AI 中文总结
该研究提出毫米波雷达解耦密集人群的新框架，结合宏微观结构与语义引导多假设推理，经18项实验验证，在行人足迹恢复上显著优于当前最优方法，鲁棒性强。
AI 中文摘要
本文提出一种用于恢复密集人群中个体足迹与行人空间行为的新型框架。该问题极具挑战性，因为在拥挤区域中，行人会频繁且长时间地相互融合或遮挡，导致观测结果高度纠缠。我们的方法同时利用学习到的人群语义和对持续雷达可观测性损失的原则性推理，系统地推断个体空间足迹。在宏观层面，我们从雷达点云中学习人群的空间使用模式；在微观层面，我们开发了一种由持续融合和遮挡导致的雷达可观测性损失的物理驱动模型。基于这种宏微观结构，我们引入了语义引导的多假设推理基础，通过对多对一和一对无观测映射的原则性推理来评估竞争假设，从而实现个体空间足迹的解耦。我们使用现成的毫米波雷达，通过18项真实世界实验对该框架进行评估，涉及最多10人的6类不同行人行为及4种环境。我们的方法即使在长时间持续的可观测性损失期间也能稳健地恢复个体空间足迹，与真实值表现出强结构一致性。恢复的足迹还可实现对行人空间使用的准确区域分析。最后，我们进行了消融研究并与当前最优方法对比，所提出的基础在所有实验中均显著优于当前最优方法，平均中位数DTW为30.5 cm，而当前最优方法为95.94 cm，凸显了该框架对频繁且长时间融合与遮挡的鲁棒性。
英文摘要
In this paper, we present a novel framework for recovering individual footprints and pedestrian spatial behaviors in dense crowds. This is a highly challenging problem, as pedestrians can merge or block one another often and for prolonged periods in crowded areas, leading to strongly entangled observations. Our approach jointly leverages learned crowd semantics and principled reasoning over persistent radar observability loss to systematically infer individual spatial footprints. At the macroscopic level, we learn spatial usage patterns of the crowd from radar point clouds, while at the microscopic level we develop a physics-informed model of radar observability loss caused by sustained merging and occlusion. Building on this macro-micro structure, we introduce a semantic-guided multi-hypothesis reasoning foundation that evaluates competing hypotheses via principled reasoning over many-to-one and one-to-none observation mappings, enabling untangling of individual spatial footprints. We evaluate the framework through 18 real-world experiments using an off-the-shelf mmWave radar, involving crowds of up to (and including) 10 individuals across six categories of diverse pedestrian behaviors, and four environments. Our methodology robustly recovers individual spatial footprints, even during prolonged and persistent observability loss, demonstrating strong structural consistency with the ground-truth. The recovered footprints further enable accurate zonal analysis of pedestrian space usage. Finally, we present an ablation study and further compare against state-of-the-art. The proposed foundation substantially outperforms the state-of-the-art across all experiments, achieving an average median DTW of 30.5 cm compared to 95.94 cm, highlighting the framework's robustness to frequent and prolonged merging and occlusion.
