Police and security teams guarding airports, docks and border crossings from terrorist attack or illegal entry need to know immediately when someone enters a prohibited area, and who they are. A network of surveillance cameras is typically used to monitor these at-risk locations 24 hours a day, but these can generate too many images for human eyes to analyze.
Now a system being developed by Christopher Amato, a postdoc at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), can perform this analysis 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,” Amato says. “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.”
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