Collaborative pedestrian tracking with multiple cameras: Data fusion and visualization

Multi-camera tracking is a current trend in video surveillance. This paper proposes a framework for a collaborative multiple-camera tracking system for seamlessly tracking pedestrians across adjacent cameras. This study develops a system consisting of several single camera tracking clients and an information fusing server, and then use a TCP/IP network to exchange information between tracking clients. This work inspires a paradigm of human visual perception, collaboration and fusion through distributed cameras and computers. The proposed system is described in two sections corresponding to the two major elements of the system: A client part responsible for single camera object detection and tracking, and a server part responsible for the multiple cameras collaborative tracking on the other hand. To improve the performance of moving pedestrian matching, gait analysis is adopted based on the feet distance change of the moving objects. Furthermore, this study proposes a cameras switching algorithm to determine whether or not the pedestrian has left the field of view. Simulation results show that the developed system performs object matching and seamless tracking in various environments robustly. The tracking accuracy is as high as 96.9% and 99.7% for two test video sequences, respectively. The resulting system is promising and can be applied to wide-area monitoring and collaborative intelligent surveillance.