times the storage space of 1080p video dependent on the compression technology used. Surveillance cameras are typically connected to a networked video recorder (NVR) that acts as a gateway or local server, collecting data from the cameras and running video management software (VMS), as well as analytics. Capturing this data is dependent on the communications path between individual cameras and the NVR. If this connection is lost, whether intentional, unintentional, or a simple malfunction, surveillance video will no longer be captured and the system will cease operations. Therefore, it has become common to use microSD cards in surveillance cameras as a failsafe mechanism. Despite lost connectivity to the NVR, the camera can still record and capture raw footage locally until the network is restored, which in itself, could take a long time depending on maintenance staff or equipment availability, weather conditions, or other unplanned issues. Since microSD cards play a critical role as a failsafe mechanism to ensure service availability, it is important to choose the right card for capturing video footage. It has become common to use microSD cards in surveillance cameras as a failsafe mechanism if an NVR breaks Key Characteristics Of microSDs There are many different microSD cards to choose from for video capture at the network’s edge, and they range from industrial grade capabilities to commercial or retail grade, and everything in-between. To help make some of these uncertainties a little more certain, here are the key microSD card characteristics for video camera capture. Designed For Surveillance As the market enjoys steady growth, storage vendors want to participate and have done so with a number of repurposed, repackaged, remarketed microSD cards targeted for video surveillance but with not much robustness, performance or capabilities specific to the application. Adding the absence of mean-time between failure (MTBF) specifications to the equation, microSD card reliability is typically a perceived measurement -- measured in hours of operation and relatively vague and hidden under metrics associated with the camera’s resolution and compression ratio. Therefore, when selecting a microSD card for surveillance cams at the edge, the choice should include a vendor that is trusted, has experience and a proven storage portfolio in video surveillance, and in microSD card technologies. Endurance, as it relates to microSD cards, represents the number of rewrites possible before the card can no longer store data correctly High Endurance Endurance, as it relates to microSD cards, represents the number of rewrites (program/erase cycles) that are possible before the card can no longer store data correctly. The rewrite operation is cyclical whereby a new stream of footage replaces older content by writing over it until the card is full, and the cycle repeats. The higher the endurance, the longer the card will perform before it needs to be replaced. Endurance is also referred to in terabytes written (TBW) or by the number of hours that the card can record continuously (while overwriting data) before a failure will occur. Health Monitoring Health monitoring is a desired capability that not many microSD cards currently support and enables the host system to check when the endurance levels of a card are low and needs to be replaced. Having a card that supports this capability enables system integrators and operators with the ability to perform preemptive maintenance that will help to reduce system failures, as well as associated maintenance costs. Performance To capture continuous streams of raw footage, microSD cards within surveillance cams perform write operations about seventy to ninety percent of the time, whereas reading captured footage is performed about ten to thirty percent. The difference in read/write performance is dependent on whether the card is used in an artificial intelligent (AI) capable camera, or a standard one. microSD cards deployed within surveillance cameras should support temperature ranges from -25 degrees Celsius to 85 degrees Celsius Finding a card that is write-friendly, and can provide enough bandwidth to properly capture streamed data, and is cost-effective, requires one that falls between fast industrial card capabilities and slower commercial ones. Bandwidth in the range of 50 MB/sec for writes and 80 MB/sec for reads are typical and sufficient for microSD cards deployed within surveillance cameras. Temperature Ranges Lower capacity support of 32GB can provide room to attract the smaller or entry-level video surveillance deployments As microSD cards must be designed for continuous operation in extreme weather conditions and a variety of climates, whether located indoors or out, support for various temperature ranges are another consideration. Given the wide spectrum of temperatures required by the camera makers, microSD cards deployed within surveillance cameras should support temperature ranges from -25 degrees Celsius to 85 degrees Celsius, or in extreme cases, as low as -40 degrees Celsius. Capacity Selecting the right-sized capacity is also very important as there needs to be a minimum level to ensure that there is enough room to hold footage for a number of days or weeks before it is overwritten or the connectivity to the NVR is restored. Though 64GB is considered the capacity sweet spot for microSD cards deployed within surveillance cameras today, lower capacity support of 32GB can provide room to attract the smaller or entry-level video surveillance deployments. In the future, even higher capacities will be important for specific use cases and will potentially become standard capacities as the market evolves. When choosing the right storage microSD card to implement into your video surveillance system, make sure the card is designed specifically for the application – does it include the right levels of endurance and performance to capture continuous streams – can it withstand environmental challenges and wide temperature extremes – will it enable preventative and preemptive maintenance to provide years of service? It is critical for the surveillance system to be able to collect video footage whether the camera is connected to an NVR or is a standalone camera as collecting footage at the base of the surveillance system is the most crucial point of failure. As such, failsafe mechanisms are required to keep the camera recording until the network is restored.
Motorola Solutions, a global pioneer in responsible video analytics, announced the newest addition to its video security and analytics portfolio, Avigilon Control Center (ACC) 7.6. ACC™ 7.6 is the company’s most recent video management software release, enabling enterprise customers to better monitor their facilities through new AI-powered facial recognition features and analytics. The introduction of this technology is a part of Motorola Solutions’ ongoing commitment to creating state-of-the-art solutions that address enterprise safety needs while safeguarding individual privacy rights and enforcing the responsible use of analytics and data management. Facial recognition capabilities In keeping with this commitment, the new video management capabilities in ACC 7.6 include robust controls to govern the retention and accessibility of watch list data, the ability to create and manage multiple watch lists from within ACC and the option to upload reference images to a watch list. These features are built into the solution natively, meaning that the technology is developed and owned by Motorola Solutions as opposed to a third-party vendor. In addition, the new facial recognition capabilities do not make consequential decisions or initiate actions on their own. The data that are entered and saved in the watch lists are managed and maintained by the enterprise customers employing the technology. Vigilance across school campuses “When leveraged responsibly, facial recognition is an incredibly powerful tool that can assist in numerous ways, including helping enterprise organizations make better informed decisions that empower them to respond to events proactively,” said John Kedzierski, senior vice president, Video Security Solutions, Motorola Solutions. “For our enterprise customers, we believe potential facial recognition matches should always be assessed and verified by a trained specialist who can then determine whether further investigation or action is necessary.” ACC 7.6 features Unusual Activity Detection analytics that provide site-wide intelligence For example, with the heightened vigilance across school campuses, district officials are able to employ the technology to ensure that anyone deemed a threat to the students and staff on campus has their photo added to the system. When there is a face match made against the watch list, the school’s security operators receive a notification so that they can take proactive measures to assess the situation and ensure the safety of students and staff. Unusual Activity Detection analytics In addition to new watch list and facial recognition capabilities, ACC 7.6 features Unusual Activity Detection analytics that provide site-wide intelligence with the ability to recognize out-of-ordinary activity such as unusual speed of movement or location of people or vehicles. This edge-based intelligence distinguishes between typical and abnormal events by continuously learning what usual activity might be for a scene over time. For example, if there is an individual running through a crowded mall, a security guard could be alerted in Focus of Attention to locate where the individual is and take measures to understand the situation. ACC 7.6 is ONVIF Profile G compliant and will be available for preview at Motorola Solutions’ first-ever virtual showcase taking place March 18-20, 2020.
Motorola Solutions announced the latest addition to its video security and analytics portfolio, the integration of its VideoTag enterprise body-worn cameras and Avigilon Control Center (ACC) video management software. The integration enhances enterprise security efforts by making live body-worn video and audio easily accessible through the ACC™ AI-powered platform. access control systems “The introduction of this integration is a part of our ongoing efforts to introduce intelligent end-to-end solutions that serve the specific security and business needs of our customers”, said John Kedzierski, senior vice president, Video Security Solutions. "By adding a mobile video capability to their access control systems, our customers are now able to better protect their employees and customers while improving on their business outcomes". The VideoTag series of body-worn cameras are lightweight, unobtrusive cameras and easily assigned to staff and attached to uniforms like ID badges. When integrated with ACC, the activation of a body-worn camera recording immediately triggers an event in the software, displaying the camera’s live video feed to the system operator alongside video feeds from fixed cameras, allowing security personnel to gain better situational awareness and respond accordingly. Increase staff protection Enterprises such as healthcare, hospitality and retail rely on body-worn cameras to increase staff protection, improve customer relations and help to prevent incidents. For example, caregivers helping patients who may behave erratically or violently are risking their own safety and security. By wearing the mobile camera, staff is able to capture events in real-time and alert security operators so that they are made aware of any incident that might compromise a staff member’s safety, and are able to respond proactively. This product will be available for preview at Motorola Solutions’ first-ever virtual showcase taking place March 18-20, 2020.
Eagle Eye Networks, an international provider of cloud video surveillance, announced the release of its cloud-client fisheye camera dewarping solution. Eagle Eye Cloud VMS customers can now securely access their fisheye cameras and dewarp the video in real-time from their desktop browser or cellphone device. Eagle Eye Cloud VMS supports a broad array of fisheye cameras from multiple manufacturers, models include: Axis M3058 Hanwha 9010R Avigilon 12.0-H4F-DO1-IR Hikvision DS-2CD63C5G0-I(V)(S) Dahua NK8BR4 12MP Eagle Eye Networks CDUF-003a Standard fisheye cameras Eagle Eye Networks expects to quickly expand the support for additional fisheye cameras from additional manufacturers. Eagle Eye Cloud VMS currently supports over 3,500 IP, analog and HD Over Coax cameras. “Fisheye cameras have numerous efficiencies and cost savings associated with them,” said Dean Drako, CEO of Eagle Eye Networks. “Eagle Eye’s fisheye dewarping solution transforms standard fisheye cameras into powerful intuitive tools while decreasing the cost and maintenance needed to cover a wide area.” Included with the new fisheye dewarping is a virtual PTZ function that provides benefits over traditional PTZ cameras. Traditional PTZ cameras can only capture the area they are aimed at when they are aimed at it. The virtual PTZ function in Eagle Eye Networks’ fisheye dewarping captures footage of an entire area regardless of any pan, tilt or zoom. Camera-based dewarping The user can, therefore, pan, tilt and zoom after the fact and see the image when viewing historic video. Previously, the Eagle Eye Cloud VMS supported fisheye cameras through camera-based dewarping. The new cloud-client dewarping no longer requires the camera to do the dewarping function. Dewarping is done in real-time or when viewing historic video. Customers can apply preset dewarps to their video in Single, Dual 180, Quad and 360 views. These views can be converted to layouts as if they were separate cameras. The Eagle Eye Cloud VMS cloud-client fisheye camera dewarping is available immediately and globally to all partners and customers. There is no additional charge for these features.
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