Surveillance cameras can serve as an effective deterrent to crime, provided security personnel actually watch the video streams from the cameras. But having the staff resources to monitor dozens of cameras at once is beyond the reach of many organizations.
With behavior-recognition software, however, it all becomes possible. By layering video-analytics software over an existing closed circuit TV (CCTV) system, law enforcement can use video as a preventive measure because officers get alerted to crimes as they occur, instead of monitoring dozens of video screens at the same time or just using the video as forensic evidence.
The technology is being used successfully at Johns Hopkins University in Baltimore, where a runaway crime rate was significantly reduced. At the Rapid City, S.D., Regional Airport, the technology effectively replaced Transportation Security Administration personnel in some areas.
The software can be configured to recognize people's movements, such as someone running, and changes in landscape, such as a package left behind, and then alert appropriate personnel. A behavior-recognition system can free up staff to take on other tasks until there is an alert, or allow a smaller staff to monitor dozens of cameras.
"It is programmed to recognize a wide variety of things based on what you, as a law enforcement person, would care about," said Nik Gagvani, chief technology officer at Cernium, which manufactures a behavior recognition system called Perceptrak. "You can customize it to create what we call a 'behavior profile.' It would just report those behaviors to you."
A behavior-recognition module can be programmed to recognize up to 18 different behavioral patterns, including slow-moving vehicles; erratic movement; a person falling down; an abandoned object; or a number of people converging. The module can be programmed to assign a score to each alert based on the significance of the behavior.
The score is influenced by factors such as time of day and others programmed into the system. All alerts are then stored automatically, giving investigators forensic evidence. How It Works
"If you think of video, it's really a series of images one after the other over time," Gagvani said. "If you look at any pair of successive images, and you subtract or find the difference of these images, you would see where it's changed over time."
The analysis is done by looking at how the pixels in the video change over time. The software is configured to recognize certain changes that correspond to movement.
It can also recognize, through a process called segmentation, if something has been left behind, such as a backpack that isn't part of the landscape.
A video scene includes ambient props such as trees, buildings and other permanent structures. That's all considered background. Anything that moves through the scene is considered foreground. Segmentation is the process of separating the background from the foreground. If a bag was left in a scene, for instance, it would not be part of the background and would trigger an alert from the system.
"Now the question is, 'Do the differences mean anything from a security point of view?'" Gagvani said. "That's what behavior recognition is all about. It's not just about what is referred to as motion detection, which has been around for a while, but now trying to qualify that motion and make sense of it. It gives you information you can act on. You don't have to look at the video until one of these things you care about is discovered by the behavior recognition module."
Then it's up to law enforcement personnel to look at the video feed and assess whether action needs to be taken.
"Basically the technology is trying to be the unblinking eye, so to speak," Gagvani said. "It's not as sophisticated as