Multi-Target Multi-Camera Tracking based on lightweight detector

Multi-target multi-camera tracking (MTMCT) aims to associate the multiple targets in consecutive frames to obtain trajectories under multiple cameras. This paper proposes a practical MTMCT framework, which is mainly composed of a lightweight detector, ReID fusion module, tracker, and cross-camera matching module. We design a lightweight detector of gate recursive convolution to improve detection efficiency while ensuring detection accuracy. To accurately track the targets in low confidence under occlusion, we propose a feature compensation strategy to search and compensate corresponding target features in previous frames. To match targets between cameras, we utilize the temporal constraints of the region to filter out false positive trajectories and merge all candidate trajectories according to the similarity matrix. In the post-processing stage, we incorporate the Re-rank, k-mutual nearest neighbor, and the hierarchical clustering algorithm to generate the final tracking results. Experiments conducted on highway scenarios and city-flow datasets demonstrate the competitive performance of the proposed method.