Radar-Based Human Gait Recognition Using Dual-Channel Deep Convolutional Neural Network

作者：Xueru Bai, Hui Ye, Li Wang, Feng Zhou · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2019 · DOI：10.1109/tgrs.2019.2929096 · 被引用次数：92 · 研究领域：Advanced SAR Imaging Techniques、Gait Recognition and Analysis、Radar Systems and Signal Processing

This paper addresses the problem of radar-based human gait recognition based on the dual-channel deep convolutional neural network (DC-DCNN). To enrich the limited radar data set of human gaits and provide a benchmark for classifier training, evaluation, and comparison, it proposes an effective method for radar echo generation from the infrared, publicly accessible motion capture (MOCAP) data set. According to the different nonstationary characteristics of micro-Doppler (m-D) for the torso and limbs, it enhances their distinguishable joint time-frequency (JTF) features by applying the short-time Fourier transforms (SFTFs) with varying sliding window length and then designs the DC-DCNN structure to achieve refined human gait recognition by separate feature extraction and fusion. Experiments have shown that compared with the traditional single-channel deep convolutional neural network (SC-DCNN), the proposed method achieves higher recognition accuracy in refined human gait classification without incurring additional radar resources and could be readily extended to refined recognition of other human activities.