Table Tennis Teaching 3D Model Based on Hierarchical Clustering Intelligent Algorithm

Table tennis is an important branch of college sports courses, but the contradiction between the uneven basic skills of students and the overly monotonous teaching mode makes it difficult for current teaching to achieve good results. By using the hierarchical clustering intelligent algorithm, we can effectively achieve targeted guidance based on individual differences of different learners, thus stimulating their potential, greatly improving the atmosphere of college table tennis classes, and achieving the best educational effect. This paper analyzes the video sequence of table tennis sports teaching 3D model obtained in the same scene and proposes an algorithm for extracting athlete objects based on hierarchical clustering intelligence. Then, by using the intersection clustering method of two types of frame difference results and extracting and processing the difference images with median filtering and morphological methods, the moving foreground objects are obtained. Experimental results show that the algorithm has a small amount of calculation, low complexity, high real-time performance, and can achieve good segmentation results.