Title: Research on leg posture recognition of sprinters based on SVM classifier

Authors: Yang He

Addresses: College of Physical Education, Hunan City University, Yiyang, Hunan, 413000, China

Abstract: 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.

Keywords: support vector machine; SVM classifier; sprint; wavelet transform; feature vector; leg pose recognition; feature extraction.

DOI: 10.1504/IJBM.2022.124680

International Journal of Biometrics, 2022 Vol.14 No.3/4, pp.367 - 382

Received: 09 Oct 2020
Accepted: 15 Dec 2020

Published online: 05 Aug 2022 *

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