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.
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:
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.
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.
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