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.

In highway scenarios, factors such as high-speed vehicle movement, lighting conditions, and positional changes significantly affect the quality of trajectories in multi-object tracking. This, in turn, impacts the trajectory clustering process within the multi-target multi-camera tracking (MTMCT) system. To address this challenge, we present the weak-cue mixed similarity matrix and boundary expansion clustering (WCBE) MTMCT system. First, the weak-cue mixed similarity matrix (WCMSM) enhances the original trajectory features by incorporating weak cues. Then, considering the practical scene and incorporating richer information, the boundary expansion clustering (BEC) algorithm improves trajectory clustering performance by taking the distribution of trajectory observation points into account. Finally, to validate the effectiveness of our proposed method, we conduct experiments on both the Highway Surveillance Traffic (HST) dataset developed by our team and the public CityFlow dataset. The results demonstrate promising outcomes, validating the efficacy of our approach.