🤖 AI Summary
In city-scale multi-camera vehicle tracking, cross-camera trajectory matching typically relies on labor-intensive spatiotemporal annotations, leading to high deployment costs and poor scalability.
Method: This paper proposes a self-supervised camera linking approach that eliminates the need for manual labels. It introduces a joint pre-matching mechanism leveraging only vehicle appearance features, trajectory-pair counts, and temporal variance to dynamically infer high-confidence spatial associations between cameras. Furthermore, it designs a probabilistic camera-linking inference framework for end-to-end camera relationship modeling.
Contribution/Results: Evaluated on the CityFlow V2 benchmark, our method achieves an IDF1 score of 61.07%, setting the new state-of-the-art among fully automatic camera-linking methods. It significantly enhances system scalability and practical deployment efficiency while removing dependency on human annotation.
📝 Abstract
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.