Efficient Multi-View Multi-Target Tracking Using a Distributed Camera Network

In this paper, we propose a multi-target tracking method using a distributed camera network, which can effectively handle the occlusion and reidenfication problems by combining advanced deep learning and distributed information fusion. The targets are first detected using a fast object detection method based on deep learning. We then combine the deep visual feature information and spatial trajectory information in the Hungarian algorithm for robust targets association. The deep visual feature information is extracted from a convolutional neural network, which is pre-trained using a large-scale person reidentification dataset. The spatial trajectories of multiple targets in our framework are derived from a multiple view information fusion method, which employs an information weighted consensus filter for fusion and tracking. In addition, we also propose an efficient track processing method for ID assignment using multiple view information. The experiments on public datasets show that the proposed method is robust to solve the occlusion problem and reidentification problem, and can achieve superior performance compared to the state of the art methods.