Computer vision systems and deep learning for the recognition of athlete's movement: A review article
Article Sidebar
Main Article Content
Abstract
The process of detecting people in videos and then tracking their movement is one of the very important topics. The process of tracking people and studying their behaviour could result in a large set of information that can help researchers in studying reactions. The techniques of detection and tracking the movement of people are used in the sports field, where the athlete's movement is studied and analyzed within the game. Based on the information obtained from the process of tracking the athlete's movement, it is possible to improve the playing performance as well as avoid injuries and choose the best playing strategy. In some games, the accuracy of athlete's performance is a measure of the points given to the athlete's like gymnastics. This study reviews a set of articles that relied on computer vision as well as deep learning in the process of distinguishing and analysing the athlete's movement. The articles are confined to the years from 2015 to 2022, dealing with different indoor and outdoor sports. Certainly, the study of indoor games is better because the influence of weather conditions is less than that of outdoor ones. Reviewing the articles demonstrates that relying on computer vision systems is more effective than relying on human rulers, as humans are more prone to error. As for relying on the deep learning techniques for detecting the object, the results are very positive, due to the correct detection of the object. The results of the analysis of this object will be more accurate.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Tikrit Journal of Pure Science is licensed under the Creative Commons Attribution 4.0 International License, which allows users to copy, create extracts, abstracts, and new works from the article, alter and revise the article, and make commercial use of the article (including reuse and/or resale of the article by commercial entities), provided the user gives appropriate credit (with a link to the formal publication through the relevant DOI), provides a link to the license, indicates if changes were made, and the licensor is not represented as endorsing the use made of the work. The authors hold the copyright for their published work on the Tikrit J. Pure Sci. website, while Tikrit J. Pure Sci. is responsible for appreciate citation of their work, which is released under CC-BY-4.0, enabling the unrestricted use, distribution, and reproduction of an article in any medium, provided that the original work is properly cited.
References
1. iaoYing Zhang and Yiran Chen,” Research on Multitarget Recognition and Detection Based on Computer Vision”, journal of Sensors,2022.
2. Athanasios Voulodimos Nikolaos Doulamis, Anastasios Doulamis, and Eftychios Protopapadakis,” Deep Learning for Computer Vision: A Brief Review”, Computational Intelligence and Neuroscience journal ,2018.
3. 3. Jing Zhang and Zhaochun Li,” Intelligent Tea- Picking System Based on Active Computer Vision and Internet of Things”, Security and Communication Networks.2021.
4. M. Waleed, A. S. Abdullah and S. R. Ahmed, “Classification of Vegetative pests for cucumber plants using artificial neural networks,” 2020 3rd International Conference on Engineering Technology and its Applications (IICETA), Sep. 2020
5. Graham Thomas, Rikke Gade, Thomas B. Moeslund, Adrian Hilton,” Computer vision for sports: current applications and research topics “, Computer Vision and Image Understanding · April 2017
6. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proceedings of the 27th IEEE Conference on Computer Recognition (CVPR ’14), pp. 580– 587, Columbus, Ohio, USA, June 2014
7. ATS. STATS SportVU R : Player tracking and predictive analytics. https://www. stats.com/publications/statsbsportvuplayer-tracking-advanced-analytics/. Accessed 12 Feb 2017.
8. Banoth Thulasya Naika , Mohammad Farukh Hashmia , Neeraj Dhanraj Bokde,” A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions”, applied
science journal ,MDPI,2022
9. Wu, Lifang, Zhou Yang, Jiaoyu He, MengJian, Yaowen Xu, Dezhong Xu, and Chang Wen Chen. "Ontology based global and collective motion patterns for event classification in basketball videos." IEEE Transactions on Circuits and Systems for Video Technology ,2019.
10. Wu, Lifang, Zhou Yang, Qi Wang, Meng Jian, Boxuan Zhao, Junchi Yan, and Chang Wen Chen. "Fusing motion patterns and key visual information for semantic event recognition in basketball videos." Neurocomputing ,2020.
11. Liu, long. "Objects detection toward complicated high remote basketball sports by leveraging deep CNN architecture." Future Generation Computer Systems ,2021.
12. Ahmed Saadi Abdullah, Majid Hamid Ali, Mohammed Waleed,” Distributed Prewitt Edge Detection System Using Lightness of Ycbcr Color Space”, Webology, Volume 19, Number 1, 2022.
13. Chakraborty, Bodhisattwa, and SukadevMeher. "A real-time trajectory-based ball detection-andtracking framework for basketball video." Journal of Optics 42, no. 2 ,2013
14. Hauri, Sandro, Nemanja Djuric, Vladan Radosavljevic, and Slobodan Vucetic. "Multi-Modal Trajectory Prediction of NBA Players." In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, , 2021.
15. Rajiv Shah, Rob Romijnders,” Applying Deep Learning to Basketball Trajectories” Neural and Evolutionary Computing,2016.
16. Leili Javadpoura, Jessica Blakesleeb, Mehdi Khazaelib and Pete Schroedera,” Optimizing the best play in basketball using deep learning”, Journal of Sports Analytic,2022
17. Peng Yao,” Real-Time Analysis of Basketball Sports Data Based on Deep Learning”, Complexity,2022.
18. Xu-Hong Meng, Hong-Ying Shi, and Wei-Hong Shang,” Analysis of Basketball Technical Movements Based on Human-Computer Interaction with Deep Learning”, Computational Intelligence and Neuroscience,2022.
19. Arda Senocak, Tae-Hyun Oh, Junsik Kim, In So Kweon,” Part-based Player Identification using Deep Convolutional Representation and Multi-scale Pooling”, IEEE,2020.
20. Garnier, Paul, and Théophane Gregoir. "Evaluating Soccer Player: from Live Camera to Deep Reinforcement Learning." arXiv preprint arXiv:2101.05388 ,2021.
21. Cioppa, Anthony, AdrienDeliege, Silvio Giancola, Bernard Ghanem, Marc Van Droogenbroeck, RikkeGade, and Thomas B. Moeslund. "A context-aware loss function for action spotting in soccer videos." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.
22. Beernaerts, Jasper, Bernard De Baets, Matthieu Lenoir, and Nico Van de Weghe. "Spatial movement pattern recognition in soccer based on relative player movements." Plos one 15, no. 1 (2020.
23. Lindström, Per, Ludwig Jacobsson, NiklasCarlsson, and Patrick Lambrix. "Predicting Player Trajectories in Shot Situations in Soccer." (2019).
24. Suzuki, Genki, Sho Takahashi, Takahiro Ogawa, and Miki Haseyama. "Team tactics estimation in soccer videos via deep extreme learning machine based on players formation." In 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE), pp. 116-117. IEEE, 2018.
25. Kautz, Thomas, Benjamin H. Groh, Julius Hannink, Ulf Jensen, HolgerStrubberg, and Bjoern M. Eskofier. "Activity recognition in beach volleyball using a Deep Convolutional Neural Network." Data Mining and Knowledge Discovery ,2017.
26. Naik, B.; Hashmi, M.F. YOLOv3-SORT detection and tracking player-ball in soccer sport. Journal of Electronic Imaging 2022
27. Jiwon Lee, Do-Won Nam, and Wonyoung Yoo,” Soccer Object Motion Recognition based on 3D Convolutional Neural Networks”, Communication Papers of the Federated Conference on Computer Science and Information Systems,2018.
28. Omprakash Yadav, Rachael Dsouza, Rhea Dsouza, Janice Jose,” Soccer Action Video Classification using Deep Learning”, International Journal for Research in Applied Science & Engineering Technology Volume 10 Issue VI June 2022.
29. Tianyi Wang and Tongyan Li,” Deep Learning-Based Football Player Detection in Videos”, Computational Intelligence and Neuroscience,2022.
30. Muhamad Rezki Dwijayanto, Sumantri Kurniawan, Budi Sugandi,” Real-Time Object Recognition for Football Field Landmark Detection Based on Deep Neural Networks”, IEEE,2019.
31. Cioppa, Anthony, AdrienDeliege, Silvio Giancola, Bernard Ghanem, Marc Van Droogenbroeck,
RikkeGade, and Thomas B. Moeslund. "A context-aware loss function for action spotting in soccer videos." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.
32. Beernaerts, Jasper, Bernard De Baets, Matthieu Lenoir, and Nico Van de Weghe. "Spatial movement pattern recognition in soccer based on relative player movements." Plos one 15, no. 1 (2020.
33. Garnier, Paul, and Théophane Gregoir. "Evaluating Soccer Player: from Live Camera to Deep Reinforcement Learning." arXiv preprint arXiv:2101.05388 ,2021.
34. Lindström, Per, Ludwig Jacobsson, NiklasCarlsson, and Patrick Lambrix. "Predicting Player Trajectories in Shot Situations in Soccer." (2019).
35. Suzuki, Genki, Sho Takahashi, Takahiro Ogawa, and Miki Haseyama. "Team tactics estimation in soccer videos via deep extreme learning machine based on players formation." In 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE), pp. 116-117. IEEE, 2018.
36. Kautz, Thomas, Benjamin H. Groh, Julius Hannink, Ulf Jensen, HolgerStrubberg, and Bjoern M. Eskofier. "Activity recognition in beach volleyball using a Deep Convolutional Neural Network." Data Mining and Knowledge Discovery ,2017.
37. Nur Azmina Rahmad, Nur Anis Jasmin Sufri , Nurul Hamizah Muzamil , Muhammad Amir As’ari ,” Badminton player detection using faster region convolutional neural network”, Indonesian Journal of Electrical Engineering and Computer Science,2019.
38. Nur Azmina Rahmad, Nur Anis Jasmin Sufri , Muhammad Amir As’Ari , Aizreena Azaman,” Recognition of Badminton Action Using Convolutional Neural Network”, Indonesian Journal of Electrical Engineering and Informatics,2019.
39. N A Rahmad and M A As’ari,” The new Convolutional Neural Network (CNN) local feature extractor for automated badminton action recognition on vision based data”, Journal of Physics: Conference Series, JICETS 2019.
40. N A Rahmad, M A As’ari , K Soeed1 and I Zulkapri,” Automated badminton smash recognition using convolutional neural network on the vision based data”, Sustainable and Integrated Engineering International Conference 2019.
41. ip Zi Yingand other,” Badminton Smashing Recognition through Video Performance by using Deep Learning” MEKATRONIKA,2022.
42. Muhammad Zeeshan Khan and other,” Deep CNN based Data-driven Recognition of Cricket Batting Shots” IEEE,2018.
43. Md Nafee Al Islam and other,” A CNN-based approach to classify cricket bowlers based on their bowling actions”, IEEE.2019.
44. Atharv Pushkraj Nirgude and other,” AN INTERACTIVE APPROACH TO IDENTIFY CRICKET SHOTS THROUGH DEEP LEARNING MECHANISM”, International Research Journal of Modernization in Engineering Technology and Science,2022.
45. JinGen Tang,” An Action Recognition Method for Volleyball Players Using Deep Learning”, Scientific Programming,2019.
46. Pobar M, Ivasic-Kos M. Active Player Detection in Handball Scenes Based on Activity Measures. Sensors (Basel). 2020.