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Capturing Natural Hand Articulation (2001)
| Venue: | In ICCV |
| Citations: | 114 - 10 self |
BibTeX
@INPROCEEDINGS{Wu01capturingnatural,
author = {Ying Wu and John Y. Lin and Thomas S. Huang},
title = {Capturing Natural Hand Articulation},
booktitle = {In ICCV},
year = {2001},
pages = {426--432}
}
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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.
Keyphrases
natural hand articulation hand articulation image observation hand state space model-based method hand natural constraint articulated motion efficient articulated motion-capturing algorithm high degree challenging task dimensional configuration space basis configuration model-based approach vision-based motion capturing hand motion constraint natural hand movement sequential monte carlo technique high dimensional hand state space hand model linear manifold
