Multi-Target Multi-Camera Tracking has a wide range of applications and is the basis for many advanced inferences and predictions. This paper describes our solution to the Track 3 multi-camera vehicle tracking task in 2021 AI City Challenge (AICITY21). This paper proposes a multi-target multi-camera vehicle tracking framework guided by the crossroad zones. The framework includes: (1) Use mature detection and vehicle re-identification models to extract targets and appearance features. (2) Use modified JDETracker (without detection module) to track single-camera vehicles and generate single-camera tracklets. (3) According to the characteristics of the crossroad, the Tracklet Filter Strategy and the Direction Based Temporal Mask are proposed. (4) Propose Sub-clustering in Adjacent Cameras for multi-camera tracklets matching. Through the above techniques, our method obtained an IDF1 score of 0.8095, ranking first on the leaderboard. The code have released: https://github.com/LCFractal/AIC21-MTMC.
@article{arxiv.2105.06623,
title = {City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones},
author = {Chong Liu and Yuqi Zhang and Hao Luo and Jiasheng Tang and Weihua Chen and Xianzhe Xu and Fan Wang and Hao Li and Yi-Dong Shen},
journal= {arXiv preprint arXiv:2105.06623},
year = {2021}
}
Comments
CVPR 2021 AI CITY CHALLENGE City-Scale Multi-Camera Vehicle Tracking Top 1