A novel SURF-RANSAC matching method for athletics posture recognition

Abstract

In athletics sports, accurate identification and correction of athlete's wrong posture can improve the quality of athlete's daily training. In the course of athletics sports, affine deformation of human body is easy to occur, which leads to the appearance of action feature points with low brightness and shading. However, the traditional method is to extract these feature points and compare them with the correct posture to realize the recognition and correction of posture, which leads to the failure of real-time detection and correction of athletes' wrong posture. Therefore, this paper proposes a method of posture recognition and correction for athletes with depth image bone tracking. The threshold method is used to preprocess the image, and the Kalman filter is used to filter the acquired image. The motion feature points are obtained from the filtered image by Gaussian distribution function. By improving SURF-RANSAC method, marginal points and action feature points with low brightness are screened out. Euclidean distance method is used to determine the distance between two adjacent feature points, and feedback monitoring principle is used to identify and correct the wrong posture. The simulation results show that the improved posture recognition and correction method of depth image bone tracking can realize tracking and monitoring of track and field athletes' movements, complete the detection and recognition of track and field sports posture with high accuracy and strong stability.