Human Behavior Recognition Based on Multi-Dimensional Feature Learning of Millimeter-Wave Radar

Abstract

In this paper, a multi-dimensional feature learning method is proposed to improve the classification accuracy and generalization performance of human behavior recognition on millimeter-wave radar. First, through the process of the radar echo reflected by the human body, we get the spectrums of range-Doppler, Doppler, the azimuth angle and the elevation angle. Then, a fixed frame-length sliding window method is used to capture 6 single-channel image features and 6 three-channel image features that can effectively represent human behaviors. Finally, a lightweight convolutional neural network (CNN) is used to learn and classify multidimensional behavior features. In order to evaluate the effectiveness of the proposed method, a dataset of six classes of human behaviors are collected by 3 people at multiple positions. The experimental results show that, compared with other features, the combined feature of range-time map (RTM), Doppler-time map (DTM) and azimuth-elevation-time map (AETM) has best classification performance for 6 human behaviors. In addition, the data of person A is used to train for a classification model, and the model is used to classify the behavior of people B and C, respectively. The recognition accuracy rates for untrained people B and C are 91.7% and 86.7%, respectively.