Simulation of sports action recognition based on maximum spanning tree algorithm

Abstract

Aiming at the situation that the motion recognition of sports athletes is interfered by a variety of factors and the recognition results are not ideal, this paper uses the maximum spanning tree algorithm as the model basis to use machine learning ideas to construct a sports player motion recognition model based on the maximum spanning tree algorithm. Moreover, this article combines a region growing algorithm based on simple surface fitting and morphological reconstruction to initially segment sports actions. After that, this paper improves the prim algorithm, and combines an optimized watershed segmentation framework to construct a new energy function using the T-prim minimum spanning tree algorithm proposed in this paper. The constructed T-prim tree is combined with this optimized watershed segmentation framework to complete the segmentation of sports images.Finally, this paper designs experiments to verify the actual effect of the method proposed in this paper. It can be seen from the research results that the model constructed in this paper basically achieves the expected goal.