Abstract
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.
Highlights
Pedestrian retrieval and identification has always been an urgent need in the fields of anti-terrorism security checks, crime investigation, medical inference, etc
Based on the above analysis, this paper proposes a human gait recognition technology based on MMW array radar around the two basic problems of human gait detection by radar
Taking l as the independent variable, the Fourier Transformation (FFT) for different pulses is equivalent to Fourier analysis for the phase components of the above signals, and the phase information of the signals can be obtained, which includes the speed of the target
Summary
Pedestrian retrieval and identification has always been an urgent need in the fields of anti-terrorism security checks, crime investigation, medical inference, etc. The study found that by extracting Doppler signals, a slight difference in step size between the two legs can be detected, allowing the judgement of gait asymmetry and diagnosing whether the target human body has dyskinesia [7] These related studies prove the feasibility of gait recognition using the micro-Doppler characteristics of radar signals. Most gait recognition methods that combine radar sensors with DL mainly use convolution neural networks (CNN) to extract and recognize features of micro-Doppler signatures [6,7,31]. The supervised learning framework based on the three-dimensional convolution neural network (3-D CNN) was used, and the obtained optical pictures and radar echo data were used for training As a result, they realized the posture recognition of the human body in real time and effectively avoided the problem of recognition-rate decline caused by obstacle occlusion by using radar sensors.
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