The Evolution of Video Analytics
Video surveillance has been around for a very long time — probably much longer than most people realize. Closed circuit television (CCTV) monitoring has been used since the mid-1960s. A major hallmark came in 1969 when New York City installed cameras on its Municipal Building. As CCTV was more widely embraced, it probably didn’t take long for users to realize that monitoring the CCTV feeds was no easy task.
Research shows that as the number of cameras an operator is tasked with monitoring increases, their ability to do so effectively decreases. So while CCTV could provide comprehensive views of an area’s activity, this information wasn’t being leveraged to the fullest — not even close. Eventually a solution to this problem emerged, in the form of video content analysis (VCA), now more commonly referred to as video analytics.
Using a combination of algorithms, video analytics analyzes captured video in real time and presents alerts about whatever the application is programmed to identify. In early versions, this was primarily motion. But as you can imagine, things move all of the time, especially outdoors. As a result, the first uses of video analytics resulted in a high number of false alarms. While these systems did manage to identify suspicious movement, they would also notify operators of unintended situations, such as when the wind picked up, or when a vehicle’s headlights blurred the scene, or when it started to rain. Unfortunately, too many false alarms eventually caused security operators to ignore alerts, which defeated the purpose of having video analytics in the first place.
Fortunately, video analytics technology has come a long way. Over the past 10 years, it has evolved at a rate similar to other technologies, and is now in its fourth generation. Today’s video analytics applications are able to do much more than just identify motion, and false alarms have been reduced to negligible rates. For example, video analytics applications are now able to automatically filter out motion caused by wind, snow, rain and change of lighting. Some applications now also have the ability to detect tampering, and can automatically adjust the visual parameters of enabled video cameras according to individual scene characteristics to ensure optimal brightness and contrast for video viewing and recording.
The more recent evolutions in video analytics have made them exponentially more effective. Object classification is one such improvement. Most basic video analytics simply detect moving objects but don’t distinguish their nature. Using object classification, newer video analytics software is able to differentiate between different types of moving things, such as a human or a car. It can also filter out certain mobile elements, such as moving vegetation, a shaking fence, shadows, and car lights, so they will not set off an alarm.
With the ability to classify objects, video analytics are able to better identify true potential threats, and of course rule out false alarms.
P/T/Z Automatic Tracking
Pan/tilt/zoom (p/t/z) cameras have the ability to follow an object as it moves around a perimeter and zoom in on a particular scene for closer and clearer images. In most cases security operators control p/t/z cameras manually. Of course this means that operators need to first identify the potential threat and then move the camera to follow the perceived threat. This has several drawbacks, one being that it’s easy to lose track of a suspicious object when it gets out of the line of sight of a camera. The other drawback is that if an operator is manually guiding the camera, he can’t do much of anything else, like call for a response team.
P/T/Z automatic tracking works by having fixed cameras (or other object detection sensors) identify a suspicious object and its location using advanced video analytics applications. The fixed camera then passes this information over to the p/t/z camera, which is able to track the object while automatically using its pan and tilt capabilities. Of no less importance is the p/t/z camera’s ability to zoom in on the tracked object to capture a more detailed image. This makes the p/t/z automatic tracking not only extremely helpful during real-time events, but also for identification purposes and after-the-fact investigation.
As we know from living in a digital world, how information is displayed is just as important as what that information is, particularly so when you’re monitoring large amounts of sites, with many information streams. If this information is not displayed intuitively, something important can be easily missed. That’s where video surveillance dashboards can help.
Dashboards are particularly effective when you’re monitoring many sites from a central control room. Just imagine a railway company with dozens of train stations or a large retail bank with hundreds or thousands of branches, all with video surveillance cameras that need to be monitored. The dashboard gives operators an at-a-glance enterprise-wide view of video system status and maintenance issues across every remote location, from one screen, so they can easily identify security, safety and maintenance problems, and respond according to their severity. Operators can also view and retrieve video of any location instantly simply by clicking on the designated location icon.
The Evolving Role of Video Surveillance and Video Analytics
Although video surveillance and video analytics have their roots in security, along the way, we’ve discovered many uses outside of the security realm, and more are emerging today.
For example, video analytics applications are now being used for operational and commercial purposes such as crowd control and the prevention of long lines. Of course overcrowding can be a security and safety issue, but it’s also an inconvenience that can annoy the traveling public.
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