FusionTrack: End-to-End Multi-Object Tracking in Arbitrary Multi-View Environment

• URL: http://arxiv.org/abs/2505.18727v1
• Date: Sat, 24 May 2025 14:51:19 GMT
• Title: FusionTrack: End-to-End Multi-Object Tracking in Arbitrary Multi-View Environment
• Authors: Xiaohe Li, Pengfei Li, Zide Fan, Ying Geng, Fangli Mou, Haohua Wu, Yunping Ge,
• Abstract summary: We propose an end-to-end framework that reasonably integrates tracking and re-identification to leverage multi-view information for robust trajectory association.<n>Experiments on our MDMOT and other benchmark datasets demonstrate that FusionTrack achieves state-of-the-art performance in both single-view and multi-view tracking.
• Score: 7.5152380894919055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-view multi-object tracking (MVMOT) has found widespread applications in intelligent transportation, surveillance systems, and urban management. However, existing studies rarely address genuinely free-viewpoint MVMOT systems, which could significantly enhance the flexibility and scalability of cooperative tracking systems. To bridge this gap, we first construct the Multi-Drone Multi-Object Tracking (MDMOT) dataset, captured by mobile drone swarms across diverse real-world scenarios, initially establishing the first benchmark for multi-object tracking in arbitrary multi-view environment. Building upon this foundation, we propose \textbf{FusionTrack}, an end-to-end framework that reasonably integrates tracking and re-identification to leverage multi-view information for robust trajectory association. Extensive experiments on our MDMOT and other benchmark datasets demonstrate that FusionTrack achieves state-of-the-art performance in both single-view and multi-view tracking.

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