Program Monitors Security Camera Feeds Better Than Humans
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CREDIT: CREDIT: winul | Shutterstock.com |
A new computer program can monitor multiple security video feeds for suspicious activity more accurately and in a fraction of the time it would take a human camera operator.
“You can’t have a person staring at every single screen, and even if you did the person might not know exactly what to look for,” said the program's co-developer Christopher Amato, a postdoc at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).
“For example, a person is not going to be very good at searching through pages and pages of faces to try to match [an intruder] with a known criminal or terrorist.”
Computer-vision systems already exist, but they tend to work fairly slowly and are generally not good at raising an alarm at the first sign of trouble.
But the new system, developed by researchers at MIT, uses a series of algorithms to reach a compromise between accuracy – so the system does not trigger an alarm every time a cat walks in front of the camera, for example – with the speed needed to allow security staff to act on an intrusion as quickly as possible.
The system can adjusts itself to the type of setting in which it is being applied, like an airport. It first analyzes test footage using a series of algorithms. These include skin detection algorithms that can identify a person in an image, or background detection systems that detect unusual objects, or when something is moving through the scene.
The program determines how long it takes each algorithm to run and then organizes them so they run in the most efficient order.
Like a human detective, the system can also take context into account when analyzing a set of images, Amato said.
So for instance, if the system is being used at an airport, it could be programmed to identify and track particular people of interest, and to recognize objects that are strange or in unusual locations, he says. It could also be programmed to sound an alarm whenever there are any objects or people in the scene, when there are too many objects, or if the objects are moving in ways that give cause for concern.
In addition to port and airport security, the system could monitor video information obtained by a fleet of unmanned aircraft, Amato said. It could also be used to analyze data from weather-monitoring sensors to determine where tornados are likely to appear, or information from water samples taken by autonomous underwater vehicles. The system would determine how to obtain the information it needs in the least amount of time and with the fewest possible sensors.
Amato and his colleagues will present their system in a paper at the 24th IAAI Conference on Artificial Intelligence in Toronto in July.
