Video camera surveillance systems have become the commonplace security solution used among municipal governments, law enforcement agencies, retail establishments, manufacturing facilities, banks, utility companies, and more to archive video footage and to provide 24×7, live monitoring of buildings, roadways, and other physical assets. But even though this kind of surveillance coverage is critical for safety and security, vast video footage, insufficient resources for monitoring and processing, and inevitable human error causes critical intelligence to constantly fall through the cracks. Enter video analytics: the key to making sure your video surveillance solutions can make the greatest impact by placing all your critical intelligence in your hands.
Today’s comprehensive video content analytics technology is backed by Artificial Intelligence, which is powered by Deep Learning, a 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. This process enhances the utility and value of video surveillance by making it easy to search and filter for actionable information. In general, the technology is used to:
1. Conduct post-incident investigations
2. Generate quantitative meta data reports about video activity
3. Enhance security situational awareness in real-time.
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, high rate of false alerting rendered these solutions inadequate for combatting the challenges of manual video monitoring. Furthermore, in situations where accuracy is crucial, imprecise or missed detections didn’t completely achieve the expected result of reducing human involvement. At first, video analytics solutions drove efficient post event investigation, but – fortunately – video content analysis has evolved tremendously, resulting in highly effective, real-time alerting capabilities that support proactive safety and security measures. While the human operator isn’t entirely removed from the process, current systems have much higher detection accuracy and more sophisticated capabilities than their forerunners and enable users to rapidly respond to dynamic conditions based on full situational awareness.
Read on to discover how real-time alerting empowers operators to respond dynamically and proactively to developing situations.
Video analytics technology improves situational awareness via customized, real-time alerts that are triggered when irregular activity is detected. When security teams know what normal or routine activity to expect, they can configure customized alerts based on specific, pre-defined criteria to notify them of irregular activity. Some examples of alerting capabilities include:
Appearance Similarity Alerts – Video surveillance operators can configure an alert based on object appearance similarity criteria like clothing and vehicle type. For example, an alert could be triggered when bikes are in an area meant for cars only, or when objects are dwelling in an area that should be clear of both people and vehicles. Video analytics can also accurately alert based on clothing, and sometimes, the technology can be further customized to recognize specific employee uniforms. By filtering between uniformed and non-uniformed individuals, managers can be alerted to unauthorized access of restricted areas with greater accuracy.
Count-Based Alerts – Alerts can be triggered when a certain number of objects (vehicles or people) are detected in a pre-defined area within a specified period of time. This can be useful in a variety of settings where traffic or crowd control is important. For instance, where a customer service department needs to address long queues of customers like at airport security, a large stadium entrance, a retail checkout counter, or a bank lobby.
Dwell Alerts – Dwelling or loitering can indicate intent to commit a crime, especially when people are lingering in 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 of environmental changes so they can respond quickly.
Face Recognition Alerts – Where facial recognition technology is allowed, security teams can use it to pinpoint suspects based on digital images extracted from video or externally imported and send out real-time alerts to proactively manage a situation. 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. This alert could also deploy security staff to the area to deter future attempts.
Real-time alerts help both security and operations staff 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.
Editor’s note: This post was originally published in October 2019, and has been refreshed and updated for accuracy.
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