Abstract
Sports is an important activity to help maintain and improve human health, help fight diseases, create flexibility for the body, and contribute to training the spirit of competition, spirit teammates, and increase soft human skills. The current sports practice has great support from technical technology, contributing to sports to achieve many high achievements. Currently, many technological techniques are applied to support sports for practicing and monitoring matches. For example, Video Assistant Referee re-examines videos to support the referee’s accurate decisions on a specific situation in football matches and applications in poses recognition of aerobics and martial arts sports. This study uses Fast Forward Moving Picture Experts Group (FFMPEG) technique to extract images from the video of Taekwondo and generate skeleton data from extracted frames with MoveNet. Then, we perform the poses classification tasks for TAEGEUK IN JANG lesson with deep learning architectures such as shallow convolutional neural networks, VGGNet, Inception, and Long Short-Term Memory networks. These architectures are modified to receive 1-Dimensional data as input, including key points of the skeleton. Poses recognition tasks in sports lessons use skeleton data to eliminate noise in the images, such as background behind practitioners and unrelated objects, and only focus on the movement/direction of the poses. Our proposed method has achieved good accuracy in the data, including 35 videos (more than 25,000 frames) distinguishing 20 poses in a basic Taekwondo lesson.
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Notes
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https://www.teamusa.org/usa-taekwondo/v2-getting-started-in-taekwondo/what-is-taekwondo, accessed on 01 August 2022.
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- 4.
https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html, accessed on 6 August 2022.
- 5.
https://phamdinhkhanh.github.io/2020/05/31/CNNHistory.html, accessed on 06 August 2022.
References
Vrigkas, M., Nikou, C., Kakadiaris, I.A.: A review of human activity recognition methods. Frontiers Robot. Artif. Intell. 2, (2015). https://doi.org/10.3389%2Ffrobt.2015.00028
Khan, S., et al.: Human action recognition: a paradigm of best deep learning features selection and serial based extended fusion. Sensors 21(23), 7941 (2021). https://doi.org/10.3390%2Fs21237941
Park, S.U., Jeon, J.W., Ahn, H., Yang, Y.K., So, W.Y.: Big data analysis of the key attributes related to stress and mental health in korean taekwondo student athletes. Sustainability 14(1), 477 (2022). https://doi.org/10.3390%2Fsu14010477
Zhang, Z., Ma, X., Song, R., Rong, X., Tian, X., Tian, G., Li, Y.: Deep learning based human action recognition: a survey. In: 2017 Chinese Automation Congress (CAC). IEEE pp. 3780-3785 (2017). https://doi.org/10.1109%2Fcac.2017.8243438
Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., Baskurt, A.: Sequential deep learning for human action recognition. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 29–39. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25446-8_4
Sargano, A.B., Wang, X., Angelov, P., Habib, Z.: Human action recognition using transfer learning with deep representations. In: 2017 International Joint Conference on Neural Networks (IJCNN). IEEE (2017).https://doi.org/10.1109%2Fijcnn.2017.7965890
Wang, P.: Research on sports training action recognition based on deep learning. Sci. Programm. 2021, 1–8 (2021). https://doi.org/10.1155%2F2021%2F3396878
Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: a review. IEEE Trans. Pattern Anal. Mach. Intell. pp. 1–20 (2022). https://doi.org/10.1109%2Ftpami.2022.3183112
Jaouedi, N., Boujnah, N., Bouhlel, M.S.: A new hybrid deep learning model for human action recognition. J. King Saud Univ. Comput. Inf. Sci. 32(4), 447–453 (2020). https://doi.org/10.1016%2Fj.jksuci.2019.09.004
Berlin, S. Jeba., John, Mala: Particle swarm optimization with deep learning for human action recognition. Multimedia Tools Appl. 79(25), 17349–17371 (2020). https://doi.org/10.1007/s11042-020-08704-0
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks. IEEE (1995). https://doi.org/10.1109%2Ficnn.1995.488968
Wei, H., Jafari, R., Kehtarnavaz, N.: Fusion of video and inertial sensing for deep learning–based human action recognition. Sensors 19(17), 3680 (2019). https://doi.org/10.3390%2Fs19173680
Vinyes Mora, S., Knottenbelt, W.J.: Deep learning for domain-specific action recognition in tennis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, (2017)
Rahmad, N.A., As’ari, M.A., Ghazali, N.F., Shahar, N., Sufri, N.A.J.: A survey of video based action recognition in sports. Indonesian J. Electr. Eng. Comput. Sci. 11(3), 987 (2018). https://doi.org/10.11591%2Fijeecs.v11.i3.pp987-993
Jiang, H., Tsai, S.B.: An empirical study on sports combination training action recognition based on SMO algorithm optimization model and artificial intelligence. Math. Prob. Eng. 2021, 1–11 (2021). https://doi.org/10.1155%2F2021%2F7217383
Liu, N., Liu, L., Sun, Z.: Football game video analysis method with deep learning. Comput. Intell. Neurosci. 2022, 1–12 (2022). https://doi.org/10.1155%2F2022%2F3284156
Tomar, S.: Converting video formats with FFmpeg. Linux J. 2006(146), 10 (2006)
Tammina, S.: Transfer learning using VGG-16 with deep convolutional neural network for classifying images. Int. J. Sci. Res. Publ. (IJSRP) 9(10), p9420 (2019). https://doi.org/10.29322%2Fijsrp.9.10.2019.p9420
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This study is funded in part by the Can Tho University, Code: TSV2022-33.
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Hoang, H.T.T. et al. (2022). Poses Classification in a Taekwondo Lesson Using Skeleton Data Extracted from Videos with Shallow and Deep Learning Architectures. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2022. Communications in Computer and Information Science, vol 1688. Springer, Singapore. https://doi.org/10.1007/978-981-19-8069-5_30
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