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He F, Li Y. Modeling of SPM-GRU ping-pong ball trajectory prediction incorporating YOLOv4-Tiny algorithm. PLoS One 2024; 19:e0306483. [PMID: 39240792 PMCID: PMC11379273 DOI: 10.1371/journal.pone.0306483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 06/17/2024] [Indexed: 09/08/2024] Open
Abstract
The research aims to lift the accuracy of table tennis trajectory prediction through advanced computer vision and deep learning techniques to achieve real-time and accurate table tennis ball position and motion trajectory tracking. The study concentrates on the innovative application of a micro-miniature fourth-generation real-time target detection algorithm with a gated loop unit to table tennis ball motion analysis by combining physical models and deep learning methods. The results show that in the comparison experiments, the improved micro-miniature fourth-generation real-time target detection algorithm outperforms the traditional target detection algorithm, with the loss value decreasing to 1.54. Its average accuracy in multi-target recognition is dramatically increased to 86.74%, which is 22.36% higher than the original model, and the ping-pong ball recognition experiments show that it has an excellent accuracy in various lighting conditions, especially in low light, with an average accuracy of 89.12%. Meanwhile, the improved model achieves a processing efficiency of 85 frames/s. In addition, compared with the traditional trajectory prediction model, the constructed model performs the best in table tennis ball trajectory prediction, with errors of 4.5 mm, 25.3 mm, and 35.58 mm. The results show that the research trajectory prediction model achieves significant results in accurately tracking table tennis ball positions and trajectories. It not only has practical application value for table tennis training and competition strategies, but also provides a useful reference for the similar techniques application in other sports.
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Affiliation(s)
- Fuxing He
- School of Physical Education, Qiongtai Normal University, Haikou, China
| | - Yongan Li
- School of Physical Education, Hainan Normal University, Haikou, China
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Zaidi Z, Martin D, Belles N, Zakharov V, Krishna A, Lee KM, Wagstaff P, Naik S, Sklar M, Choi S, Kakehi Y, Patil R, Mallemadugula D, Pesce F, Wilson P, Hom W, Diamond M, Zhao B, Moorman N, Paleja R, Chen L, Seraj E, Gombolay M. Athletic Mobile Manipulator System for Robotic Wheelchair Tennis. IEEE Robot Autom Lett 2023. [DOI: 10.1109/lra.2023.3249401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Affiliation(s)
| | | | | | | | | | - Kin Man Lee
- Georgia Institute of Technology, Atlanta, GA, USA
| | | | - Sumedh Naik
- Georgia Institute of Technology, Atlanta, GA, USA
| | | | - Sugju Choi
- Georgia Institute of Technology, Atlanta, GA, USA
| | | | | | | | | | - Peter Wilson
- Georgia Institute of Technology, Atlanta, GA, USA
| | - Wendell Hom
- Georgia Institute of Technology, Atlanta, GA, USA
| | | | - Bryan Zhao
- Georgia Institute of Technology, Atlanta, GA, USA
| | - Nina Moorman
- Georgia Institute of Technology, Atlanta, GA, USA
| | - Rohan Paleja
- Georgia Institute of Technology, Atlanta, GA, USA
| | - Letian Chen
- Georgia Institute of Technology, Atlanta, GA, USA
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Thamo B, Hanley D, Dhaliwal K, Khadem M. Data-Driven Steering of Concentric Tube Robots in Unknown Environments via Dynamic Mode Decomposition. IEEE Robot Autom Lett 2023. [DOI: 10.1109/lra.2022.3231490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Balint Thamo
- School of Informatics, University of Edinburgh, Edinburgh, U.K
| | - David Hanley
- School of Informatics, University of Edinburgh, Edinburgh, U.K
| | - Kevin Dhaliwal
- The Translational Healthcare Technologies Group in Centre for Inflammation Research, The Queen's Medical Research Institute, University of Edinburgh, Edinburgh, U.K
| | - Mohsen Khadem
- School of Informatics, University of Edinburgh, Edinburgh, U.K
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Gao Y, Tebbe J, Zell A. Optimal stroke learning with policy gradient approach for robotic table tennis. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04131-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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