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

TL;DR

This paper introduces GMT, a unified global framework for multi-camera multi-target tracking that jointly exploits intra-view and inter-view cues, significantly improving tracking accuracy over existing two-stage methods.

Contribution

The paper proposes GMT, a novel global MCMT tracking framework with a Cross-View Feature Consistency Enhancement module and a Global Trajectory Association module, advancing beyond traditional two-stage approaches.

Findings

Achieves up to 21.3% improvement in CVMA

Achieves up to 17.2% improvement in CVIDF1

Introduces the VisionTrack dataset with greater diversity

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.