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, January). Real Time Fencing Move Classification and Detection at Touch Time during a Fencing Match. Presented at International Conference on Pattern Recognition (ICPR), Milan

• 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