Research on Badminton Movement Machine Learning Model Based on Computer Vision Technology

This paper aims to explore an innovative method combining computer vision and machine learning to accurately identify and analyze various movements in badminton. This paper first summarizes the application prospect of computer vision in the field of sports analysis, and introduces its specific application scenarios in badminton in detail. By constructing a complete technical framework of image preprocessing module, feature extraction algorithm and deep learning model, the complex movements of badminton players such as swing, stroke and moving pace are captured and analyzed. In the research process, we used multi-view image fusion and key point detection technology to accurately extract action features in badminton, combined with convolutional neural network (CNN), recurrent neural network (RNN), long term memory network (LSTM) and other deep learning models to efficiently learn and model these features. Thus, the automatic classification and recognition of badminton movement can be realized. The experimental results show that the model has significant accuracy in badminton action recognition, good generalization ability and practicability, and can be effectively applied in the badminton teaching and training process of athlete performance evaluation, competition data analysis and other aspects. This research result not only expands the practical application of computer vision technology in the field of badminton, but also provides new ideas and tools for further promoting the development of sports intelligence and digitalization.