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System improves automated monitoring of security cameras

A system being developed by Christopher Amato, a postdoc at MITs Computer Science and Artificial Intelligence Laboratory (CSAIL), can perform security-camera analysis to identify potential terrorists or illegal entrymore accurately and in a fraction of the time it would take a human camera operator. So Amato and colleagues Komal Kapoor, Nisheeth Srivastava and Paul Schrater at the University of Minnesota are developing a system that uses mathematics 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. We plug all of the things we have learned into the POMDP framework, and it comes up with a policy that might tell you to start out with a skin analysis, for example, and then depending what you find out, you might run an analysis to try to figure out who the person is, or use a tracking system to figure out where they are [in each frame], Amato says......

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June 07, 2012 June 10, 2012 Kurzweilai Applications MachineLearning Vision NewsFinder Text English HTML

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