The Evolution of Video Analytic
In the moves, the bad guys are somehow always able to slip past the guards watching hundreds of security cameras. And no wonder. Who could watch video feeds of hundreds of empty hallways for hours on end without getting bored and distracted? That’s why the security community has put such an emphasis on video analytics, letting computers monitor the video feeds and send an alert if they see anything worth investigating.
But video analytics hasn’t always been up to the task. First generation video analytics was limited to watching for changes in individual pixels, or perhaps a significant portion of the pixels. If a camera was outdoors, trees waving in the wind or even clouds moving in front of the sun might trigger a false alarm. Most of the research and development in video analytics has therefore gone into reducing the number of false alarms. One way they’ve done this is to program the analytics to view the changes as objects rather than pixels: That’s just a rabbit, that’s rain, that’s snow, that’s a spider making a web on the front of the camera. The technology is continuously improving the accuracy of categorizing what the camera is observing according to size, shape, aspect ratio, speed, motion behavior, and color among other parameters.
That first generation of video analytics has evolved in two ways: In-camera or edge analytics, where the specialized camera has the intelligence to interpret what it sees, are getting much more sophisticated at a very low price point, sometimes to the extent of being bundled with the camera itself. In contrast, Server-based analytics integrate information from a large number of cameras – no matter the brand or model - to give security professionals a greater situational understanding, and they do so by leveraging the latest in IT-class processors that continue to rapidly evolve.
While first generation video analytics focused on real-time alerts and minimizing false alarms, second generation technology focused on providing forensic tools to search the video post-event. If there had been a robbery and someone saw the suspects escape in a white van, you might search a camera for all white vans that drove past your location the day prior. Or if the situation involved an assault by a person wearing red, you might search a camera for the past three hours for people wearing red. The second generation of video analytics helped to reconstruct what happened; however, it was designed more as a forensic tool rather than a tool that exists to tell you where the van or person is right now, just moments after you heard about them. That sense of immediacy is what makes all the difference in response and mitigation.
The third generation of video analytics is purpose-built to offer real-time insight, based on searching for someone or something across multiple cameras, during a time frame typically leading up to the current time. It answers the simple question ‘Where is he now?’ and has to be fast, accurate, and easy to use in a high stress real-time situation.
Security Camera Installation, Security Camera in LA.
But video analytics hasn’t always been up to the task. First generation video analytics was limited to watching for changes in individual pixels, or perhaps a significant portion of the pixels. If a camera was outdoors, trees waving in the wind or even clouds moving in front of the sun might trigger a false alarm. Most of the research and development in video analytics has therefore gone into reducing the number of false alarms. One way they’ve done this is to program the analytics to view the changes as objects rather than pixels: That’s just a rabbit, that’s rain, that’s snow, that’s a spider making a web on the front of the camera. The technology is continuously improving the accuracy of categorizing what the camera is observing according to size, shape, aspect ratio, speed, motion behavior, and color among other parameters.
That first generation of video analytics has evolved in two ways: In-camera or edge analytics, where the specialized camera has the intelligence to interpret what it sees, are getting much more sophisticated at a very low price point, sometimes to the extent of being bundled with the camera itself. In contrast, Server-based analytics integrate information from a large number of cameras – no matter the brand or model - to give security professionals a greater situational understanding, and they do so by leveraging the latest in IT-class processors that continue to rapidly evolve.
While first generation video analytics focused on real-time alerts and minimizing false alarms, second generation technology focused on providing forensic tools to search the video post-event. If there had been a robbery and someone saw the suspects escape in a white van, you might search a camera for all white vans that drove past your location the day prior. Or if the situation involved an assault by a person wearing red, you might search a camera for the past three hours for people wearing red. The second generation of video analytics helped to reconstruct what happened; however, it was designed more as a forensic tool rather than a tool that exists to tell you where the van or person is right now, just moments after you heard about them. That sense of immediacy is what makes all the difference in response and mitigation.
The third generation of video analytics is purpose-built to offer real-time insight, based on searching for someone or something across multiple cameras, during a time frame typically leading up to the current time. It answers the simple question ‘Where is he now?’ and has to be fast, accurate, and easy to use in a high stress real-time situation.
Security Camera Installation, Security Camera in LA.
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