基于稀疏毫米波雷达点云的开放集步态识别
Open-Set Gait Recognition from Sparse mmWave Radar Point Clouds
AI总结：
本文首次研究基于稀疏毫米波雷达点云的开放集步态识别，提出结合有监督分类与无监督重建的神经网络及概率新颖性检测算法，并发布mmGait10数据集，F1分数平均提升24%。
摘要：
近年来，采用毫米波（mmWave）雷达设备进行人体感知，特别是步态识别，因其高效性、对环境条件的鲁棒性以及隐私保护特性而受到广泛关注。在本工作中，我们解决了基于稀疏毫米波雷达点云的开放集步态识别（OSGR）这一具有挑战性的问题。与大多数现有研究假设封闭集场景不同，我们的工作考虑了更现实的开放集情况，即在推理时可能存在未知受试者，系统应能正确识别这些未知个体。点云非常适合资源受限的边缘计算应用，但与更常见的微多普勒特征等其他表示形式相比，点云受噪声和随机波动的影响更为显著。这是首个利用稀疏点云数据解决开放集步态识别问题的工作。为此，我们提出了一种新颖的神经网络架构，该架构将有监督分类与点云的无监督重建相结合，从而构建一个鲁棒、丰富且高度正则化的步态特征隐空间。为了在推理时检测未知受试者，我们引入了一种概率新颖性检测算法，该算法利用结构化隐空间，并在推理速度与预测精度之间提供了可调节的权衡。随本文一同，我们发布了mmGait10，这是一个原创的人体步态数据集，包含来自十名受试者在多种行走方式下超过五小时的测量数据。大量实验结果表明，在多个开放度水平下，我们的解决方案平均比针对点云适配的最先进方法在F1分数上提升了24%。
英文摘要：
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 adapted for point clouds, on average, and across multiple openness levels.
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