Pattern discovery for video surveillance

There is a need in many surveillance applications to automatically detect certain events, such as activities and/or behaviors exhibited by people, vehicle, or other moving objects. Existing systems require that every event be custom coded, predefined, into the computer system. We present a novel system that can automatically capture and define (learn) new events by pattern discovery, and further presents the events to the operator for confirmation. The operator checks for validity of the newly detected events and adds them into the event library. We also propose a new feature selection procedure that can uniquely identify important events such as people falling. We present experimental results on real dataset, which shows the effectiveness of the proposed method.