KNOWLEDGE EXTRACTION FROM SURVEILLANCE SENSORS

RAMA CHELLAPPA, ASHOK VEERARAGHAVAN, AND ASWIN C. SANKARANARAYANAN

Center for Automation Research and Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland

1 INTRODUCTION

In the last decade, surveillance and monitoring has become critical for homeland security. With this increasing focus on surveillance of large public areas, a traditional human-centric surveillance system where a human operator watches a bank of cameras is largely being supported with automated surveillance suites. In a typical public area such as an airport or a train station there could be anywhere between 50 to a few hundred cameras deployed all over the area. It is almost impossible for human operators to keep a close watch on all of these cameras and continuously and robustly identify subjects and events of interest. Therefore, there is a greater need for automated analysis of the data obtained from multiple video cameras and other sensors that are distributed all around the area of interest.

Several current state-of-the-art surveillance systems work in aid of human operators. These surveillance systems have a host of sensors [visual, audio, infrared (IR) etc.] that are distributed in the area of interest. These sensors are in turn networked and connected to a central command center, where sophisticated algorithms for varied tasks such as person detection, tracking, and recognition; vehicle detection and classification; ...

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