Human Action Recognition Method for Millimeter Wave Radar Based on Dual-Stream CNN-BiLSTM
Most of current radar-based human action recognition methods first perform multidimensional fast Fourier transform(FFT)on the echo signal of human action to obtain information such as distance,Doppler and angle,construct various data spectrograms and then input them into a neural network for classification and recognition,which is a complicated data pre-processing process.A millimeter wave radar human action recognition method is proposed based on a dual-stream convolutional neural network(CNN)bridged with a bi-directional long short-term memory network(BiLSTM)(denoted as CNN-BiLSTM).The original complex radar echo signal(I/Q)is first frame-differenced to eliminate static interference and then converted to amplitude/phase(A/P)data format.Then the frame-differenced I/Q and A/P data are fed into the single-stream CNN-BiLSTM network respectively to extract spatial and temporal features of human actions,and finally the fusion of the dual-stream network is performed to enhance the feature interaction and improve the recognition ac-curacy.Experimental results show that the proposed method is simple in data pre-processing and makes full use of the inter-frame corre-lation of action data,and the model converges quickly and the recognition accuracy can reach 99%,demonstrating that the proposed method is a fast and effective human motion recognition method.
radar target recognitionhuman action recognitionConvolutional Neural Network(CNN)Bi-directional Long Short-Term Memory(BiLSTM)
万方数据
维普
