COMPUTING RESEARCH HIGHLIGHT OF THE WEEK [June 7- June 15]
System Improves Automated Monitoring of Security Cameras
Massachusetts Institute of Technology (MIT) researchers have developed a system that can analyze several surveillance cameras more accurately and in less time than it would take a human operator. The system, known as partially observable Markov decision process (POMDP), uses mathematics to reach a compromise between accuracy and speed to enable security staff to act on an intrusion as quickly as possible.
The system first conducts a learning phase, in which it assesses how each piece of software works in the type of setting in which it is being applied. The system then adds the information to its mathematical framework, which determines which of the available algorithms to run on the situation. "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]," says MIT's Christopher Amato. The system also can take context into account when analyzing a set of images.
Full Article...
Source: MIT News
Researchers:
Christopher Amato (University of Minnesota)
Paul R. Schrater (University of Minnesota)
Komal Kapoor
Nisheeth Srivastava
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Computing Research Highlight of the Week is a service of the Computing Community Consortium and the Computing Research Association designed to highlight some of the exciting and important recent research results in the computing fields. Each week a new highlight is chosen by CRA and CCC staff and volunteers from submissions from the computing community. Want your research featured? Submit it!.
