无需中心节点，100个摄像头实时3D跟踪精度超90%
Fully Distributed Multi-View 3D Tracking in Real-Time
- 各摄像头自主运行轻量模块，通过点对点通信协同跟踪
- 在WILDTRACK上达96.5% IDF1，SCOUT上首次突破50% MOTA
- 零样本部署，无须训练即可用于新场景
多视角3D跟踪通常依赖中心化融合，导致计算瓶颈，难以规模化部署。本文提出MV3DT，一种完全分布式的实时多视角3D跟踪框架，通过摄像头间的对等协作实现准确的身份传播与遮挡恢复，无需中央聚合。每个相机节点执行轻量级模块化流程：单目3D感知、分布式多视角关联及轻量级消息传递的协作融合。在WILDTRACK数据集上达到96.5% IDF1、93.1% MOTA和94.6% MOTP，性能媲美最先进的集中式方法；在SCOUT上实现前所未有的41.7% IDF1和50.9% MOTA。系统具备优异可扩展性：100个摄像头下保持30 FPS，跨摄像头延迟低于10ms，通信开销仅2.2%。给定相机标定信息后，无需场景特定学习即可零样本部署，直接应用于新环境。结果表明，MV3DT是大规模重叠摄像头网络中实时多视角跟踪的实用解决方案。
原文摘要 · Abstract (English)
Multi-camera tracking with overlapping fields of view typically relies on centralized fusion, which creates computational bottlenecks that prevent deployment at scale. We present MV3DT, a fully distributed framework for real-time multi-view 3D tracking that achieves accurate identity propagation and occlusion recovery through peer-to-peer coordination, eliminating the need for central aggregation. Each camera node executes a lightweight modular pipeline comprising monocular 3D perception, distributed multi-view association, and collaborative fusion via lightweight messaging. MV3DT achieves 96.5% IDF1, 93.1% MOTA, and 94.6% MOTP on WILDTRACK, competitive with state-of-the-art centralized methods, and unprecedented 41.7% IDF1 and 50.9% MOTA on SCOUT while demonstrating superior scalability: sustaining 30 FPS on 100 cameras with <10ms inter-camera latency and only 2.2% communication overhead. MV3DT operates in a zero-shot regime given camera calibrations, requiring no scene-specific learning and making it directly deployable in new environments. These results establish MV3DT as a practical solution for real-time multi-view tracking in large-scale overlapping camera networks.
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