Using complex networks and multiple artificial intelligence algorithms for table tennis match action recognition and technical-tactical analysis-Reference-Cited by-同舟云学术

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Using complex networks and multiple artificial intelligence algorithms for table tennis match action recognition and technical-tactical analysis

Published:2024-01 Issue: Volume:178 Page:114343
ISSN:0960-0779
Container-title:Chaos, Solitons & Fractals
language:en
Short-container-title:Chaos, Solitons & Fractals

Author:

Song Honglin,Li Yutao,Fu Chenyi,Xue Feng,Zhao Qiyue,Zheng Xingyu,Jiang Kunkun,Liu Tianbiao

Funder

National Office for Philosophy and Social Sciences

National Social Science Fund Youth Project

Publisher

Elsevier BV

Subject

General Mathematics,General Physics and Astronomy,Statistical and Nonlinear Physics,Applied Mathematics

Reference98 articles.

1. Computer-assisted yoga training system;Chen;Multimed Tools Appl,2018

2. Estimating player positions from Padel high-angle videos: accuracy comparison of recent computer vision methods;Javadiha;Sensors,2021

3. Development of a vision-based unmanned ground vehicle for mapping and tennis ball collection: a fuzzy logic approach;Latifinavid;Future Internet,2023

4. Vision-based movement recognition reveals badminton player footwork using deep learning and binocular positioning;Luo;Heliyon,2022

5. Development, evaluation and application of a novel markerless motion analysis system to understand push-start technique in elite skeleton athletes;Needham;PLoS One,2021

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1. Analysis of attacking corner kick strategies in the Chinese Women’s Super League 2019;Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology;2024-08-06

2. Adaptive temporal aggregation for table tennis shot recognition;Neurocomputing;2024-06

3. Modified Coot bird optimization algorithm for solving community detection problem in social networks;Neural Computing and Applications;2024-02-07

4. Rhythms of Victory: Predicting Professional Tennis Matches Using Machine Learning;IEEE Access;2024

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