Research on static image recognition of sports based on machine learning.

At present, artificial intelligence for sports static image recognition is mostly in the action judgment stage, but less analysis on the action detail stage. Based on this, based on machine learning, this study uses static images and video sequences as carriers to improve traditional algorithm research and to perform motion gesture recognition. Through performance analysis, this paper explores the traditional algorithm and uses parameter analysis to improve the feature extraction and classification of traditional algorithms. Moreover, this paper uses the multi-scale feature approximation calculation method to improve the speed of the algorithm to extract features, and the algorithm is tested using the UCF motion data set and the self-created motion data set. In addition, this paper obtains representative motion video through data collection to test the effectiveness of the proposed algorithm. The research shows that the proposed algorithm has good performance and can provide theoretical reference for subsequent related research. [ABSTRACT FROM AUTHOR]