Attitude Target Tracking of Kabadi Athletes Based on Machine Learning

In order to improve the performance of Kabaddi athlete posture target tracking, a method of Kabaddi athlete posture target tracking based on machine learning is proposed. The threshold change parameter is calculated by using the obtained athletes’ posture characteristic parameters, and the golden section is introduced to transform it and smooth the features of athletes’ posture are extracted. Constraint loss is added to the local global supervision module of machine learning, and the local features of athlete pose are integrated, and the local parameters of athlete pose are obtained by loss function. Taylor formula was used to calculate the athletes’ pose velocity, and Kalman filter was used to evaluate the joint motion data, and Kabaddi athlete pose model was constructed. The frame difference of the background image is calculated by normalizing the athletes’ pose image, and the athletes’ pose is automatically tracked. The experimental results show that this method can track all nodes of the athletes’ posture, and has good performance in the absolute error, detail loss and tracking lag rate of the athletes’ posture tracking, so it is helpful to improve the athletes’ technical level and improve the training effect.