Posture-based and Action-based Graphs for Boxing Skill Visualization

Shen, Yijun, Wang, He, Ho, Edmond, Yang, Longzhi and Shum, Hubert P. H. (2017) Posture-based and Action-based Graphs for Boxing Skill Visualization. Computers and Graphics. ISSN 0097-8493 (In Press)

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Automatic evaluation of sports skills has been an active research area. However, most of the existing research focuses on low-level features such as movement speed and strength. In this work, we propose a framework for automatic motion analysis and visualization, which allows us to evaluate high-level skills such as the richness of actions, the flexibility of transitions and the unpredictability of action patterns. The core of our framework is the construction and visualization of the posture-based graph that focuses on the standard postures for launching and ending actions, as well as the action-based graph that focuses on the preference of actions and their transition probability. We further propose two numerical indices, the Connectivity Index and the Action Strategy Index, to assess skill level according to the graph. We demonstrate our framework with motions captured from different boxers. Experimental results demonstrate that our system can effectively visualize the strengths and weaknesses of the boxers.

Item Type: Article
Uncontrolled Keywords: Motion graph, Hidden Markov model,, Information visualization, Dimensionality reduction, Human motion analysis, Boxing
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 26 Oct 2017 09:05
Last Modified: 26 Oct 2017 14:27

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