A Clustering Approach for Controlling PTZ Cameras in Automated Video Surveillance

The efficient control of Pan/Tilt/Zoom (PTZ) cameras has been a major research problem. This paper presents a solution that seeks to optimize the overall subject recognition probability by controlling various deployed cameras, based on the characteristics of the subjects in the surveillance area. In particular, we propose and analyze a clustering-based approach, which can be used in conjunction with recently proposed camera scheduling schemes, to achieve significant improvements in both the subject recognition probability and the algorithm computation time. We extensively analyze the effectiveness of the clustering approach, considering the impacts of subject arrival rate.