FusionTrack: End-to-End Multi-Object Tracking in Arbitrary Multi-View Environment
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913857642954752 |
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| author | Li, Xiaohe Li, Pengfei Fan, Zide Geng, Ying Mou, Fangli Wu, Haohua Ge, Yunping |
| author_facet | Li, Xiaohe Li, Pengfei Fan, Zide Geng, Ying Mou, Fangli Wu, Haohua Ge, Yunping |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18727 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FusionTrack: End-to-End Multi-Object Tracking in Arbitrary Multi-View Environment Li, Xiaohe Li, Pengfei Fan, Zide Geng, Ying Mou, Fangli Wu, Haohua Ge, Yunping Computer Vision and Pattern Recognition 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. |
| title | FusionTrack: End-to-End Multi-Object Tracking in Arbitrary Multi-View Environment |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.18727 |