Analysis Technology of Tennis Sports Match Based on Data Mining and Image Feature Retrieval

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.

Trajectory Method for Defense Human Motion Posture Based on Nano-Sensor

“…For the method in OATPP [6], the minimum recognition accuracy is 75%, and DMMSD [7] is 73%, while ODMM [8] method is 71%. The recognition accuracy of the proposed method is 17% higher than that the method in ATTS [4], 13% higher than DMPL [5], 22% higher than OATPP [6], 24% higher than DMMSD [7], and 26% higher than ODMM [8]. These results demonstrate that the defensive posture recognition accuracy of the proposed method is higher, indicating favorable practical application outcomes.…”

“…The minimum recognition accuracy of the proposed method is 97%. The method in ATTS [4] has a minimum recognition accuracy of 80%, while the method in DMPL [5] achieved a minimum recognition accuracy of 84%. For the method in OATPP [6], the minimum recognition accuracy is 75%, and DMMSD [7] is 73%, while ODMM [8] method is 71%.…”

“…As seen in Figure 5, the proposed method only has confusion in the trial step and the number of groups is one, which shows a small statistical bias, while other classification data such as pose are consistent with the original data. While the methods of ATTS [4] and DMPL [5], as well as the method of OATPP [6], accomplished accurate classification only in the jump step, without the mixing of other poses, all other poses showed classification bias. In contrast, the methods of DMMSD [7] and ODMM [8] showed classification errors and a high number of misclassifications in the backward step and side sliding steps and in the trial step, and did not complete the full number in the remaining defense posture classification.…”

“…For the important research topic of motion trajectory data mining, Huang H, Deng R [4] proposed a motion trajectory data mining algorithm based on image recognition, combining data mining techniques to obtain effective data from massive video and image data, using mathematical statistics and data mining techniques for data processing, and scientific analysis of motion race technology with the support of ergonomics. Based on machine learning algorithms, image analysis and image feature recognition processing are combined with image recognition technology to realize real-time trajectory of athletes' dynamic features, to achieve deep data mining.…”

The Biomechanical Analysis on the Tennis Batting Angle Selection Under Deep Learning

“…It is often difficult to obtain scientific and effective tennis technical training strategies through some game videos. To improve the performance of technical analysis of tennis competition, Huang and Deng (2020) [11] combined image analysis with image recognition technology based on ML algorithm and used image recognition technology to extract the motion features of athletes to track their dynamic features and record their technical features in real-time. Yang and Zhang (2021) [12] revealed that the tennis research force, as one of the main driving forces to promote the development of tennis in China, was an important means to guide the scientific development of tennis to understand the development characteristics and research trend of tennis teaching mode in China and could provide the theoretical basis for follow-up research and practice.…”