Affiliations: Department of P.E., Hanshan Normal University, Chaozhou, Guangdong, China
Correspondence:
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Corresponding author. Xiaobing Du, Department of P.E., Hanshan Normal University, China. E-mail: duxbyx@126.com.
Abstract: Sports athletes not only exercise fast, but also suffer from the surrounding complex environment. Therefore, the video needs to be sequenced to improve processing efficiency. From the perspective of machine learning, this paper designs a spatial feature extractor based on CNN to extract time series features of sports. Moreover, this paper uses the support vector machine as the basis of the construction model to construct a feature extraction model based on support vector machine and random forest based on different situations. At the same time, this paper collects test data through the sports database and uses the swimming project as an example to analyze the model performance. Finally, the paper verifies the validity of the model by comparing and verifying methods. The research indicates that the proposed method has certain effectiveness and can provide theoretical reference for subsequent related research.
Keywords: Support vector machine, random forest, time series, feature extraction
