Sports athletes' action identification based on dynamic Bayesian network
by Jinrong Li; Hui Wang; Luojing Wang
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 14, No. 4, 2022

Abstract: In order to overcome the problems of poor accuracy, recall rate and poor real-time performance of traditional methods, the sports athletes' action identification method based on dynamic Bayesian network was proposed. Firstly, the shape vector is used to quantify the motion features of athletes, and the motion shape feature vector is projected, so that all vectors are in the same coordinate system, and then the feature data is preprocessed. Secondly, on the basis of feature data preprocessing, a phase space is constructed to extract athletes' action features. Finally, dynamic Bayesian network is used to identify athletes' action based on feature extraction results. Experimental results show that the accuracy of the proposed method is above 95%, and the recall rate is stable at about 9%, and all action identification can be completed in five minutes.

Online publication date: Mon, 31-Oct-2022

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