Weak-Cue Mixed Similarity Matrix and Boundary Expansion Clustering for Multi-Target Multi-Camera Tracking Systems in Highway Scenarios

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.