banner
Home > Open Journal Systems

Transfer learning model for the motion detection of sports players

Wael Y. Alghamdi

Abstract


Recognizing and analyzing moving targets is an important research subject since computer vision is employed in so many facets of our daily lives, including intelligent robotics, video surveillance, medical education, sporting events, and the maintenance of our national defense. This is because it may be difficult to properly analyse and keep up with moving materials. The various training postures of an athlete are explored in this study through the examination of a weightlifting video. This article was written to assist coaches in their efforts to improve the performance of their athletes in their respective sports. A technique for extracting essential poses from sports films has been proposed. The classification of different subjects of interest serves as the foundation for this technique. Because of its inadequate edge detection method, the current motion identification system does a bad job of detecting athletes, which is one of the reasons why it does a poor job of identifying motion in general. This flaw is one of the reasons why the system isn’t very strong at detecting athletes. The following was one of the factors that contributed to this outcome: in truth, the situation is currently in this state. The result of the newly developed system outperforms the prior system in terms of tracking recognition accuracy and convergence speed. The system was put to the test. The findings of the system’s study served as the foundation for this decision. Finally, the findings of the categorization reveal that the selection approach tries to separate fundamental postures.

Keywords


Deep Learning; Motion Detection; Sportsman; Convolutional Neural Network; Segmentation

Full Text:

PDF

References


1. Miller J, Nair U, Ramachandran R, Maskey M. Detection of transverse cirrus bands in satellite imagery using deep learning. Computers & Geosciences 2018; 118: 79–85. doi: 10.1016/j.cageo.2018.05.012.

2. Liu H, Jin J, Xu Z, et al. Deep learning based code smell detection. IEEE Transactions on Software Engineering 2021; 47(9): 1811–1837. doi: 10.1109/TSE.2019.2936376.

3. Gao M, Cai W, Liu R. AGTH-Net: Attention-based graph convolution-guided third-order hourglass network for sports video classification. Engineering 2021; 2021: 8517161. doi: 10.1155/2021/8517161.

4. Pan S. A method of key posture detection and motion recognition in sports based on Deep Learning. Mobile Information Systems 2022; 2022: 5168898. doi: 10.1155/2022/5168898.

5. Zhou J, Wang Y, Zhang W. Underwater image restoration via information distribution and light scattering prior. Computers and Electrical Engineering 2022; 100: 107908. doi: 10.1016/j.compeleceng.2022.107908.

6. Lamas A, Tabik S, Cruz P, et al. MonuMAI: Dataset, deep learning pipeline and citizen science based app for monumental heritage taxonomy and classification. Neurocomputing 2021; 420: 266–280. doi: 10.1016/j.neucom.2020.09.041.

7. Nandyal S, Kattimani SL. Bird swarm optimization-based stacked autoencoder deep learning for umpire detection and classification. Scalable Computing 2020; 21(2): 173–188. doi: 10.12694/scpe.v21i2.1655.

8. Zhao X, Zuo T, Hu X. OFM-SLAM: A visual semantic SLAM for dynamic indoor environments. Mathematical Problems in Engineering 2021; 2021: 5538840. doi: 10.1155/2021/5538840.

9. Guo Q. Detection of head raising rate of students in classroom based on head posture recognition.

10. Traitement du Signal 2020; 37(5): 823–830.

11. Bruno A, Gugliuzza F, Pirrone R, Ardizzone E. A multi-scale colour and keypoint density-based approach for visual saliency detection. Access 2020; 8: 121330–121343. doi: 10.1109/ACCESS.2020.3006700.

12. Xu Y. A sports training video classification model based on deep learning. Scientific Programming 2021; 2021: 7252896. doi: 10.1155/2021/7252896.

13. Liu J, Chen D, Wu Y, et al. Image edge recognition of virtual reality scene based on multi-operator dynamic weight detection. Access 2020; 8: 111289–111302. doi: 10.1109/ACCESS.2020.3001386.

14. Kalakoti G, Prabakaran G. Key-frame detection and video retrieval based on DC coefficient-based cosine orthogonality and multivariate statistical tests. Traitement du Signal 2020; 37(5): 773–784.

15. Li-quan C, You L, Shen F, et al. Pose recognition in sports scenes based on deep learning skeleton sequence model. Journal of Intelligent & Fuzzy Systems 2021; Preprint: 1–10.

16. Pu B, Zhu N, Li K, Li S. Fetal cardiac cycle detection in multi-resource echocardiograms using hybrid classification framework. Future Generation Computer Systems 2021; 115: 825–836. doi: 10.1016/j.future.2020.09.014.

17. Cui J, Wang M, Luo Y, Zhong H. DDoS detection and defense mechanism based on cognitive-inspired computing in SDN. Future Generation Computer Systems 2019; 97: 275–283. doi: 10.1016/j.future.2019.02.037.

18. Taborri J, Molinaro L, Santospagnuolo A, et al. A machine-learning approach to measure the anterior cruciate ligament injury risk in female basketball players. Sensors 2021; 21(9): 3141. doi: 10.3390/s21093141.

19. Wilkens S. Sports prediction and betting models in the machine learning age: The case of tennis. Journal of Sports Analytics 2021; 7(2): 99–117. doi: 10.3233/JSA-200463.

20. Liu G, Zhu W, Schulte O. Interpreting deep sports analytics: Valuing actions and players in the NHL. In: Brefeld U, Davis J, Van Haaren J, Zimmermann A (editors). Machine learning and data mining for sports analytics. MLSA 2018. Lecture notes in computer science, vol 11330. Cham: Springer Cham; 2019. p. 69–81.

21. Wang P, Liu ZQ. Research on evaluation of vehicle dangerous traveling state based on information fusion method. Advanced Materials Research 2013; 791–793: 1018–1022. doi: 10.4028/www.scientific.net/AMR.791-793.1018.

22. Xie T, Zhang C, Zhang Z. Utilizing active sensor nodes in smart environments for optimal communication coverage. IEEE Access 2018; 7: 11338–11348. doi: 10.1109/ACCESS.2018.2889717.

23. Li T, Sun J, Wang L. An intelligent optimization method of motion management system based on BP neural network. Neural Computing & Applications 2021; 33(2): 707–722. doi: 10.1007/s00521-020-05093-1.

24. Barshan B, Yüksek MC. Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units. The Computer Journal 2014; 57(11): 1649–1667. doi: 10.1093/comjnl/bxt075.

25. Zhang C, Xie T, Yang K, et al. Positioning optimisation based on particle quality prediction in wireless sensor networks. IET Networks 2019; 8(2): 107–113. doi: 10.1049/iet-net.2018.5072.

26. Chunfeng G. Research on pre-competition emotion recognition of student athletes based on improved machine learning. Journal of Intelligent and Fuzzy Systems 2020; 39(4): 5687–5698. doi: 10.3233/JIFS-219218.

27. Ding Q, Ding Z. Machine learning model for feature recognition of sports competition based on improved TLD algorithm. Journal of Intelligent and Fuzzy 2021; 40(2): 2697–2708. doi: 10.3233/JIFS-189312.

28. Li P, Cai J. Research on image recognition from wireless sensor data based on deep learning. Journal of Critical Care 2017; 42(6): 2607–2612.

29. Colyer SL, Evans M, Cosker DP, Salo AIT. A review of the evolution of vision-based motion analysis and the integration of advanced computer vision methods towards developing a markerless system. Sports Medicine-Open 2018; 4: 24. doi: 10.1186/s40798-018-0139-y.

30. Pan D, Liu H, Qu D, Zhang Z. Human falling detection algorithm based on multisensor data fusion with SVM. Mobile Information Systems 2020; 2020: 8826088. doi: 10.1155/2020/8826088.

31. Nagalakshmi Vallabhaneni DPP. The analysis of the impact of yoga on healthcare and conventional strategies for human pose recognition. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 2021; 12(6): 1772–1783. doi: 10.17762/turcomat.v12i6.4032.

32. Yan L, Shen M, Yao W, et al. Recognition method of lactating sows’ posture based on sensor MPU6050. Transactions of the Chinese Society for Agricultural Machinery 2015; 46(5): 279–285. doi: 10.6041/j.issn.1000-1298.2015.05.040.

33. Hu X, Zhang Y. Study of face detection algorithm based on complexion. Journal of Hefei University of Technology 2012; 35(7): 908–912. doi: 10.3969/j.issn.1003-5060.2012.07.011.

34. Kashyap R. Evolution of histopathological breast cancer images classification using stochastic dilated residual ghost model. Turkish Journal of Electrical Engineering & Computer Science 2021; 29: 2758–2779. doi: 10.3906/elk-2104-40.




DOI: https://doi.org/10.32629/jai.v6i1.577

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Wael Y. Alghamdi

License URL: https://creativecommons.org/licenses/by-nc/4.0