Online Clustering-based Multi-Camera Vehicle Tracking in Scenarios with overlapping FOVs
This work addresses the problem of real-time multi-camera vehicle tracking for intelligent transportation systems, offering an incremental improvement over existing offline methods by reducing latency and post-processing requirements.
This paper introduces a low-latency online approach for multi-target multi-camera (MTMC) vehicle tracking in scenarios with overlapping fields of view, such as road intersections. The method detects vehicles per camera, merges detections across cameras using appearance and location-based clustering, and then temporally associates these clusters to form tracks frame-by-frame, achieving promising low-latency results without post-processing.
Multi-Target Multi-Camera (MTMC) vehicle tracking is an essential task of visual traffic monitoring, one of the main research fields of Intelligent Transportation Systems. Several offline approaches have been proposed to address this task; however, they are not compatible with real-world applications due to their high latency and post-processing requirements. In this paper, we present a new low-latency online approach for MTMC tracking in scenarios with partially overlapping fields of view (FOVs), such as road intersections. Firstly, the proposed approach detects vehicles at each camera. Then, the detections are merged between cameras by applying cross-camera clustering based on appearance and location. Lastly, the clusters containing different detections of the same vehicle are temporally associated to compute the tracks on a frame-by-frame basis. The experiments show promising low-latency results while addressing real-world challenges such as the a priori unknown and time-varying number of targets and the continuous state estimation of them without performing any post-processing of the trajectories.