Automatic scoring in fencing by using skeleton points extracted from images

Takehiro Sawahata; Alessandro Moro; Sarthak Pathak; Kazunori Umeda

doi:10.1117/12.3000424

28 July 2023 Automatic scoring in fencing by using skeleton points extracted from images

Takehiro Sawahata, Alessandro Moro, Sarthak Pathak, Kazunori Umeda

Proceedings Volume 12749, Sixteenth International Conference on Quality Control by Artificial Vision; 1274919 (2023) https://doi.org/10.1117/12.3000424
Event: Sixteenth International Conference on Quality Control by Artificial Vision, 2023, Albi, France

Abstract

First time spectators of fencing competitions cannot understand the complicated rules, making it difficult for them to enjoy the game. Therefore, in this paper, we propose a system that detects the situation of a fencing match using skeleton points extracted from videos. Players cannot be equipped with sensors or other devices to prevent interference with the match. Consequently, this research proposes a system that detects "phrases" using skeleton point information extracted from videos and displays the game situation. We evaluate actual videos of fencing to confirm the performance.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Takehiro Sawahata, Alessandro Moro, Sarthak Pathak, and Kazunori Umeda "Automatic scoring in fencing by using skeleton points extracted from images", Proc. SPIE 12749, Sixteenth International Conference on Quality Control by Artificial Vision, 1274919 (28 July 2023); https://doi.org/10.1117/12.3000424

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KEYWORDS

Deep learning

Video

RGB color model

Systems modeling

Visualization

Cameras

Pose estimation

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