基于聚类的 DGS 微多普勒特征提取用于自动动态手势识别。

Clustering-Driven DGS-Based Micro-Doppler Feature Extraction for Automatic Dynamic Hand Gesture Recognition.

机构信息

Beijing Key Laboratory of Millimeter Wave and Terahertz Technology, Beijing Institute of Technology, Beijing 100081, China.

Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China.

出版信息

Sensors (Basel). 2022 Nov 5;22(21):8535. doi: 10.3390/s22218535.

DOI:10.3390/s22218535

PMID:36366232

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9657879/

Abstract

We propose in this work a dynamic group sparsity (DGS) based time-frequency feature extraction method for dynamic hand gesture recognition (HGR) using millimeter-wave radar sensors. Micro-Doppler signatures of hand gestures show both sparse and structured characteristics in time-frequency domain, but previous study only focus on sparsity. We firstly introduce the structured prior when modeling the micro-Doppler signatures in this work to further enhance the features of hand gestures. The time-frequency distributions of dynamic hand gestures are first modeled using a dynamic group sparse model. A DGS-Subspace Pursuit (DGS-SP) algorithm is then utilized to extract the corresponding features. Finally, the support vector machine (SVM) classifier is employed to realize the dynamic HGR based on the extracted group sparse micro-Doppler features. The experiment shows that the proposed method achieved 3.3% recognition accuracy improvement over the sparsity-based method and has a better recognition accuracy than CNN based method in small dataset.

摘要

我们提出了一种基于动态分组稀疏（DGS）的时频特征提取方法，用于使用毫米波雷达传感器进行动态手势识别（HGR）。手势的微多普勒特征在时频域中表现出稀疏和结构化的特征，但以前的研究仅关注稀疏性。我们首先在本工作中引入结构先验来对手势的微多普勒特征进行建模，以进一步增强手势的特征。首先，使用动态分组稀疏模型对手势的时频分布进行建模。然后，利用 DGS-Subspace Pursuit（DGS-SP）算法提取相应的特征。最后，基于提取的分组稀疏微多普勒特征，使用支持向量机（SVM）分类器实现动态 HGR。实验表明，与基于稀疏性的方法相比，所提出的方法的识别精度提高了 3.3%，并且在小数据集下的识别精度优于基于 CNN 的方法。