A Robust Online Multi-Camera People Tracking System With Geometric Consistency and State-aware Re-ID Correction

Zhenyu Xie, Zelin Ni, Wenjie Yang, Yuang Zhang, Yihang Chen, Yang Zhang, Xiao Ma; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 7007-7016

Multi-camera multiple people tracking is a crucial technology for surveillance crowd management and social behavior analysis enabling large-scale monitoring and comprehensive understanding of complex scenarios involving multiple individuals across different camera views. However due to severe occlusion within the scene and significant variations in camera viewpoints there are high demands for matching and correlating the same target among different cameras especially in an online setting. To address this challenge we propose a novel online multi-camera multiple people tracking system. This system integrates geometric-consistent constraints and appearance information of the targets effectively improving tracking accuracy. Additionally we design a state-aware Re-ID correction mechanism that adaptively leverages Re-ID features to correct mismatches among targets. This system has demonstrated good adaptability across various scenarios. Our proposed system is evaluated in track1 of the 2024 AI City Challenge achieving a HOTA score of 67.2175% and securing the 2nd position on the leaderboard. The code will be available at: https://github.com/ZhenyuX1E/PoseTrack