Abstract
Tennis game technical analysis is affected by factors such as complex background and on-site noise, which will lead to certain deviations in the results, and it is difficult to obtain scientific and effective tennis technical training strategies through a few game videos. In order to improve the performance of tennis game technical analysis, based on machine learning algorithms, this paper combines image analysis to identify athletes’ movement characteristics and image feature recognition processing with image recognition technology, realizes real-time tracking of athletes’ dynamic characteristics, and records technical characteristics. Moreover, this paper combines data mining technology to obtain effective data from massive video and image data, uses mathematical statistics and data mining technology for data processing, and scientifically analyzes tennis game technology with the support of ergonomics. In addition, this paper designs a controlled experiment to verify the technical analysis effect of the tennis match and the performance of the model itself. The research results show that the model constructed in this paper has certain practical effects and can be applied to actual competitions.
Highlights
Compared with traditional, technical, and tactical analysis methods, data mining technology can more clearly describe and analyze the process and sequence of each hitting technique, such as the position and route of the shot, and the process and sequence of winning and losing points
In the Markov decision process, our goal is to find an optimal strategy π∗ with the largest cumulative reward Rπ: π∗
It is foreseeable that machine learning models will become part of making rational decisions in large-scale AI systems. ese modes will be crucial to the support of the action. is research uses SIMI Motion human motion video processing software to display the changing form of each action during the serve in a digital way
Summary
Tennis game technical analysis is affected by factors such as complex background and on-site noise, which will lead to certain deviations in the results, and it is difficult to obtain scientific and effective tennis technical training strategies through a few game videos. This paper combines data mining technology to obtain effective data from massive video and image data, uses mathematical statistics and data mining technology for data processing, and scientifically analyzes tennis game technology with the support of ergonomics. This paper designs a controlled experiment to verify the technical analysis effect of the tennis match and the performance of the model itself. E research results show that the model constructed in this paper has certain practical effects and can be applied to actual competitions
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
