City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones

• URL: http://arxiv.org/abs/2105.06623v1
• Date: Fri, 14 May 2021 03:01:17 GMT
• Title: City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones
• Authors: 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
• Abstract summary: This paper describes our solution to the Track 3 multi-camera vehicle tracking task in 2021 AI City Challenge (AICITY21) The framework includes:. Use mature detection and vehicle re-identification models to extract targets and appearance features. According to the characteristics of the crossroad, the Tracklet Filter Strategy and the Direction Based Temporal Mask are proposed.
• Score: 28.922703073971466
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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