Exploration of Applying Pose Estimation Techniques in Table Tennis
-
Published:2023-02-01
Issue:3
Volume:13
Page:1896
-
ISSN:2076-3417
-
Container-title:Applied Sciences
-
language:en
-
Short-container-title:Applied Sciences
Author:
Wu Chih-Hung1ORCID, Wu Te-Cheng2, Lin Wen-Bin3ORCID
Affiliation:
1. Department of Digital Content and Technology, National Taichung University of Education, Taichung 403, Taiwan 2. Physical Education Office, National Tsing Hua University, Hsinchu 300, Taiwan 3. Physical Education Center, Taipei National University of the Arts, Taipei City 112, Taiwan
Abstract
The newly developed computer vision pose estimation technique in artificial intelligence (AI) is an emerging technology with potential advantages, such as high efficiency and contactless detection, for improving competitive advantage in the sports industry. The related literature is currently lacking an integrated and comprehensive discussion about the applications and limitations of using the pose estimation technique. The purpose of this study was to apply AI pose estimation techniques, and to discuss the concepts, possible applications, and limitations of these techniques in table tennis. This study implemented the OpenPose pose algorithm in a real-world video of a table tennis game. The research results show that the pose estimation algorithm performs well in estimating table tennis players’ poses from the video in a graphics processing unit (GPU)-accelerated environment. This study proposes an innovative two-stage AI pose estimation method for effectively addressing the current difficulties in applying AI to table tennis players’ pose estimation. Finally, this study provides several recommendations, benefits, and various perspectives (training vs. tactics) of table tennis and pose estimation limitations for the sports industry.
Funder
Ministry of Science and Technology Taiwan
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference28 articles.
1. Yu, C., Huang, T.-Y., and Ma, H.-P. (2022). Motion Analysis of Football Kick Based on an IMU Sensor. Sensors, 22. 2. (2022, October 01). The Ministry of Science and Technology of Taiwan, Available online: https://www.nstc.gov.tw/folksonomy/detail/177379c3-0061-43bb-ab33-c966df9edc73?l=ch. 3. Montella, R., Ciaramella, A., Fortino, G., Guerrieri, A., and Liotta, A. (2019). Internet and Distributed Computing Systems, Springer International Publishing. 4. Balan, A.O., Sigal, L., Black, M.J., Davis, J.E., and Haussecker, H.W. (2007, January 17–22). Detailed human shape and pose from images. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA. 5. Bregler, C., and Malik, J. (1998, January 23–25). Tracking people with twists and exponential maps. Proceedings of the 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231), Santa Barbara, CA, USA.
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|