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

TL;DR

This paper introduces a low-latency online multi-camera vehicle tracking method that effectively handles overlapping fields of view in real-time traffic scenarios, avoiding post-processing delays.

Contribution

It presents a novel online approach combining detection, cross-camera clustering, and temporal association for real-time vehicle tracking in overlapping FOVs.

Findings

Achieves low-latency tracking suitable for real-world traffic monitoring

Handles unknown and varying number of targets effectively

Operates without post-processing of trajectories

Abstract

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.