Affiliations: [a] Department of Physical Education, Tangshan Normal University, TangShan, China
| [b] Department of Physical Education, Hengshui University, Hengshui, China
Correspondence:
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Corresponding author. Ningning Li, Department of Physical Education, Hengshui University, Hengshui, China. E-mail: [email protected].
Abstract: In the past decade, people’s life is getting better and better, and the attention to sports competition is also increasing. In the current information age, sports and athletes’ data are very important, especially team football. In college, football coaches can use the data to analyze the situation of college football players and opposing players to better specify the corresponding tactics to win the game. However, at present, most of the data results need to be manually recorded and counted on the spot or after the game. In the process of statistics, Zhou Jing will inevitably have omissions and other problems. For this problem, a method based on space-time graph convolution. In the process, machine vision and motion recognition methods are combined, and the joint movements of different football players are extracted through the pose estimation method to obtain motion recognition results. To ented the methods on the KTH dataset. The results showed that the football motion recognition using the research method reached 98% on the dataset, which significantly improved the accuracy of nearly 5% over the existing state-of-the-art methods. At the same time, the accuracy rate of football movements was less than 5%. This means that the research method can effectively identify football sports, and can be widely used in other fields, and promote the development of human movement recognition in human-computer interaction and smart city and other fields.
Keywords: Space-time graph convolution, football teaching, motion recognition
