用动态图网络统一建模多视角人体跟踪，无需预设轨迹片段。
One Graph to Track Them All: Dynamic GNNs for Single- and Multi-View Tracking
- 构建时空动态图，融合空间、上下文与时间信息
- 在新数据集上达到顶尖性能，尤其在遮挡场景表现优异
- 适合研究多视角人体跟踪与复杂场景建模的学者
本文提出一种统一的可微模型，用于多人群体跟踪，无需依赖预计算的轨迹片段。模型构建动态时空图，聚合空间、上下文与时间信息，实现序列内信息的无缝传播。为提升遮挡处理能力，图结构还能编码场景特异性信息。研究引入一个包含25个部分重叠视角的大规模新数据集，具备精细场景重建和大量遮挡。实验表明，该模型在公开基准和新数据集上均达到当前最优性能，且对多种条件具有强适应性。数据集与方法将开源，以推动多人群体跟踪研究。
原文摘要 · Abstract (English)
This work presents a unified, fully differentiable model for multi-people tracking that learns to associate detections into trajectories without relying on pre-computed tracklets. The model builds a dynamic spatiotemporal graph that aggregates spatial, contextual, and temporal information, enabling seamless information propagation across entire sequences. To improve occlusion handling, the graph can also encode scene-specific information. We also introduce a new large-scale dataset with 25 partially overlapping views, detailed scene reconstructions, and extensive occlusions. Experiments show the model achieves state-of-the-art performance on public benchmarks and the new dataset, with flexibility across diverse conditions. Both the dataset and approach will be publicly released to advance research in multi-people tracking.
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