Millimeter Wave Radar Human Activity Recognition Algorithm Based on Feature Fusion

The human activity recognition method based on millimeter-wave radar captures the electromagnetic wave signals of human activities in non-contact way for recognition.It is not easily interfered by smoke and light,which has a certain degree of privacy protection,and has become a research hotspot at present.However,the existing algorithms have some problems,such as single feature input,complex model structure,and insufficient generalization ability verification.A human activity recognition algorithm with two stream feature fusion convolutional neural network is proposed,named 2S-FCNN,which uses the residual neural network equipped with attention mechanism as the backbone network,inputs the time-distance image and the time-velocity image in parallel,and uses the feature weighted score fusion method to fuse the features for classification and recognition,so as to achieve a high recognition accuracy.A set of in-depth comparative experiments are conducted with other existing algorithms on both public and self built datasets,and the experimental results show that the proposed algorithm has good performance in recognition rate and generalization ability.