Bayes Network Based Collaborating Control Algorithm in Active Multicamera Network with Applications to Object Tracking

Abstract

Intelligent video surveillance network has many practical applications such as human tracking, vehicle tracking, and event detection. In this paper, an active multicamera network framework is designed for human detection and tracking by optimizing the cameras collaborating control. A multicamera collaborating control algorithm is proposed based on Bayes network to minimize the number of PTZ cameras with control and optimize the cameras' field of view. Hybrid human local feature transform selected by AdaBoost algorithm is adopted to improve the tracking precision. Experimental results on real world environment indicate the effectiveness and efficiency of proposed framework and algorithm.