Most motion tracking algorithms rely on an initial skeleton model that has already been fitted to a special posture setup. However, such a first identification of markers often requires multiple manual actions of a designer. To automate this process, a novel approach for adapting a basic skeleton model to empirical motion capture data is presented. The approach is based on the anthropometric dimensions of a subject and subsequent tree-based skeleton fitting. It generates a tree representation of different possible skeleton configurations. The tree is annotated with costs based on discrepancies between markers and anatomic landmarks. A computation of the least cost path through the tree automatically results in an optimal fitting of the observed markers to the given anthropometric data of the subject.
Enhancing Motion Capture Performance by Means of an Internal Anthropometric Skeleton Model
Sae Technical Papers
Digital Human Modeling for Design and Engineering Symposium ; 2008
2008-06-17
Conference paper
English
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