Box-Grained Reranking Matching for Multi-Camera Multi-Target Tracking

Abstract

Multi-Camera Multi-Target tracking (MCMT) is an essential task in intelligent transportation systems. It is highly challenging due to several problems such as heavy occlusion and appearance variance caused by various camera perspectives and congested vehicles. In this paper, we propose a practical framework for dealing with the city-scale MCMT task, consisting of four modules. The vehicles detection and ReID feature extraction are the first two modules, which locate all vehicles and extract the appearance features for all cameras. The third module is Single-Camera Multi-Target tracking (SCMT), which tracks multiple vehicles to generate candidate trajectories within each camera on the basis of the detected boxes and appearance features. The last module is Inter-Camera Association (ICA), which associates all candidate trajectories between two successive cameras using the K-reciprocal nearest neighbors algorithm, and combines all successively matched trajectories for final results. The ICA module takes the constraints of traveling time, road topology structures, and traffic rules into consideration to reduce the searching space and accelerate the matching speed. Experiments results on the public test set of 2022 AI CITY CHALLENGE Track1 demonstrate the effectiveness of our method, which achieves IDF1 of 84.86%, ranking 1st on the leaderboard.