Radar-based solutions have attracted great attention in human activity recognition (HAR) for their advantages in accuracy, robustness, and privacy protection. The conventional approaches transform radar signals into feature maps and then directly process them as visual images. While effective, these image-based methods may not be the best solutions in terms of representation efficiency to encode the relevant information for classification. This article proposes a novel HAR method combining sparse theory and PointNet network, with both operations in the time-Doppler (TD) and range-Doppler (RD) domains. First, sparsity-based feature extraction is introduced to use a limited number of sparse solutions to characterize human activities in the form of TD sparse point clouds (TDSP) or dynamic RD sparse point clouds (DRDSP). This new representation is validated by comparing the reconstructed and original signals. Then, PointNet networks are adopted to summarize multidomain features and predict human activity labels by a sparse set of input point clouds. Comprehensive experiments were conducted to demonstrate that the proposed method can yield a higher representation efficiency, classification accuracy, and better generalization capability than existing ones.

中文翻译：

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使用便携式 FMCW 雷达通过 PointNet 进行基于稀疏性的人类活动识别


基于雷达的解决方案因其在准确性、鲁棒性和隐私保护方面的优势而在人类活动识别（HAR）领域引起了广泛关注。传统方法将雷达信号转换为特征图，然后直接将其处理为视觉图像。虽然有效，但这些基于图像的方法在编码相关信息进行分类的表示效率方面可能不是最佳解决方案。本文提出了一种结合稀疏理论和 PointNet 网络的新型 HAR 方法，同时在时间多普勒 (TD) 和距离多普勒 (RD) 域中进行操作。首先，引入基于稀疏性的特征提取，以TD稀疏点云（TDSP）或动态RD稀疏点云（DRDSP）的形式使用有限数量的稀疏解决方案来表征人类活动。通过比较重建信号和原始信号来验证这种新的表示形式。然后，采用 PointNet 网络来总结多域特征，并通过一组稀疏的输入点云来预测人类活动标签。综合实验表明，该方法比现有方法具有更高的表示效率、分类精度和更好的泛化能力。