Cem Ekin Sunal
Real Time Fencing Move Classification and Detection at Touch Time during a Fencing Match
Sunal, Cem Ekin; Willcocks, Chris G.; Obara, Boguslaw
Abstract
Fencingis a fast-paced sport played with swords which are Épée, Foil, and Sabre. However, such fast-pace can cause referees to make wrong decisions. Review of slow-motion camera footage in tournaments helps referees' decision-making, but it interrupts the match and may not be available for every organisation. Motivated by the need for better decision-making, analysis and availability, we introduce the first fully-automated deep learning classification and detection system for fencing body moves at the moment a touch is made. This is an important step towards creating a fencing analysis system, with player profiling and decision tools that will benefit the fencing community. The proposed architecture combines You Only Look Once version three (YOLOv3) with a ResNet-34 classifier, trained on ImageNet settings, to obtain 83.0 % test accuracy on the fencing moves. These results are exciting development in the sport, providing immediate feedback and analysis along with accessibility, hence making it a valuable tool for trainers and fencing match referees.
Citation
Sunal, C. E., Willcocks, C. G., & Obara, B. (2021). Real Time Fencing Move Classification and Detection at Touch Time during a Fencing Match. . https://doi.org/10.1109/icpr48806.2021.9412024
Presentation Conference Type | Conference Paper (Published) |
---|---|
Conference Name | International Conference on Pattern Recognition (ICPR) |
Start Date | Jan 10, 2021 |
End Date | Jan 15, 2021 |
Acceptance Date | Oct 11, 2020 |
Online Publication Date | May 5, 2021 |
Publication Date | 2021-10 |
Deposit Date | Nov 27, 2020 |
Publicly Available Date | Oct 29, 2021 |
Pages | 5760-5766 |
Series ISSN | 1051-4651 |
DOI | https://doi.org/10.1109/icpr48806.2021.9412024 |
Public URL | https://durham-repository.worktribe.com/output/1139891 |
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