Confidence building among correlated streams in multimedia surveillance systems

Abstract

Multimedia surveillance systems utilize multiple correlated media streams, each of which has a different confidence level in accomplishing various surveillance tasks. For example, the system designer may have a higher confidence in the video stream compared to the audio stream for detecting humans running events. The confidence level of streams is usually precomputed based on their past accuracy. This traditional approach is cumbersome especially when we add a new stream in the system without the knowledge of its past history. This paper proposes a novel method which dynamically computes the confidence level of new streams based on their agreement/disagreement with the already trusted streams. The preliminary experimental results show the utility of our method. © Springer-Verlag Berlin Heidelberg 2007.