Multi-Camera Multi-Vehicle Tracking Guided by Highway Overlapping FoVs

Hongkai Zhang, Ruidi Fang, Suqiang Li, Qiqi Miao, Xinggang Fan (), Jie Hu () and Sixian Chan
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Hongkai Zhang: School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei 230601, China
Ruidi Fang: School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei 230601, China
Suqiang Li: School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei 230601, China
Qiqi Miao: School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei 230601, China
Xinggang Fan: Zhijiang College, Zhejiang University of Technology, Hangzhou 312030, China
Jie Hu: Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
Sixian Chan: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

Mathematics, 2024, vol. 12, issue 10, 1-22

Abstract: Multi-Camera Multi-Vehicle Tracking (MCMVT) is a critical task in Intelligent Transportation Systems (ITS). Differently to in urban environments, challenges in highway tunnel MCMVT arise from the changing target scales as vehicles traverse the narrow tunnels, intense light exposure within the tunnels, high similarity in vehicle appearances, and overlapping camera fields of view, making highway MCMVT more challenging. This paper presents an MCMVT system tailored for highway tunnel roads incorporating road topology structures and the overlapping camera fields of view. The system integrates a Cascade Multi-Level Multi-Target Tracking strategy (CMLM), a trajectory refinement method (HTCF) based on road topology structures, and a spatio-temporal constraint module (HSTC) considering highway entry–exit flow in overlapping fields of view. The CMLM strategy exploits phased vehicle movements within the camera’s fields of view, addressing such challenges as those presented by fast-moving vehicles and appearance variations in long tunnels. The HTCF method filters static traffic signs in the tunnel, compensating for detector imperfections and mitigating the strong lighting effects caused by the tunnel lighting. The HSTC module incorporates spatio-temporal constraints designed for accurate inter-camera trajectory matching within overlapping fields of view. Experiments on the proposed Highway Surveillance Traffic (HST) dataset and CityFlow dataset validate the system’s effectiveness and robustness, achieving an IDF1 score of 81.20% for the HST dataset.

Keywords: multi-camera multi-vehicle tracking; ReID; camera overlapping fields of view; highway tunnel scenarios; topology modeling (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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