  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.
