A model for Managing the Competitive Activitiesof Top-Level Teams Based on Computer Vision Online
- Authors: Goldstein S.L.1, Polozov A.A.1, Papulovskaya N.V.1, Maltseva N.A.1, Kraev M.V.1
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Affiliations:
- Ural Federal University
- Issue: Vol 25, No 4 (2024)
- Pages: 441-459
- Section: Articles
- URL: https://journals.rudn.ru/engineering-researches/article/view/43096
- DOI: https://doi.org/10.22363/2312-8143-2024-25-4-441-459
- EDN: https://elibrary.ru/CCNDAS
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Abstract
The use of PIRS (Polozov Information Rating System) technology in working with the Russian men’s national futsal team (head coach S.L. Skorovich) in the period from 2010 to 2018 allowed it to move from 5th place in the world ranking to first. The problem of managing a high-level sports team is to selectthe control indicators and their values in order to get the best result of the next match. Usually such parameters are: optimization of the composition, the direction of the game, the placement of their players, the tasks recommended to them. The aim of the study was to structure the activities for the transition from PIRS technology in working with top-level teams to the creation of the PIRSonline system. The description of the PIRSonline system for the development of its technical part required obtaining a number of schemes with their description, which regulated the development of the project. This is the composition of documentation, subsystems, functional diagram, component diagram, function-component matrix. The resulting sequence of actions for the development of PIRSonline includes image acquisition, a block for initializing game parameters, ball detection, determining the 3D coordinates of the ball, detecting players, tracking, classifying players and referees, determining the player’s pose. This made it possible to implement an integrated approach to online management of competitive activities of top-level teams. The use of modern computer vision and machine learning methods has made it possible to create interactive data visualization.
About the authors
Sergei L. Goldstein
Ural Federal University
Email: s.l.goldshtein@urfu.ru
SPIN-code: 5951-7411
Doctor of Technical Sciences, Professor of the Department of Technical Physics Ekaterinburg, Russia
Andrey A. Polozov
Ural Federal University
Author for correspondence.
Email: a.a.polozov@mail.ru
ORCID iD: 0000-0003-1729-3340
SPIN-code: 5234-6875
Doctor of Pedagogical Sciences, Professor of the Department of Physical Education, Institute of Physical Culture, Sports and Youth Policy
Ekaterinburg, RussiaNatalia V. Papulovskaya
Ural Federal University
Email: n.v.papulovskaia@urfu.ru
ORCID iD: 0000-0001-7407-1491
SPIN-code: 1087-5428
Candidate of Pedagogical Sciences, Associate Professor of the Department of Information Technology and Management Systems
Ekaterinburg, RussiaNatalya A. Maltseva
Ural Federal University
Email: natalia.maltseva.susu@gmail.com
ORCID iD: 0009-0002-5270-0247
SPIN-code: 2647-1891
Postgraduate student, Lecturer at the Department of Information Technology and Management Systems
Ekaterinburg, RussiaMaxim V. Kraev
Ural Federal University
Email: kraev.antooz@yandex.ru
ORCID iD: 0000-0002-3724-8929
Teacher of the Department of Physical Education, Institute of Physical Culture, Sports and Youth Policy
Ekaterinburg, RussiaReferences
- Morozov YuA, Beskov KI. Analysis of technical and tactical activities of football players at the 10th World Championship. Training of football players. Moscow: Fizkul’tura i sport Publ.; 1977. P. 134–155. (In Russ.)
- Goldenko GA. Individual programs of technical and tactical training of highly qualified football players taking into account the peculiarities of competitive activity: Abstract of Cand. Sci. (Pedagogical Sciences) Dissertation. Moscow; 1984. (In Russ.)
- Fedotov E.V. et al. Evaluation of competitive load and development of means of special training of female athletes in field hockey using heart rate monitors. Theory and Practice of Physical Education. 2006;(3):23–26. (In Russ.)
- Godik M.A. Control during sports training. Training of football players. Moscow: Fizkul’tura i sport Publ.; 1978. (In Russ.)
- Platonov VN. The system of training athletes in Olympic sports: General theory and its practical applications. Kyiv: Olympic Literature Publ.; 2004. (In Russ.)
- Polivaev AG. Automated system for assessing the utility coefficient of a player in mini-football. Omsk Scientific Bulletin. 2015;4(141):219–224. (In Russ.) EDN: UKTYCT
- Beetz M, Hoyningen-Huene NV, Kirchlechner B, Gedikli S, Siles F, Durus M, Lames M. ASPOGAMO: Automated Sports Games Analysis Models. International Journal of Computer Science in Sport. 2009;8(1):1–21.
- Polozov AA, Kraev MV, Rolis AV. Feasibility of statistics of technical and tactical actions in football on the example of the Wyscout company. Theory and practice of physical education. 2021;(1):82–84. (In Russ.) EDN: PXLEBP
- Shurmanov EG, Polozov AA, Mehryakov SV, Bozhko EM. Evaluation of the implementation of goal chances in a game sport. Theory and practice of physical education. 2018;(1);66–68. (In Russ.)
- Polozov AA, Kraev MV, Gazimova ZF. Informat-ion model of football on the example of participation of the Russian national team at the 2018 World Cup. Human. Sport. Medicine. 2018;18(1):138–148. (In Russ.)
- Naik BT, Hashmi MF, Bokde ND. A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions. Applied Sciences. 2022;12(9):4429. https://doi.org/10.3390/app12094429
- Blond A. Computer Vision in Sports: Revolutionizing Performance Analysis. Blog Requestum. 11.2020. Available from: https://requestum.com/blog/computer-vision-in-sports (accessed: 21.02.2024).
- Korol S. Computer Vision in Sports: People Train and Compete — Machines Watch and Help. OpenCV Blog. 2024. Available from: https://www.opencv.ai/blog/computer-vision-in-sports (accessed: 21.02.2024).
- Boesch G. Automatic Refereeing and AI in Sports. Viso.ai Blog. 2024. Available from: https://viso.ai/applications/visual-ai-in-sports/ (accessed: 21.02.2024).
- Oldham KM, Chung PWH, Edirisinghe EA, Hal-kon BJ. Experiments in the Application of Computer Vision for Ball and Event Identification in Indoor Sports. In: ВS Phon-Amnuaisuk, TW Au (eds.). Computational Intelligence in Information Systems SE – 27. Advances in Intelligent Systems and Computing. Springer Publ.; 2015. Р. 275–284. http://doi.org/10.1007/978-3-319-13153-5_27
- Akyildiz Z, Nobari H, González-Fernández F, Praça GM, Sarmento H, Guler AH, Saka EK, Clemente FM, Figueiredo AJ. Variations in the physical demands and tech-nical performance of professional soccer teams over three consecutive seasons. Scientific Reports. 2022;12:2412. http://doi.org/10.1038/s41598-022-06365-7
- Kovalchik SA. Player Tracking Data in Sports. Annual Review of Statistics and Its Application. 2023;10:677–697. https://doi.org/10.1146/annurev-statistics-033021-110117
- AWS “AI-Powered Sports Analytics: Enhancing Team Performance”. AWS Sports Journal 2024. Available from: https://aws.amazon.com/ru/sports/performance-analytics/ (accessed: 21.02.2024).
