Artificial Intelligence (AI) Trends In Physical Security Systems

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If you’ve been paying attention over the last twelve months, you will have noticed that deep learning techniques and artificial intelligence (AI) are making waves in the physical security market, with manufacturers eagerly adopting these buzzwords at the industry's biggest trade shows. With all the hype, security professionals are curious to know what these terms really mean, and how these technologies can boost real-world security system performance.

The growing number of applications of deep learning technology and AI in physical security is a clear indication that these are more than a passing fad. This review of some of our most comprehensive articles on these topics shows that AI is an all-pervasive trend that the physical security industry will do well to embrace quickly. Here, we examine the opportunities that artificial intelligence presents for smart security applications, and look back at how some of the leading security companies are adapting to respond to rapidly-changing expectations:

What Is Deep Learning Technology?

Machine Learning involves collecting large amounts of data related to a problem, training a model using this data and employing this model to process new data. Recently, there have been huge advances in a branch of Machine Learning called Deep Learning. This describes a family of algorithms based on neural networks. These algorithms are able to learn efficiently from example, and subsequently apply this learning to new data. Here, Zvika Ashani explains how deep learning technology can boost video surveillance systems.

Relationship Between Deep Learning And Artificial Intelligence

There have been huge advances in a branch of Machine Learning called Deep Learning

With deep learning, you can show a computer many different images and it will "learn" to distinguish the differences. This is the "training" phase. After the neural network learns about the data, it can then use "inference" to interpret new data based on what it has learned. For example, if it has seen enough cats before, the system will know when a new image is a cat. In effect, the system “learns” by looking at lots of data to achieve artificial intelligence (AI). Larry Anderson explores how new computer hardware - the Graphic Processing Unit (GPU) – is making artificial intelligence accessible to the security industry.

Improving Surveillance Efficiency And Accuracy With AI

The Graphic Processing Unit (GPU) – is making artificial intelligence accessible to the security industry

Larry Anderson explains how the latest technologies from Neurala and Motorola will enable the addition of AI to existing products, changing an existing solution from a passive sensor to a device that is “active in its thinking.” The technology is already being added to existing Motorola body-worn-cameras to enable police officers to more efficiently search for objects or persons of interest. In surveillance applications, AI could eliminate the need for humans to do repetitive or boring work, such as look at hours of video footage.

Intelligent Security Systems Overcome Smart City Surveillance Challenges

In surveillance applications, AI could eliminate the need for humans to do repetitive or boring work, such as look at hours of video footage

AI technology is expected to answer the pressing industry questions of how to use Big Data effectively and make a return on the investment in expensive storage, while maintaining (or even lowering) human capital costs. However, until recently, these expectations have been limited by factors such as a limited ability to learn, and high ongoing costs. Zvika Ashani examines how these challenges are being met and overcome, making artificial intelligence the standard in Smart City surveillance deployments.

Combining AI And Robotics To Enhance Security Operations

Artificial intelligence is becoming the standard in Smart City surveillance deployments

With the abilities afforded by AI, robots can navigate any designated area autonomously to keep an eye out for suspicious behavior or alert first responders to those who may need aid. This also means that fewer law enforcement and/or security personnel will have be pulled from surrounding areas. While drones still require a human operator to chart their flight paths, the evolution of artificial intelligence (AI) is increasing the capabilities of these machines to work autonomously, says Steve Reinharz.

Future Of Artificial Intelligence In The Security Industry

While drones still require a human operator to chart their flight paths, the evolution of artificial intelligence (AI) is increasing the capabilities of these machines to work autonomously

Contributors to SourceSecurity.com have been eager to embrace artificial intelligence and its ability to make video analytics more accurate and effective. Manufacturers predicted that deep learning technology could provide unprecedented insight into human behaviour, allowing video systems to more accurately monitor and predict crime. They also noted how cloud-based systems hold an advantage for deep learning video analytics. All in all, manufacturers are hoping that AI will provide scalable solutions across a range of vertical markets.

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People mentioned in this article

Larry Anderson

Zvika Ashani Agent Vi (Agent Video Intelligence)

Steve Reinharz Robotic Assistance Devices, LLC.

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Security Device Manufacturers Develop Interfaces To Be Compatible With Gunshot Detection Technologies

Gunshot detection today is part of more physical security systems than ever before, and many manufacturers are developing interfaces to the latest gunshot detection technologies. Genetec has integrated ShotSpotter gunshot detection technology into its unified IP security platform, Security Center. Thanks to this integration, police departments and security professionals will be able to receive more actionable information, gain rapid access and detailed location insights when a gunshot situation occurs. Cloud-Based analysis software ShotSpotter uses wide-area acoustic surveillance that automatically gets activated when gunfire occurs ShotSpotter uses wide-area acoustic surveillance that automatically gets activated when gunfire occurs, providing comprehensive outdoor coverage on campuses and in complex urban geographies. ShotSpotter sensors and cloud-based analysis software triangulate and pinpoint the precise location of each round fired within seconds, the number of shots fired and provide an immediate gunfire alert with an audio clip. Data capture form to appear here! Detailed incident data is immediately sent to SST’s Incident Review Center (IRC), a secure data processing and alert validation facility, where acoustics experts analyze the data and qualify the incident in real-time 24/7. Once validated, an alert is sent directly into Genetec’s Security Center, all in under a minute. Once the alarm is triggered within Security Center upon gunshot detection, operators can quickly access video and other related data surrounding the location of a gunshot alert, and immediately visualize it on Security Center maps, leveraging the dynamic visualization capabilities of the Security Center Plan Manager. Genetec has integrated ShotSpotter gunshot detection technology into its unified IP security platform Gunshot identification software Stanley Security has announced a partnership with Shooter Detection Systems LLC (SDS), gunshot detection solutions provider. As an authorized dealer, STANLEY Security is now certified to sell, install and service SDS products and services. SDS’s Guardian Indoor Active Shooter Detection System combines acoustic gunshot identification software with infrared gunfire flash detection for a fully automated gunshot detection and alerting solution. The Guardian System immediately detects gunshots and simultaneously alerts building occupants The Guardian System immediately detects gunshots and simultaneously alerts building occupants and first responders within one second and with zero false alerts. Johnson Controls has announced that its exacqVision video management system now integrates with Databuoy Corp.’s ShotPoint acoustic gunshot detection and localization system to speed response time and heighten the decision-making process for local responders during active shooter situations. Integrated security systems Johnson Controls has also released the Detect360 Active Shooter Response (ASR) system, combining reliable gunshot detection with notification technology to provide immediate warning when a gunshot is fired within a building. By providing early notification and precise location of the shooter, the ASR system gives occupants time to find safety and allows police and security personnel to mitigate the threat up to 60 percent faster. Detect360 ASR gives first responders information as it develops at the scene, including shot location, number of shots, location of the shooter in near real-time and the ability to remotely monitor the situation through integrated security systems. It also collects forensic information that can later be used by law enforcement. The system supports multiple integration options, including video feeds from existing CCTV and intrusion systems, mass notification systems, access control systems and panic button/manual initiation systems. Detect360 ASR gives first responders information as it develops at the scene Public safety access points Listed as one of the most important new technologies debuted at CES 2019 by the Wall Street Journal and NBC News, Safe Zone also showcased its Safe Zone Gunfire Detector (GFD) at ISC West 2019. Priced at $149 per unit, the Safe Zone Gunfire Detector combines infrared and sound detection technologies with immediate cloud-based data analysis that can analyze more than 3,000 data points of each loud noise that exceeds ambient levels by a certain threshold. The system determines the location of shots fired, the number of shots, and the type Utilizing the data from multiple detectors simultaneously, the system determines the location of shots fired, the number of shots, and the type and caliber of gun being used. Within 10 seconds of the trigger pull, an alert is sent to Public Safety Access Points in the appropriate dispatch center, giving local 911 dispatchers all critical information on their computers. Reseller recruitment program Shot Tracer has launched a reseller recruitment program with a highly attractive on-boarding proposition for qualified dealers. Qualifying resellers can receive a Gunshot Simulator Kit valued at $1,000 when they commit to becoming a Shot Tracer dealer. Shot Tracer’s Eagle Gunshot Detection System easily integrates with virtually any security, alarm, surveillance and access system via contact closure – or wirelessly via IP integration. In fact, Shot Tracer gunshot detection sensors are as easy to install as a smoke detector, so there’s virtually no learning curve for installing dealers. The new Shot Tracer Reseller Recruitment Program includes an exclusive offer for installing dealers to receive a free Gunshot Simulator Kit, valued at over $1000, which is essential for demonstrating Shot Tracer Eagle Gunshot Detection sensors. Terms and conditions apply for qualifying resellers.

viisights’ Next Generation Of Video Analytics Offer ‘Behavioral Understanding’

A video analytics system that provides ‘behavioral understanding’ can yield more meaningful and actionable data for a range of applications. In public safety and security, such a system can alert on violent or suspicious behaviors, such as people fighting, vandalism, people with weapons, etc. In advanced traffic surveillance and monitoring, it can provide alerts to vehicle collisions (accidents), traffic hazards or vehicle that aren’t using the road properly, such as a car that stops in the middle of the junction. For enterprise and campus security, it can provide advanced anti-tailgating and detect unauthorized activity. Video surveillance infrastructure viisights was founded by a group of entrepreneurs with track records in developing technology businesses These uses are among the benefits of viisights’ video analytics technology based on behavioral understanding of video content. “It means we can extract more meaningful data from the huge amount of video content that is captured, and we can transform that data to actionable insights that eventually justify the massive investment in video surveillance infrastructure,” says Asaf Birenzvieg, CEO of viisights. Their behavioral understanding systems for real-time video intelligence leverage artificial intelligence technology. viisights was founded by a group of serial entrepreneurs with track records in developing technology businesses. The Israeli company’s founders recognized a growing global need for intelligence to make physical and virtual public areas safer – and realized the role that smart video understanding technology can play. Developing artificial intelligence technologies viisights is committed to developing artificial intelligence technologies that facilitate human-like video understanding, which in turn serves as the basis for fully autonomous video intelligence systems powered by pattern prediction technology. “Behavioral recognition is the future of video analytics and the next generation of the object classification analytics systems that hold the majority of the market today,” says Birenzvieg. viisights has developed a video understanding technology for real-time video processing “To date most video analytics systems still base their product features on static analysis of objects from images using image recognition, even the ones that use ‘AI analytics.’ Products built using such object classification technology are extremely limited.” For example, object classification analytics cannot recognize behavioral events in a video such as people fighting or a car collision because such behaviors can’t accurately be concluded in large scale from analyzing a single static image/frame. Video understanding technology viisights has developed a video understanding technology for real-time video processing. The technology can process live video feeds. In addition to recognizing a particular object (e.g., person) and its attributes (e.g., red shirt), the system can understand an object’s actions, interactions with other objects (events), the scene being viewed (i.e., crowd is gathering, riots) and the context (a car is driving on the road or on the sidewalk). The main verticals are smart cities, enterprises and campuses, banks and ATM security“Basically, we are able to extract more meaningful data from a live video feed and therefore create actionable insights and greater ROI,” says Birenzvieg. The company focuses mostly on security and safety use-cases. The main verticals are smart cities, enterprises and campuses, banks and ATM security, security guard companies and transportation hubs. The company is working on a new product for in-vehicle monitoring mostly for security, safety, vehicle protection and proper vehicle use; it monitors passengers’ behavior inside a bus, train, or taxi. The product will come to market next year. Video management system viisights’ video analytics offering is currently optimized for server-side deployment, and the integration architecture is similar to most video analytics systems. From one side it is integrated with the video management system (VMS). They are a Milestone verified partner and soon will be part of Milestone's marketplace. From the other end, it is connected to a command-and-control system for processing the data and presenting the alerts to the end-user. The analytics company makes most sales through system integrators. They have partnerships with big system integrators like Motorola Solutions and NEC and are also working with smaller ones. They are looking to expand their system integrator network, mostly in the USA and Europe. Behaviors can have many variations and they can be very diverse Cloud video surveillance “We will continue to invest in performance and accuracy, meaning higher recall and lower false positive rate,” says Birenzvieg. “Since our major value proposition is in behavior recognition, behavior events many times are not clearly defined, which is very different from object classification. Behaviors can have many variations and they can be very diverse.” An example is a simple behavior like a person falling on the floor. A person can fall on the floor in many ways, but the challenge is to ignore similar behaviors that are not a person falling and that confuse the system, such as a person bending over to tie his shoelaces. With cloud video surveillance becoming a trend, viisights is also looking into offering some of their advanced functionalities in a video-analytics-as-a-service-model.