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arXiv · 2407.01007

GMT: Effective Global Framework for Multi-Camera Multi-Target Tracking

Yihao Zhen·Mingyue Xu·Qiang Wang·Baojie Fan·Jiahua Dong·Tinghui Zhao·Huijie Fan
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Abstract

Multi-Camera Multi-Target (MCMT) tracking aims to locate and associate the same targets across multiple camera views. Existing methods typically adopt a two-stage framework, involving single-camera tracking followed by inter-camera tracking. However, in this paradigm, multi-view information is used only to recover missed matches in the first stage, providing a limited contribution to overall tracking. To address this issue, we propose GMT, a global MCMT tracking framework that jointly exploits intra-view and inter-view cues for tracking. Specifically, instead of assigning trajectories independently for each view, we integrate the same historical targets across different views as global trajectories, thereby reformulating the two-stage tracking as a unified global-level trajectory-target association process. We introduce a Cross-View Feature Consistency Enhancement (CFCE) module to align visual and spatial features across views, providing a consistent feature space for global trajectory modeling. With these aligned features, the Global Trajectory Association (GTA) module associates new detections with existing global trajectories, enabling direct use of multi-view information. Compared to the two-stage framework, GMT achieves significant improvements on existing datasets, with gains of up to 21.3 percent in CVMA and 17.2 percent in CVIDF1. Furthermore, we introduce VisionTrack, a high-quality, large-scale MCMT dataset providing significantly greater diversity than existing datasets. Our code and dataset will be released.

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Yihao Zhen, Mingyue Xu, Qiang Wang, Baojie Fan, Jiahua Dong, Tinghui Zhao, Huijie Fan. 2024-07-01. GMT: Effective Global Framework for Multi-Camera Multi-Target Tracking. https://arxiv.org/abs/2407.01007

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