Millimeter-Wave Array Radar-Based Human Gait Recognition Using Multi-Channel Three-Dimensional Convolutional Neural Network
At present, there are two obvious problems in radar-based gait recognition. First, the traditional radar frequency band is difficult to meet the requirements of fine identification with due to its low carrier frequency and limited micro-Doppler resolution. Another significant problem is that radar s...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-09-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/19/5466 |
| _version_ | 1797552843030265856 |
|---|---|
| author | Xinrui Jiang Ye Zhang Qi Yang Bin Deng Hongqiang Wang |
| author_facet | Xinrui Jiang Ye Zhang Qi Yang Bin Deng Hongqiang Wang |
| author_sort | Xinrui Jiang |
| collection | DOAJ |
| description | At present, there are two obvious problems in radar-based gait recognition. First, the traditional radar frequency band is difficult to meet the requirements of fine identification with due to its low carrier frequency and limited micro-Doppler resolution. Another significant problem is that radar signal processing is relatively complex, and the existing signal processing algorithms are poor in real-time usability, robustness and universality. This paper focuses on the two basic problems of human gait detection with radar and proposes a human gait classification and recognition method based on millimeter-wave array radar. Based on deep-learning technology, a multi-channel three-dimensional convolution neural network is proposed on the basis of improving the residual network, which completes the classification and recognition of human gait through the hierarchical extraction and fusion of multi-dimensional features. Taking the three-dimensional coordinates, motion speed and intensity of strong scattering points in the process of target motion as network inputs, multi-channel convolution is used to extract motion features, and the classification and recognition of typical daily actions are completed. The experimental results show that we have more than 92.5% recognition accuracy for common gait categories such as jogging and normal walking. |
| first_indexed | 2024-03-10T16:06:45Z |
| format | Article |
| id | doaj.art-882d623d520647e58cc1825f8ed4445d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T16:06:45Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-882d623d520647e58cc1825f8ed4445d2023-11-20T14:50:54ZengMDPI AGSensors1424-82202020-09-012019546610.3390/s20195466Millimeter-Wave Array Radar-Based Human Gait Recognition Using Multi-Channel Three-Dimensional Convolutional Neural NetworkXinrui Jiang0Ye Zhang1Qi Yang2Bin Deng3Hongqiang Wang4College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaAt present, there are two obvious problems in radar-based gait recognition. First, the traditional radar frequency band is difficult to meet the requirements of fine identification with due to its low carrier frequency and limited micro-Doppler resolution. Another significant problem is that radar signal processing is relatively complex, and the existing signal processing algorithms are poor in real-time usability, robustness and universality. This paper focuses on the two basic problems of human gait detection with radar and proposes a human gait classification and recognition method based on millimeter-wave array radar. Based on deep-learning technology, a multi-channel three-dimensional convolution neural network is proposed on the basis of improving the residual network, which completes the classification and recognition of human gait through the hierarchical extraction and fusion of multi-dimensional features. Taking the three-dimensional coordinates, motion speed and intensity of strong scattering points in the process of target motion as network inputs, multi-channel convolution is used to extract motion features, and the classification and recognition of typical daily actions are completed. The experimental results show that we have more than 92.5% recognition accuracy for common gait categories such as jogging and normal walking.https://www.mdpi.com/1424-8220/20/19/5466human gait recognitionmillimeter-wave array radarmulti-channel three-dimensional convolution neural networkfeature fusion |
| spellingShingle | Xinrui Jiang Ye Zhang Qi Yang Bin Deng Hongqiang Wang Millimeter-Wave Array Radar-Based Human Gait Recognition Using Multi-Channel Three-Dimensional Convolutional Neural Network Sensors human gait recognition millimeter-wave array radar multi-channel three-dimensional convolution neural network feature fusion |
| title | Millimeter-Wave Array Radar-Based Human Gait Recognition Using Multi-Channel Three-Dimensional Convolutional Neural Network |
| title_full | Millimeter-Wave Array Radar-Based Human Gait Recognition Using Multi-Channel Three-Dimensional Convolutional Neural Network |
| title_fullStr | Millimeter-Wave Array Radar-Based Human Gait Recognition Using Multi-Channel Three-Dimensional Convolutional Neural Network |
| title_full_unstemmed | Millimeter-Wave Array Radar-Based Human Gait Recognition Using Multi-Channel Three-Dimensional Convolutional Neural Network |
| title_short | Millimeter-Wave Array Radar-Based Human Gait Recognition Using Multi-Channel Three-Dimensional Convolutional Neural Network |
| title_sort | millimeter wave array radar based human gait recognition using multi channel three dimensional convolutional neural network |
| topic | human gait recognition millimeter-wave array radar multi-channel three-dimensional convolution neural network feature fusion |
| url | https://www.mdpi.com/1424-8220/20/19/5466 |
| work_keys_str_mv | AT xinruijiang millimeterwavearrayradarbasedhumangaitrecognitionusingmultichannelthreedimensionalconvolutionalneuralnetwork AT yezhang millimeterwavearrayradarbasedhumangaitrecognitionusingmultichannelthreedimensionalconvolutionalneuralnetwork AT qiyang millimeterwavearrayradarbasedhumangaitrecognitionusingmultichannelthreedimensionalconvolutionalneuralnetwork AT bindeng millimeterwavearrayradarbasedhumangaitrecognitionusingmultichannelthreedimensionalconvolutionalneuralnetwork AT hongqiangwang millimeterwavearrayradarbasedhumangaitrecognitionusingmultichannelthreedimensionalconvolutionalneuralnetwork |