Posture-based and action-based graphs for boxing skill visualization

Yijun Shen, He Wang, Edmond S.l. Ho, Longzhi Yang, Hubert P.h. Shum

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
5 Downloads (Pure)

Abstract

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.
Original languageEnglish
Pages (from-to)104-115
JournalComputers & Graphics
Volume69
Early online date14 Oct 2017
DOIs
Publication statusPublished - Dec 2017

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