Online clustering-based multi-camera vehicle tracking in scenarios with overlapping FOVs
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
Springer NatureDate
2022-02Citation
10.1007/s11042-022-11923-2
Multimedia Tools and Applications 81.5 (2022): 7063-7070
ISSN
1380-7501 (print); 1573-7721 (online)DOI
10.1007/s11042-022-11923-2Funded by
This work was partially supported by the Spanish Government (TEC2017-88169-RMobiNetVideo). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPU used for the research of our groupProject
Gobierno de España. TEC2017-88169-RMobiNetVideoEditor's Version
https://doi.org/10.1007/s11042-022-11923-2Subjects
Multi-camera tracking; Multi-target tracking; Online tracking; TelecomunicacionesNote
The version of record of this article, first published in Multimedia Tools and Applications, is available online at Publisher’s website: http://dx.doi.org/10.1007/s11042-022-11923-2Rights
© The author(s)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
Files in this item
Google Scholar:Luna García, Elena
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San Miguel Avedillo, Juan Carlos
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Martínez Sánchez, José María
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Escudero Viñolo, Marcos
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