Beschreibung:
The estimation time and estimation precision of motion pose samples are problematic for the pose estimation algorithm of sports movements. This paper proposes a multifeature fusion-based algorithm for accurate posture estimation. The human rod model is constructed after analyzing the human pose estimation technology. Using the Kalman filter method, the degree of freedom and range of motion of the major joints of the human body were determined. The eight-star model was used to extract the sports posture features, and the weighted average method was used to process the grayscale images of sports. Using the multifeature fusion method, the extracted multisource feature vector information is thoroughly analyzed and processed, and a new group of fusion feature vectors is created. Using a mixture Gaussian distribution model, the posture estimation of an athlete’s body is accomplished. Experimental results indicate that when the amount of sports pose sample data is 900 GB, the accurate estimation time of the proposed method is 5.3 s, and its accuracy is 100 percent. Improve the estimation accuracy of samples of sports posture.