  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.
