Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

Series Vol. 5 , 31 May 2023


Open Access | Article

Review of computer vision in sports

Xinyu Shao * 1
1 University of Manchester, Manchester, United Kingdom

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 5, 28-33
Published 31 May 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Xinyu Shao. Review of computer vision in sports. ACE (2023) Vol. 5: 28-33. DOI: 10.54254/2755-2721/5/20230519.

Abstract

All industries employ machine learning extensively, and one of the most promising fields is computer vision. Computer vision is a simulation of the human visual system that uses cameras and computers to take the role of the human eye to find the target, follow it, and gather data from it so that a decision may be made on whether to take further action or provide recommendations. The various uses of computer vision in sports are covered in this paper. Currently, computer vision is mostly utilized for broadcast enhancement, tracking and detection of players and balls. Although the game’s graphics has been substantially improved by this technology, there are still several flaws. For instance, some areas are not suited to employ this technology. Another is the issue of players being blocked in multiplayer sports. For broadcasters, computer vision has significant commercial value. For athletes, this technique can improve their performance.

Keywords

Machine Learning, Computer Vision, Sport, Tracking, Detection, Broadcast Enhancements.

References

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3. Bialik, C. (2014). The people tracking every touch, pass and tackle in the world cup. Fivethirtyeight. com.

4. Tamir, M., & Oz, G. (2008). U.S. Patent Application No. 11/909, 080.

5. Monier, E., Wilhelm, P., & Rückert, U. (2009). A computer vision based tracking system for indoor team sports. In The fourth international conference on intelligent computing and information systems.

6. Lewis, J. P. 1995. Fast normalized cross-correlation. In Proceedings of Vision Interface 95, Canadian Image Processing and Pattern Recognition Society, pp.120-123.

7. Grau, O., Price, M. C., & Thomas, G. A. (2000, December). Use of 3d techniques for virtual production. In Videometrics and Optical Methods for 3D Shape Measurement (Vol. 4309, pp. 40-50). SPIE.

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
ISBN (Print)
978-1-915371-57-7
ISBN (Online)
978-1-915371-58-4
Published Date
31 May 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/5/20230519
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated