Research on leg posture recognition of sprinters based on SVM classifier

作     者：He, Yang 

作者机构：Hunan City Univ Coll Phys Educ Yiyang 413000 Hunan Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF BIOMETRICS》 (国际生物识别技术杂志)

年 卷 期：2022年第14卷第3-4期

页      面：367-382页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 09[农学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by National Research Foundation of Korea (NRF) grant funded by the Ministry of Education (2018R1A6A1A03025242) and the Ministry of Science and ICT (MIST) (2020R1A2C3007007) 

主　　题：support vector machine SVM classifier sprint wavelet transform feature vector leg pose recognition feature extraction 

摘      要：In order to overcome the problems of low recognition rate, high time consumption and high misclassification rate caused by the difficulty in obtaining the global motion pattern information of sprinters in traditional posture recognition methods, a leg pose recognition method based on SVM classifier is proposed. Using multivariate statistical model to denoise the sprint image, the effective leg movement pattern information of sprinters is extracted. In the SVM classifier, the samples are divided by decision function to realise the recognition of sprinters leg posture. In order to verify the effectiveness of the method in this paper, a comparative experiment is designed. Experimental results show that the recognition rate of the proposed method is more than 90%, the time consumption of recognition process is always less than 0.5 s and the misclassification rate of leg pose features is always below 5%, which fully demonstrates the high recognition performance of the method.