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Multi-Target Multi-Camera (MTMC) vehicle tracking is an essential task ofvisual traffic monitoring, one of the main research fields of IntelligentTransportation Systems. Several offline approaches have been proposed toaddress this task; however, they are not compatible with real-worldapplications due to their high latency and post-processing requirements. Inthis paper, we present a new low-latency online approach for MTMC tracking inscenarios with partially overlapping fields of view (FOVs), such as roadintersections. Firstly, the proposed approach detects vehicles at each camera.Then, the detections are merged between cameras by applying cross-cameraclustering based on appearance and location. Lastly, the clusters containingdifferent detections of the same vehicle are temporally associated to computethe tracks on a frame-by-frame basis. The experiments show promisinglow-latency results while addressing real-world challenges such as the a prioriunknown and time-varying number of targets and the continuous state estimationof them without performing any post-processing of the trajectories.

Online Clustering-based Multi-Camera Vehicle Tracking in Scenarios with overlapping FOVs