Online position recognition and correction method for sports athletes

In swimming, the correct recognition and correction of the wrong posture of the swimmer can improve the training quality of the athletes on weekdays. When the posture recognition is corrected, the affine deformation of the human body is easy to occur in the course of swimming. The traditional method is to extract these feature points and compare them with the correct posture to realize the recognition and correction of the posture. Due to the failure to detect and correct the wrong posture of athletes in real time, a method of position recognition and correction for swimmers with depth image bone tracking is proposed, and the threshold method is used to preprocess the image. The kalian filter is used to filter the collected image, Ago Si distribution function is used to obtain the action feature points from the filtered image, and the marginal point and the action feature point with low brightness are screened out by surf method. The Euclidean distance method is used to determine the distance between two adjacent feature points, and the feedback monitoring principle is used to identify and correct the wrong posture. The simulation results show that the improved pose recognition and correction method for skeletal tracking in depth image is improved. It can track and monitor the movement of athletes and complete the detection and recognition of swimming posture with high accuracy and strong stability.