Affiliations: [a] Department of Physical Education, Jiaozuo Teachers College, Jiaozuo, Henan, China
| [b] Sports Department, Henan University of Economics and Law, Zhengzhou, Henan, China
| [c] Department of Physical Education, Henan Polytechnic University, Jiaozuo, Henan, China
Correspondence:
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Corresponding author. Aizeng Li, Department of Physical Education, Henan Polytechnic University, Jiaozuo 454003, Henan, China. E-mail: [email protected].
Abstract: Action is the key to sports and the core factor of standardization, quantification, and comprehensive evaluation. However, in the actual competition training, the occurrence of sports activities is often fleeting, and it is difficult for human eyes to identify quickly and accurately. There are many existing quantitative analysis methods of sports movements, but because there are many complex factors in the actual scene, the effect is not ideal. How to improve the accuracy of the model is the key to current research, but also the core problem to be solved. To solve this problem, this paper puts forward an intelligent system of sports movement quantitative analysis based on deep learning method. The method in this paper is firstly to construct the fuzzy theory human body feature method, through which the influencing factors in the quantitative analysis of movement can be distinguished, and the effective classification can be carried out to eliminate irrelevantly and simplify the core elements. Through the method of human body characteristics based on fuzzy theory, an intelligent system of deep learning quantitative analysis is established, which optimizes the algorithm and combines many modern technologies including DBN architecture. Finally, the accuracy of the method is improved by sports action detection, figure contour extraction, DBN architecture setting, and normalized sports action recognition and quantification. To verify the effect of this model, this paper established a performance comparison experiment based on the traditional method and this method. The experimental results show that compared with the traditional three methods, the accuracy of the in-depth learning sports movement quantitative analysis method in this paper has greatly improved and its performance is better.
Keywords: Action recognition, sports movement, deep learning, action characteristics
