Research on human combination continuous motion recognition method

The long time data which contains the complete behavioral action is usually analyzed as a whole in human motion recognition methods of traditional radars, extracting global behavioral features for classification. This method does not work well in cases where the behavior contains multiple combinations of consecutive action. Based on millimeter wave radar, a solution in terms of time-frequency analysis and target detection of combination continuous motions is proposed to improve the recognition effect. First, a millimeter wave radar data acquisition platform is constructed to pre-process the collected echo signals; the projection method is then used to analyze the human movement echoes in time and frequency, and the FT-SSIM(Feature Transformation-structural similarity) algorithm is used to enhance the data; finally, a combination of YOLOv5 (You Only Look Once v5) and DeepSORT(Deep Simple Online and Realtime Tracking) is used to identify and count multiple combinations of consecutive action. Experimental results show that the recognition architecture proposed in this paper has good recognition effect on combined continuous action and average recognition accuracy of 97.5% after 300 iterations.