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Real Time Fencing Move Classification and Detection at Touch Time during a Fencing Match

Sunal, Cem Ekin; Willcocks, Chris G.; Obara, Boguslaw

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Authors

Cem Ekin Sunal

Boguslaw Obara



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

Conference Name International Conference on Pattern Recognition (ICPR)
Conference Location Milan
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 Mar 29, 2024
Pages 5760-5766
Series ISSN 1051-4651
DOI https://doi.org/10.1109/icpr48806.2021.9412024

Files

Accepted Conference Proceeding (706 Kb)
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