Sumo Action Classification Using Mawashi Keypoints

Yuki Utsuro, Hidehiko Shishido, Yoshinari Kameda

2024

Abstract

We propose a new method of classification for kimarites in sumo videos based on kinematic pose estimation. Japanese wrestling sumo is a combat sport. Sumo is played by two wrestlers wearing a mawashi, a loincloth fastened around the waist. In a sumo match, two wrestlers grapple with each other. Sumo wrestlers perform actions by grabbing their opponents’ mawashi. A kimarite is a sumo winning action that decides the outcome of a sumo match. All the kimarites are defined based on their motions. In an official sumo match, the kimarite of the match is classified by the referee, who oversees the classification just after the match. Classifying kimarites from videos by computer vision is a challenging task. There are two reasons. The first reason is that the definition of kimarites requires us to examine the relationship between the mawashi and the pose. The second reason is the heavy occlusion caused by the close contact between wrestlers. For the precise examination of pose estimation, we introduce a wrestler-specific skeleton model with mawashi keypoints. The relationship between mawashi and body parts is uniformly represented in the pose sequence with this extended skeleton model. As for heavy occlusion, we represent sumo actions as pose sequences to classify the sumo actions. Our method achieves an accuracy of 0.77 in action classification by LSTM. We confirmed that the skeleton model extension by mawashi keypoints improves the accuracy of action classification in sumo through the experiment results.

Paper Citation

in Harvard Style

Utsuro Y., Shishido H. and Kameda Y. (2024). Sumo Action Classification Using Mawashi Keypoints. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 401-408. DOI: 10.5220/0012337000003660

in Bibtex Style

@conference{visapp24,
author={Yuki Utsuro and Hidehiko Shishido and Yoshinari Kameda},
title={Sumo Action Classification Using Mawashi Keypoints},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2024},
pages={401-408},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012337000003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Sumo Action Classification Using Mawashi Keypoints
SN - 978-989-758-679-8
AU - Utsuro Y.
AU - Shishido H.
AU - Kameda Y.
PY - 2024
SP - 401
EP - 408
DO - 10.5220/0012337000003660
PB - SciTePress