DETECTING EVENTS IN VIDEO SURVEILLANCE SYSTEM

Multimedia systems utilize multiple media streams, each of which has different confidence levels in accomplishing various detection tasks. For example, in a multimedia surveillance system, one would usually have higher confidence in an audio stream compared to a video stream for detecting human shouting events. The pre-computation of these confidence levels is cumbersome especially when new media streams are dynamically added to the system. This paper proposes a novel method, which dynamically computes the confidence levels of new streams based on the past history of their agreement/disagreement with the already trusted streams. To demonstrate the utility of the proposed method, we provide the experimental results for detecting events in a multimedia surveillance scenario.