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

Author

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

Abstract

This study developed two action recognition models using the YOLOv8-Alphapose two-stream spatial temporal graph convolutional networks (2s-STGCN), and the networks were used to recognize technical actions in table tennis. This study proposed a novel framework that merges dynamic and static complex network analysis with a community detection algorithm aimed at evaluating table tennis players' techniques, tactical patterns and styles. Two datasets that contain 8015 high-definition action videos of 37 elite players were constructed: a front-facing player technical action dataset (4154 videos) and a backwards-facing player technical action dataset (3861 videos). The results showed that YOLOv8-Alphapose-2s-STGCN achieved better recognition performance than seven other YOLOv8-Alphapose-based artificial intelligence algorithms (transformer, BiGRU, BiLSTM, GRU, LSTM, TCN and RNN algorithms) on both datasets and exhibited robust performance in practical applications. In the case study, multiple indicators were used to measure the importance of nodes (players' techniques) within the serving and receiving networks and within the two-round (winning and losing) networks. Dynamic complex network analysis was adopted to evaluate tactical styles and patterns. Furthermore, this study examined whether players and their opponents exhibit variability or similarity in their tactical patterns, focusing on the player networks and the two-round winning and losing networks. By integrating action recognition with process-focused match analysis, this study explored an innovative and comprehensive way to analyse matches, with implications for the performance analysis of table tennis players and players in related racket sports.

Suggested Citation

  • Song, Honglin & Li, Yutao & Fu, Chenyi & Xue, Feng & Zhao, Qiyue & Zheng, Xingyu & Jiang, Kunkun & Liu, Tianbiao, 2024. "Using complex networks and multiple artificial intelligence algorithms for table tennis match action recognition and technical-tactical analysis," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    • Handle: RePEc:eee:chsofr:v:178:y:2024:i:c:s0960077923012456
    DOI: 10.1016/j.chaos.2023.114343
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    References listed on IDEAS

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    1. Thorben Hülsdünker & Martin Ostermann & Andreas Mierau, 2019. "Standardised computer-based reaction tests predict the sport-specific visuomotor speed and performance of young elite table tennis athletes," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 19(6), pages 953-970, November.
    2. Masoud Latifinavid & Aydin Azizi, 2023. "Development of a Vision-Based Unmanned Ground Vehicle for Mapping and Tennis Ball Collection: A Fuzzy Logic Approach," Future Internet, MDPI, vol. 15(2), pages 1-19, February.
    3. Gómez, Miguel–Ángel & Rivas, Fernando & Leicht, Anthony S. & Buldú, Javier M., 2020. "Using network science to unveil badminton performance patterns," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    4. Zhou, Yunjing & Zong, Shouxin & Cao, Run & Gómez, Miguel-Ángel & Chen, Chuqi & Cui, Yixiong, 2023. "Using network science to analyze tennis stroke patterns," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    5. Norbert Schrapf & Shaimaa Alsaied & Markus Tilp, 2017. "Tactical interaction of offensive and defensive teams in team handball analysed by artificial neural networks," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 23(4), pages 363-371, July.
    6. Wu, Yao & Xia, Zeyu & Wu, Tian & Yi, Qing & Yu, Runyu & Wang, Jun, 2020. "Characteristics and optimization of core local network: Big data analysis of football matches," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    7. Pengfei Li & Tong Zhang & Yantao Jin, 2023. "A Spatio-Temporal Graph Convolutional Network for Air Quality Prediction," Sustainability, MDPI, vol. 15(9), pages 1-13, May.
    8. Steele, Fiona & Clarke, Paul & Kuha, Jouni, 2019. "Modeling within-household associations in household panel studies," LSE Research Online Documents on Economics 88162, London School of Economics and Political Science, LSE Library.
    9. Alessandro Chessa & Pierpaolo D’Urso & Livia Giovanni & Vincenzina Vitale & Alfonso Gebbia, 2023. "Complex networks for community detection of basketball players," Annals of Operations Research, Springer, vol. 325(1), pages 363-389, June.
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