Shum, HPH, Wang, H orcid.org/0000-0002-2281-5679, Ho, ESL et al. (1 more author) (2016) SkillVis: A Visualization Tool for Boxing Skill Assessment. In: MIG '16 Proceedings of the 9th International Conference on Motion in Games. The 9th International Conference on Motion in Games (MIG '16), 10-12 Oct 2016, Burlingame, California, USA. ACM , New York, USA , pp. 145-153. ISBN 978-1-4503-4592-7
Abstract
Motion analysis and visualization are crucial in sports science for sports training and performance evaluation. While primitive computational methods have been proposed for simple analysis such as postures and movements, few can evaluate the high-level quality of sports players such as their skill levels and strategies. We propose a visualization tool to help visualizing boxers' motions and assess their skill levels. Our system automatically builds a graph-based representation from motion capture data and reduces the dimension of the graph onto a 3D space so that it can be easily visualized and understood. In particular, our system allows easy understanding of the boxer's boxing behaviours, preferred actions, potential strength and weakness. We demonstrate the effectiveness of our system on different boxers' motions. Our system not only serves as a tool for visualization, it also provides intuitive motion analysis that can be further used beyond sports science.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2016 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License (https://creativecommons.org/licenses/by/4.0/) |
Keywords: | Motion Graph, Information Visualization, Dimensionality Reduction |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Institute for Computational and Systems Science (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 21 Oct 2016 10:53 |
Last Modified: | 23 Jun 2023 22:15 |
Published Version: | https://doi.org/10.1145/2994258.2994266 |
Status: | Published |
Publisher: | ACM |
Identification Number: | 10.1145/2994258.2994266 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:106266 |