A method for identifying foul actions of athletes based on multimodal perception

In order to improve the recall rate and accuracy of foul action recognition for track and field athletes, and solve the problem of poor classification effect of foul action, this study proposed and designed a multi-modal perception-based foul action recognition method for track and field athletes. Firstly, the foul action dataset of track and field athletes is constructed. Then, the wavelet denoising method is used to process the movement image noise of track and field athletes. Finally, the recognition function of foul action of track and field athletes is established by means of multi-modal perception, and the bidirectional ranking loss is used to train the function and the similarity between skeleton and video matching is calculated, so as to obtain the final recognition result of foul action of track and field athletes. The experimental results show that the accuracy of foul action identification is 98.5%, the classification accuracy is 98.6%, the recognition recall rate is 99.2%, the recognition sensitivity is high, and the application effect is good.