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The analysis of tennis recognition model for human health based on computer vision and particle swarm optimization algorithm

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Abstract

The study aims to solve the problem of tennis picking for players in the training process and realize intelligent tennis picking. An intelligent tennis picking robot is studied to recognize and position tennis balls. First, the tennis recognition algorithm based on HSV (hue, saturation, value) color space is used to identify the tennis ball, and the coordinates of tennis and obstacles are obtained by background difference and OF (optical flow). Second, particle swarm optimization (PSO) that has excellent global planning ability and support vector machine (SVM) that has good obstacle avoidance performance are applicable because there may be some obstacles in tennis courts. Therefore, the traditional PSO and SVM are combined to obtain the optimized PSO. And the simulation comparison experiment is carried out on the Matlab simulation software. Finally, the model is tested and 50 random screenshots of tennis videos collected on the spot, and tennis photos downloaded on the network are tested in the dataset. The results show that the number of tennis balls correctly identified by the proposed algorithm is 248 and that of tennis balls wrongly identified is 8. Its recognition accuracy is 96.88% and the time spent is 9.33 s. The algorithm proposed provides some ideas to solve the problem of tennis picking for tennis players.

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References

  • Archana M, Ayyasamy A (2018) Tracking based event detection of singles broadcast tennis video. In: 2018 3rd International conference on communication and electronics systems (ICCES). IEEE, pp 835–838

  • Arunraj M, Srinivasan A, Juliet AV (2018) Online action recognition from RGB-D cameras based on reduced basis decomposition. J Real Time Image Process 17:1–16

    Google Scholar 

  • Bai N, Liu N (2021) Deep spatial/temporal-level feature engineering for tennis-based action recognition. Future Gener Comput Syst 125(6):29–31

    Google Scholar 

  • Bini AA (2019) Image restoration via DOST and total variation regularisation. IET Image Proc 13(3):458–468

    Article  Google Scholar 

  • Cai J, Tang X (2018) RGB video based tennis action recognition using a deep weighted long short-term memory. arXiv preprint arXiv:1808.00845

  • Chen C, Liu K, Kehtarnavaz N (2016) Real-time human action recognition based on depth motion maps. J Real Time Image Proc 12(1):155–163

    Article  Google Scholar 

  • Chen C, Shu Y, Shu KI et al (2018) WiTT: modeling and the evaluation of table tennis actions based on WIFI signals. In: 2018 24th International conference on pattern recognition (ICPR). IEEE, pp 3100–3107

  • Choi HR, Kim TY (2018) Modified dynamic time warping based on direction similarity for fast gesture recognition. Math Probl Eng 2018:1–9

    Google Scholar 

  • Fu Z, Shu KI, Zhang H (2019) Ping Pong motion recognition based on smart watch. In: 3rd International conference on mechatronics engineering and information technology (ICMEIT 2019). Atlantis Press

  • Gao C, Wang L, Xiao Y et al (2018) Infrared small-dim target detection based on Markov random field guided noise modeling. Pattern Recogn 76:463–475

    Article  Google Scholar 

  • Gao Y, Tebbe J, Krismer J et al (2019) Markerless racket pose detection and stroke classification based on stereo vision for table tennis robots. In: 2019 Third IEEE international conference on robotic computing (IRC). IEEE, pp 189–196

  • Gu S, Zeng W, Jia Y et al (2019) Intelligent tennis robot based on a deep neural network. Appl Sci 9(18):3746

    Article  Google Scholar 

  • Harada K, Wan W, Tsuji T et al (2018) Experiments on learning based industrial bin-picking with iterative visual recognition. Ind Robot 45(4):446–457

    Article  Google Scholar 

  • Hu Z, Lee SH, Lee EJ (2018) Improved DT algorithm based human action features detection. J Korea Multimed Soc 21(4):478–484. (in Korean)

  • Ji YF, Zhang JW, Shi Z et al (2018) Research on real-time tracking of table tennis ball based on machine learning with low-speed camera. Syst Sci Control Eng 6(1):71–79

    Article  Google Scholar 

  • Jian Y, Qin C (2021) Design of rolling ball control system based on image recognition. J Phys Confer Ser 1865(4):042037

    Article  Google Scholar 

  • Lara JPR, Vieira CLR, Misuta MS et al (2018) Validation of a video-based system for automatic tracking of tennis players. Int J Perform Anal Sport 18(1):137–150

    Article  Google Scholar 

  • Martinez-Castillo C, Astray G, Mejuto JC et al (2020) Random forest, artificial neural network, and support vector machine models for honey classification. eFood 1(1):69–76

    Article  Google Scholar 

  • Ml A, Hk B, Hjka B (2020) Sparse-view CT reconstruction based on multi-level wavelet convolution neural network. Physica Med 80:352–362

    Article  Google Scholar 

  • Mochizuki I, Toyoura M, Mao X (2018) Visual attention prediction for images with leading line structure. Vis Comput 34(6–8):1031–1041

    Article  Google Scholar 

  • Reno V, Mosca N, Marani R et al (2018) Convolutional neural networks based ball detection in tennis games. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 1758–1764

  • Shimizu T, Hachiuma R, Saito H et al (2019) Prediction of future shot direction using pose and position of tennis player. In: Proceedings of the 2nd international workshop on multimedia content analysis in sports. ACM, pp 59–66

  • Shorman AA, Faris H, Aljarah I (2020) Unsupervised intelligent system based on one class support vector machine and Grey Wolf optimization for IoT botnet detection. J Ambient Intell Humaniz Comput 11(7):2809–2825

    Article  Google Scholar 

  • Wang P, Li Z, Hou Y et al (2016) Action recognition based on joint trajectory maps using convolutional neural networks. In: Proceedings of the 24th ACM international conference on multimedia. ACM, pp 102–106

  • Wei Z (2017) Research on the teaching system of table tennis based on artificial intelligence. In: 2017 Sixth international conference on future generation communication technologies (FGCT). IEEE, pp 1–4

  • Xu S, Liang L, Ji C (2020) Gesture recognition for human–machine interaction in table tennis video based on deep semantic understanding. Signal Process Image Commun 81:115688

    Article  Google Scholar 

  • Zhang D, Gao L, Teng T et al (2020) Underwater moving target detection using track-before-detect method with low power and high refresh rate signal. Appl Acoust 174(4):107750

    Google Scholar 

  • Zhou J, Yang W, Yin Y et al (2021) Improved two-color LIF thermometry for gas–liquid system by optical flow algorithm. Exp Fluids 62(6):1–12

    Article  Google Scholar 

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This research received no external funding.

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Authors and Affiliations

  1. Department of Physical Education, Luxun Academy of Fine Arts, Dalian, 116650, China

    Zhanguo Wang

  2. Department of Visual Communication Design, Luxun Academy of Fine Arts, Dalian, 116650, China

    Yuanbing Zhao

  3. Department of Basic Instruction, Luxun Academy of Fine Arts, Dalian, 116650, China

    Cui Bian

Authors
  1. Zhanguo Wang
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  2. Yuanbing Zhao
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  3. Cui Bian
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All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

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Correspondence to Cui Bian.

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Wang, Z., Zhao, Y. & Bian, C. The analysis of tennis recognition model for human health based on computer vision and particle swarm optimization algorithm. Int J Syst Assur Eng Manag 13 (Suppl 3), 1228–1241 (2022). https://doi.org/10.1007/s13198-022-01673-7

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  • DOI: https://doi.org/10.1007/s13198-022-01673-7

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