We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.

Durham Research Online
You are in:

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

Sunal, Cem Ekin and Willcocks, Chris G. and Obara, Boguslaw (2021) 'Real Time Fencing Move Classification and Detection at Touch Time during a Fencing Match.', International Conference on Pattern Recognition (ICPR) Milan, 10 - 15 Jan 2021.


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.

Item Type:Conference item (Paper)
Full text:(AM) Accepted Manuscript
Download PDF
Publisher Web site:
Publisher statement:© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:11 October 2020
Date deposited:29 October 2021
Date of first online publication:05 May 2021
Date first made open access:29 October 2021

Save or Share this output

Look up in GoogleScholar