Capturing natural hand articulation

Y. Wu*, J. Y. Lin, T. S. Huang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

184 Scopus citations

Abstract

Vision-based motion capturing of hand articulation is a challenging task, since the hand presents a motion of high degrees of freedom. Model-based approaches could be taken to approach this problem by searching in a high dimensional hand state space, and matching projections of a hand model and image observations. However, it is highly inefficient due to the curse of dimensionality. Fortunately, natural hand articulation is highly constrained, which largely reduces the dimensionality of hand state space. This paper presents a model-based method to capture hand articulation by learning hand natural constraints. Our study shows that natural hand articulation lies in a lower dimensional configurations space characterized by a union of linear manifolds spanned by a set of basis configurations. By integrating hand motion constraints, an efficient articulated motion-capturing algorithm is proposed based on sequential Monte Carlo techniques. Our experiments show that this algorithm is robust and accurate for tracking natural hand movements. This algorithm is easy to extend to other articulated motion capturing tasks.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE International Conference on Computer Vision
Pages426-432
Number of pages7
Volume2
StatePublished - Jan 1 2001
Event8th International Conference on Computer Vision - Vancouver, BC, United States
Duration: Jul 9 2001Jul 12 2001

Other

Other8th International Conference on Computer Vision
Country/TerritoryUnited States
CityVancouver, BC
Period7/9/017/12/01

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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