Machine learning model for feature recognition of sports competition based on improved TLD algorithm

被引：5

作者：

Ding, Qinglong ^{[1
]}

Ding, Zhenfeng ^{[2
]}

机构：

[1] Anshan Normal Univ, Dept Publ Phys Educ, Anshan, Liaoning, Peoples R China

[2] Nanjing Univ Finance & Econ, Dept Phys Educ, Nanjing, Jiangsu, Peoples R China

来源：

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2021年 / 40卷 / 02期

关键词：

TLD algorithm; improved algorithm; machine learning; competition features; feature recognition; SIMULATION; EDUCATION;

D O I：

10.3233/JIFS-189312

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Sports competition characteristics play an important role in judging the fairness of the game and improving the skills of the athletes. At present, the feature recognition of sports competition is affected by the environmental background, which causes problems in feature recognition. In order to improve the effect of feature recognition of sports competition, this study improves the TLD algorithm, and uses machine learning to build a feature recognition model of sports competition based on the improved TLD algorithm. Moreover, this study applies the TLD algorithm to the long-term pedestrian tracking of PTZ cameras. In view of the shortcomings of the TLD algorithm, this study improves the TLD algorithm. In addition, the improved TLD algorithm is experimentally analyzed on a standard data set, and the improved TLD algorithm is experimentally verified. Finally, the experimental results are visually represented by mathematical statistics methods. The research shows that the method proposed by this paper has certain effects.

引用

页码：2697 / 2708

页数：12

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