Research on leg posture recognition of sprinters based on SVM classifier
by Yang He
International Journal of Biometrics (IJBM), Vol. 14, No. 3/4, 2022

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.

Online publication date: Fri, 05-Aug-2022

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