Collaborative Pedestrian Tracking and Data Fusion With Multiple Cameras

This work presents a framework of a collaborative multiple-camera tracking system for seamlessly object tracking across fixed cameras in overlapping and nonoverlapping fields of view (FOVs). The proposed system is constructed by two major elements: a client part responsible for single-camera object detection and tracking, and a server part responsible for multiple-camera collaborative tracking on the other hand. To improve object segmentation, this work proposes the concept of foreground edge extraction and compensation with an active edge image. For the single-camera tracking, the Kalman filter was applied. When the object was tracked, the system identified useful features for object matching. Color clustering was conducted using a K-mean clustering method, and the gait period was extracted. Furthermore, the homography technique was used to find the corresponding FOV lines in each camera view. Finally, this work used FOV lines for multiple-camera switching and then integrated various features for object matching. In addition, in order to provide a clear view for monitoring, we have implemented a visualization interface that will automatically show the continuous tracking result through a homography mapping. Simulation results indicate that the proposed system effectively addresses the seamless tracking problem in overlapping and nonoverlapping areas. This paper inspires a paradigm of human visual perception for multicamera object tracking, collaboration, and fusion through distributed cameras and computers.