Ultra wide band radar gait recognition based on slow-time segmentation

doi:10.3785/j.issn.1008-973X.2020.02.009

Journal of ZheJiang University (Engineering Science)

2020, Vol. 54

Issue (2): 283-290 DOI: 10.3785/j.issn.1008-973X.2020.02.009

Computer Technology, Information Engineering

Ultra wide band radar gait recognition based on slow-time segmentation

Jin-hai ZHOU1(

),Yi-chuan WANG1,Jing-ping TONG1,Shi-yi ZHOU1,Xiang-fei WU2

1. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
2. Hangzhou Magnet Intelligent Technology Co. Ltd, Hangzhou 310000, China

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Abstract

An algorithm for free space gait recognition using ultra wide band (UWB) radar was proposed in order to solve the problems of privacy leakage caused by cameras and intrusiveness caused by wearable devices in the indoor monitoring, and to relax the restriction on walking conditions in traditional radar gait recognition algorithms. The radar gait signal reflected from the walking target is segmented along the slow-time axis to generate a series of sub-signals. For each sub-signal, the Fourier transform is performed on range bins to obtain a range-Doppler map. These range-Doppler maps temporally correlate each other. The features of a set of range-Doppler maps belonging to the same gait signal are extracted using the histogram of oriented gradient algorithm, and the resulting features are modeled by long short-term memory networks to obtain the final classification result of the target identity. The experiment was carried out in an empty indoor environment, and the accuracy of gait classification for four individuals was 79.10%. Results show that the proposed algorithm has a certain discrimination ability to different individual’s gait in free space.

Key words： ultra wide band (UWB) radar free space slow-time segmentation histogram of oriented gradient long short-term memory network

Received: 20 July 2019 Published: 10 March 2020

CLC:

TP 391.4

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Cite this article:

Jin-hai ZHOU,Yi-chuan WANG,Jing-ping TONG,Shi-yi ZHOU,Xiang-fei WU. Ultra wide band radar gait recognition based on slow-time segmentation. Journal of ZheJiang University (Engineering Science), 2020, 54(2): 283-290.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2020.02.009 OR http://www.zjujournals.com/eng/Y2020/V54/I2/283

基于慢时间分割的超宽带雷达步态识别

为了解决室内监控时摄像头的隐私泄露问题和可穿戴设备的侵入性等问题，同时针对传统雷达步态识别算法中的行走条件限制问题，提出基于超宽带（UWB）雷达的自由空间步态识别算法.算法沿慢时间轴对目标行走动作产生的雷达步态信号进行分割产生一系列子信号；对于每个子信号，在距离单元上分别进行傅里叶变换得到距离-多普勒图像，这些距离-多普勒图像前后之间存在时序关系；利用方向梯度直方图算法对属于同一个步态信号的一组距离-多普勒图像进行特征提取，采用长短期记忆网络对得到的特征进行时序建模以获取目标身份分类结果. 实验在空旷的室内环境中进行，对四人的步态分类准确率为79.10%. 结果表明所提出的算法对自由空间中不同个体的步态具有一定的区分能力.

关键词： 超宽带UWB雷达, 自由空间, 慢时间, 分割, 方向梯度直方图, 长短期记忆网络

Fig.1 Time-Doppler map for free walking with radar frequency band of 6.5~8.0 GHz

Fig.2 Slow-time segmentation for gait signal

Fig.3 Range-Doppler maps for classical gait signal

Fig.4 Algorithm block diagram for gait recognition algorithm based on slow-time segmentation

Fig.5 HOG feature extraction for RDM

Fig.6 Information flow relationship of LSTM unit

Fig.7 Environment configuration of data collection experiment

Tab.1 Physical characteristics of experimental targets

Tab.2 Parameter comparison for HOG and LSTM models

Tab.3 Confusion matrix of model on testing set

Fig.8 Classification accuracy and loss for training and testing sets


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