Improved Object Classification and Tracking Based on Overlapping Cameras in Video Surveillance

Abstract

Object classification and tracking are important in intelligent video surveillance systems. In this paper, an approach based on multiple overlapping cameras cooperation is proposed for object classification and tracking. In the proposed surveillance system, all the cameras are connected to the central computer server through network connection. Viewpoint correspondence and data fusion from multiple overlapping cameras are utilized to improve object classification and tracking in complex occlusion scenes. This paper demonstrates the benefit gained both in tracking and classification through the communication between the two individual modules. Experimental results show that the proposed method achieves higher classification accuracy and tracking performance in comparison with single-camera method.