Image detection and basketball training performance simulation based on improved machine learning

被引:11
|
作者
Wang Pengyu [1 ]
Gao Wanna [2 ]
机构
[1] Shanghai Univ Sport, Sch Psychol, Shanghai, Peoples R China
[2] Shenyang Sport Univ, Shenyang, Liaoning, Peoples R China
关键词
Machine learning; basketball; simulation model; basketball player; SPORTS; EDUCATION;
D O I
10.3233/JIFS-189243
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Basketball player detection technology is an important subject in the field of computer vision and the basis of related image processing research. This study uses machine learning technology to build a basketball sport feature recognition model. Moreover, this research mainly takes the characteristic information of basketball in the state of basketball goals as the starting point and compares and analyzes the detection methods by detecting the targets in the environment. By comprehensively considering the advantages and disadvantages of various methods, a method suitable for the subject is proposed, namely, a fast skeleton extraction and model segmentation method. The fitting effect of this method, whether in terms of compactness or quantity, has greater advantages than traditional bounding boxes, and realizes the construction of dynamic ellipsoidal bounding boxes in a moving state. In addition, this study designs a controlled trial to verify the analysis of this research model. The research results show that the model proposed in this paper has certain effects and can improve practical guidance for competitions and basketball players training.
引用
收藏
页码:2493 / 2504
页数:12
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