Multi-Target Multi-Camera Tracking (MTMCT) has broad applications and forms the basis for numerous future city-wide systems (e.g. traffic management, crash detection, etc.). However, the challenge of matching vehicle trajectories across different cameras based solely on feature extraction poses significant difficulties. This article introduces an innovative multi-camera vehicle tracking system that utilizes a self-supervised camera link model. In contrast to related works that rely on manual spatial-temporal annotations, our model automatically extracts crucial multi-camera relationships for vehicle matching. The camera link is established through a pre-matching process that evaluates feature similarities, pair numbers, and time variance for high-quality tracks. This process calculates the probability of spatial linkage for all camera combinations, selecting the highest scoring pairs to create camera links. Our approach significantly improves deployment times by eliminating the need for human annotation, offering substantial improvements in efficiency and cost-effectiveness when it comes to real-world application. This pairing process supports cross camera matching by setting spatial-temporal constraints, reducing the searching space for potential vehicle matches. According to our experimental results, the proposed method achieves a new state-of-the-art among automatic camera-link based methods in CityFlow V2 benchmarks with 61.07% IDF1 Score.

本研究提出了一种创新的多摄像头车辆追踪系统，利用自监督的摄像头链接模型，自动提取关键的多摄像头关系来进行车辆匹配，并通过设置时空约束来支持跨摄像头匹配，在实际应用中具有显著的效率和成本效益改善，实验证明该方法在CityFlow V2基准测试中达到了自动摄像头链接方法的最新技术水平，IDF1得分为61.07%。

具有改进的自主监督相机连接模型的城市级多摄像机车辆跟踪系统