What is Video Content Analytics Technology?

Video camera surveillance systems are commonly used among municipal governments, law enforcement agencies, retail establishments, manufacturing facilities, banks, and utility companies. These surveillance systems have become a de facto standard to provide 24×7 live monitoring of buildings, roadways, and other physical assets, as well as an archive of video footage. However, it’s virtually impossible for security staff to monitor each camera constantly, which means that staff don’t truly have comprehensive situational awareness. Furthermore, video cameras generate overwhelming quantities of footage, so security teams often don’t have the time to manually review the archived footage if they need to conduct a post-incident investigation. Even if they can dedicate the time and resources to review all the footage, their observations are prone to human error.

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To solve these challenges, video content analytics software (VCA) has emerged to enhance the utility and value of video surveillance: Video intelligence technology processes video, identifies objects in the video footage (people, vehicles, and other items), and indexes them so that footage can be easily and quickly searched and analyzed. Video content analysis technology is essential to quickly search and filter video footage for actionable information. In general, the technology is used in three different ways: 1) to conduct post-incident investigations to find persons of interest; 2) to generate quantitative meta data reports about video activity (for example, traffic analysis, from quantifying pedestrian, bicycle or vehicle to visualizing traffic density in heatmaps. The same traffic BI can be applied to analyze building usage, as well) and 3) to enhance security situational awareness in real-time. This blog post will focus on advanced video analytics’ real-time capabilities and how they empower operators to respond dynamically and proactively to developing situations.

Triggering Real-Time Alerts

Video analysis technology improves situational awareness via customized real-time alerts that are triggered when irregular activity is detected that may require a response. When security teams know what normal, routine activity to expect, they can configure customized alerts based on specific pre-defined criteria to notify them of irregular activity. Some examples include:

• Appearance similarity alerting – Video surveillance operators can configure an alert based on object appearance similarity criteria. For example, an alert could be triggered when there are bikes in a specific area where bikers aren’t meant to ride, or when objects are dwelling in an area where there should not be people or vehicles. It can even detect people based on the clothes they are wearing, such as triggering alerts to track clothing colors of an employee uniform – so operations managers can keep tabs on employees – employee uniforms or detecting suspects based on their clothing as described by witnesses.
• Count-based alerting – Alerts can be triggered when a certain number of objects (vehicles or people) are detected in a pre-defined area within in a specified time period. This can be useful in a variety of settings where traffic or crowd control is important, or where a customer service department needs to address long queues of customers who are waiting for service, such as an airport security check area, a large stadium entrance, a retail checkout counter or a bank lobby. (There are several more uses for real-time alerts in retail operations.)
• Dwell alerts – Dwelling or loitering can indicate intent to commit a crime, especially when people are lingering in areas such as entryways or facilities that store valuable inventory. When more people than expected are dwelling in a certain area, a video-based alert can make the security staff aware so they can respond quickly.
• Face recognition alerting– Where facial recognition technology is allowed, security agencies can use it to easily pinpoint suspects and send out alerts in real-time, based on digital images extracted from video or externally imported. For instance, security staff can extract images of suspects from different shoplifting incidents and assemble a suspect watchlist based on video surveillance footage. When a suspect from a watchlist is detected entering the shopping center, security can be on high alert and track the suspected shoplifter, intervening if a repeat offense is surveilled or deploying security staff to deter future attempts.

Technology Backed by Artificial Intelligence

Early video analysis products tried to reduce the need for real-time human monitoring of video activity by triggering alerts for unusual or suspicious motion activity.  However, these products did not achieve the level of accuracy the market needed. They could not discern certain objects or behaviors, and they tended to trigger false positives. Thus, they still required a lot of human interaction. Fortunately, video content analysis has evolved tremendously in the past decade, resulting in highly effective alerting capabilities as well as sophisticated reporting. While the human operator isn’t entirely removed from the process, the systems today have much higher detection accuracy and more sophisticated capabilities than their forerunners, that enable users to rapidly respond to dynamic conditions based on full situational awareness. Today’s comprehensive video content analytics technology is backed by Artificial Intelligence, which is powered by Deep Learning, a new model in which neural networks are trained to recognize patterns from massive amounts of data. As the video analytics software processes raw video, it simultaneously detects, tracks, extracts, and classifies every object that appears and creates a structured database of information out of the unstructured video data, enabling smart alerting, as well as granular search and comprehensive reporting.

Benefits of Real-Time Alerting

Real-time alerts help both security and operations staff to increase their situational awareness, detect unusual and excessive loitering, monitor crowds or queues, and accelerate responses to emergencies, threats or suspicious behavior. In response, they can send staff as needed to improve public safety or the customer experience. For these reasons, video content analytics technology is quickly becoming an essential complement to video surveillance camera networks, enabling organizations to maximize the value of their existing infrastructure.