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. This lack of suitable approaches motivates our proposal: 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. Our code is available at http://www-vpu.eps.uam.es/publications/Online-MTMC-Tracking.
Highlights
Intelligent Transportation Systems (ITS) are considered a key part of smart cities
We considered the CityFlow benchmark [42], since there is no other publicly available dataset devoted to Multi-Target Multi-Camera (MTMC) vehicle tracking with partially overlapping FOVs
The MTMC tracking ground-truth provided by the CityFlow benchmark consists of the bounding boxes of multi-camera vehicles labeled with consistent IDs
Summary
Intelligent Transportation Systems (ITS) are considered a key part of smart cities. Consistent with the accelerated development of modern sensors, new computing capabilities and Multimedia Tools and Applications communication, ITS technology engages the attention of both academia and industry. One of the main research fields on ITS is visual traffic monitoring using video analytics with data captured by visual sensors. This data can be used to provide information, such as traffic flow estimation, or to detect traffic patterns or anomalies. In recent years it has become an active field within the computer vision community [11, 44, 46], it is still remains a challenging task [30], mainly if we consider the case of multiple cameras
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