Multi-modal hit detection and positional analysis in padel competitions
- Author
- Robbe Decorte (UGent) , Martin Paré (UGent) , Jelle Vanhaeverbeke (UGent) , Joachim Taelman (UGent) , Maarten Slembrouck (UGent) and Steven Verstockt (UGent)
- Organization
- Abstract
- Padel is a rapidly growing racquet sport and has gained popularity globally due to its accessibility and exciting gameplay dynamics. Effective coordination between teammates hinges on maintaining an appropriate distance, allowing for seamless transitions between offensive and defensive maneuvers. A balanced inter-player distance and distance to the net not only facilitates efficient communication but also enhances the team’s ability to exploit openings in the opponent’s defense while minimizing vulnerabilities. We introduce a new open dataset of padel rallies with annotations for hits and player-ball interactions, a predictive model for detecting hits based on audio signals, a re-identification algorithm for pose tracking, and a framework for calculating inter-player and player-net distances during rallies. Our predictive model achieves an average F1-score of 92% for hit detection, demonstrating robust performance across different match conditions. Furthermore, we develop a system for accurately assigning hits to individual players, achieving an overall accuracy of 83.70% for player-specific assignment and 86.83% for team-based assignment.
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01J0X4X6M285CWXTT89316SBCH
- MLA
- Decorte, Robbe, et al. “Multi-Modal Hit Detection and Positional Analysis in Padel Competitions.” 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE, 2024, pp. 3306–14, doi:10.1109/CVPRW63382.2024.00335.
- APA
- Decorte, R., Paré, M., Vanhaeverbeke, J., Taelman, J., Slembrouck, M., & Verstockt, S. (2024). Multi-modal hit detection and positional analysis in padel competitions. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 3306–3314. https://doi.org/10.1109/CVPRW63382.2024.00335
- Chicago author-date
- Decorte, Robbe, Martin Paré, Jelle Vanhaeverbeke, Joachim Taelman, Maarten Slembrouck, and Steven Verstockt. 2024. “Multi-Modal Hit Detection and Positional Analysis in Padel Competitions.” In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 3306–14. IEEE. https://doi.org/10.1109/CVPRW63382.2024.00335.
- Chicago author-date (all authors)
- Decorte, Robbe, Martin Paré, Jelle Vanhaeverbeke, Joachim Taelman, Maarten Slembrouck, and Steven Verstockt. 2024. “Multi-Modal Hit Detection and Positional Analysis in Padel Competitions.” In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 3306–3314. IEEE. doi:10.1109/CVPRW63382.2024.00335.
- Vancouver
- 1.Decorte R, Paré M, Vanhaeverbeke J, Taelman J, Slembrouck M, Verstockt S. Multi-modal hit detection and positional analysis in padel competitions. In: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE; 2024. p. 3306–14.
- IEEE
- [1]R. Decorte, M. Paré, J. Vanhaeverbeke, J. Taelman, M. Slembrouck, and S. Verstockt, “Multi-modal hit detection and positional analysis in padel competitions,” in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA, 2024, pp. 3306–3314.
@inproceedings{01J0X4X6M285CWXTT89316SBCH,
abstract = {{Padel is a rapidly growing racquet sport and has gained popularity globally due to its accessibility and exciting gameplay dynamics. Effective coordination between teammates hinges on maintaining an appropriate distance, allowing for seamless transitions between offensive and defensive maneuvers. A balanced inter-player distance and distance to the net not only facilitates efficient communication but also enhances the team’s ability to exploit openings in the opponent’s defense while minimizing vulnerabilities. We introduce a new open dataset of padel rallies with annotations for hits and player-ball interactions, a predictive model for detecting hits based on audio signals, a re-identification algorithm for pose tracking, and a framework for calculating inter-player and player-net distances during rallies. Our predictive model achieves an average F1-score of 92% for hit detection, demonstrating robust performance across different match conditions. Furthermore, we develop a system for accurately assigning hits to individual players, achieving an overall accuracy of 83.70% for player-specific assignment and 86.83% for team-based assignment.}},
author = {{Decorte, Robbe and Paré, Martin and Vanhaeverbeke, Jelle and Taelman, Joachim and Slembrouck, Maarten and Verstockt, Steven}},
booktitle = {{2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}},
isbn = {{9798350365481}},
issn = {{2160-7508}},
language = {{eng}},
location = {{Seattle, WA, USA}},
pages = {{3306--3314}},
publisher = {{IEEE}},
title = {{Multi-modal hit detection and positional analysis in padel competitions}},
url = {{http://doi.org/10.1109/CVPRW63382.2024.00335}},
year = {{2024}},
}
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