Abstract
The adoption of Millimeter-Wave (mmWave) radar devices for human sensing, particularly gait recognition, has recently gathered significant attention due to their efficiency, resilience to environmental conditions, and privacy-preserving nature. In this work, we tackle the challenging problem of Open-set Gait Recognition (OSGR) from sparse mmWave radar point clouds. Unlike most existing research, which assumes a closed-set scenario, our work considers the more realistic open-set case, where unknown subjects might be present at inference time, and should be correctly recognized by the system. Point clouds are well-suited for edge computing applications with resource constraints, but are more significantly affected by noise and random fluctuations than other representations, like the more common micro-Doppler signature. This is the first work addressing open-set gait recognition with sparse point cloud data. To do so, we propose a novel neural network architecture that combines supervised classification with unsupervised reconstruction of the point clouds, creating a robust, rich, and highly regularized latent space of gait features. To detect unknown subjects at inference time, we introduce a probabilistic novelty detection algorithm that leverages the structured latent space and offers a tunable trade-off between inference speed and prediction accuracy. Along with this paper, we release mmGait10, an original human gait dataset featuring over five hours of measurements from ten subjects, under varied walking modalities. Extensive experimental results show that our solution attains F1-Score improvements by 24% over state-of-the-art methods, on average, and across multiple openness levels.
Abstract (translated)
毫米波（mmWave）雷达设备在人体感应中的应用，特别是步态识别领域，因其实效性、环境条件的适应性和隐私保护特性而备受关注。本研究旨在解决来自稀疏毫米波雷达点云数据的开放集步态识别（OSGR）这一挑战性问题。与大多数现有的假设封闭集合场景的研究不同，我们的工作考虑了更为现实的开放集案例，在这种情况下，未知主体可能在推理时出现，并且系统应能够正确地识别它们。点云非常适合资源受限边缘计算应用的需求，但与其他表示方式相比（如常见的微多普勒签名），其更容易受到噪声和随机波动的影响。这是首个针对稀疏点云数据的开放集步态识别的研究工作。 为了解决这一问题，我们提出了一种新颖的神经网络架构，该架构结合了监督分类与无监督重建点云技术，从而创建了一个具有强大鲁棒性、丰富性和高度正则化的潜在步态特征空间。为了在推理时检测未知主体，我们引入了一种概率异常检测算法，利用结构化潜在空间，并提供了可调节的推断速度和预测准确性之间的权衡。 此外，本研究还发布了mmGait10数据集——一个包含来自十名不同参与者的超过五小时步态测量的新原始人体步态数据库，涵盖了多种步行模式。广泛的实验结果表明，我们的解决方案在开放性程度不同的情况下，平均比现有方法的F1-Score提高了24%。
URL
https://arxiv.org/abs/2503.07435
