Abstract
In this paper, we present a novel framework for extracting underlying crowd motion patterns and inferring crowd semantics using mmWave radar. First, our proposed signal processing pipeline combines optical flow estimation concepts from vision with novel statistical and morphological noise filtering to generate high-fidelity mmWave flow fields - compact 2D vector representations of crowd motion. We then introduce a novel approach that transforms these fields into directed geometric graphs, where edges capture dominant flow currents, vertices mark crowd splitting or merging, and flow distribution is quantified across edges. Finally, we show that by analyzing the local Jacobian and computing the corresponding curl and divergence, we can extract key crowd semantics for both structured and diffused crowds. We conduct 21 experiments on crowds of up to (and including) 20 people across 3 areas, using commodity mmWave radar. Our framework achieves high-fidelity graph reconstruction of the underlying flow structure, even for complex crowd patterns, demonstrating strong spatial alignment and precise quantitative characterization of flow split ratios. Finally, our curl and divergence analysis accurately infers key crowd semantics, e.g., abrupt turns, boundaries where flow directions shift, dispersions, and gatherings. Overall, these findings validate our framework, underscoring its potential for various crowd analytics applications.
Abstract (translated)
在这篇论文中，我们提出了一种新的框架，用于通过毫米波雷达（mmWave雷达）提取潜在的人群运动模式并推断人群语义。首先，我们提出的信号处理管道结合了视觉中的光流估计概念，并采用新颖的统计和形态噪声过滤技术来生成高保真度的毫米波流动场——即紧凑的二维向量表示形式，用于描述人群的运动情况。 其次，我们介绍了一种新的方法，该方法将这些流动场转换为有向几何图，在这种方法中，边表示主要的流动方向，顶点标记出人流的分叉或汇聚处，并且流动分布在整个边缘上进行了量化。最后，通过分析局部雅可比矩阵并计算相应的旋度和散度，我们可以提取结构化人群和扩散人群中关键的人群语义信息。 我们在三个不同区域对多达（包括）20人的群体进行了21项实验，并使用了商用毫米波雷达设备。我们的框架能够以高保真度重构潜在的流动结构图，即使对于复杂的人员流动模式也是如此，这表明在空间对齐和精确量化流动分叉比方面表现出色。此外，通过旋度和散度分析，可以准确地推断出关键的人群语义信息，例如突然转向、流向改变的边界、扩散事件以及聚集行为。 总体而言，这些发现验证了我们框架的有效性，并突显其在多种人群数据分析应用中的潜力。
URL
https://arxiv.org/abs/2507.07331
