提出端到端多视角多目标追踪框架，支持任意视角无人机协同追踪
FusionTrack: End-to-End Multi-Object Tracking in Arbitrary Multi-View Environment
- 构建多无人机多目标追踪数据集MDMOT，首次实现任意视角追踪基准
- 融合追踪与重识别，端到端利用多视角信息提升轨迹关联鲁棒性
- 在真实场景下实现最优性能，适合智能交通与城市监控应用
多视角多目标追踪（MVMOT）在智能交通、监控系统和城市管理中广泛应用。然而，现有研究极少关注真正的自由视角多视角追踪系统，这严重限制了协作追踪系统的灵活性与可扩展性。为填补这一空白，我们首先构建了多无人机多目标追踪（MDMOT）数据集，由移动无人机群在多样化真实场景中采集，首次建立了任意多视角环境下的多目标追踪基准。在此基础上，我们提出了端到端的FusionTrack框架，合理融合追踪与重识别，充分挖掘多视角信息以实现鲁棒的轨迹关联。在MDMOT及其他基准数据集上的大量实验表明，FusionTrack在单视角和多视角追踪任务中均达到当前最优性能。
原文摘要 · Abstract (English)
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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