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Learning Silhouette Features for Control of Human Motion (2004)
| Venue: | ACM Trans. Graph |
BibTeX
@ARTICLE{Carnegie04learningsilhouette,
author = {Liu Ren Carnegie and Liu Ren},
title = {Learning Silhouette Features for Control of Human Motion},
journal = {ACM Trans. Graph},
year = {2004},
volume = {24},
pages = {1303--1331}
}
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Abstract
features that are computed on the silhouette images [Viola and Jones 2001]. These features are computationally efficient and therefore suited to real-time applications. The best local features for estimating yaw and body configuration are selected from a very broad set of possible features based on a variant of the AdaBoost algorithm [Schapire and Singer 1999] (Figure 2). Synthetic data are used for the feature selection. The determination of the yaw orientation incorporates a fast search method called locality sensitive hashing. The determination of the body configuration (joint angles, root position, and orientation) relies on the temporal coherence in a domain-specific database of human motion. The database is pre-processed into an augmented motion graph that extends the motion graph [Lee et al. 2002] to handle scaling in velocity and two interacting characters. The search for the pose of the animated character is performed locally in the augmented motion graph in the region aroun
Keyphrases
human motion silhouette feature augmented motion graph body configuration motion graph lee root position synthetic data animated character region aroun domain-specific database temporal coherence feature selection real-time application broad set possible feature locality sensitive hashing fast search method adaboost algorithm schapire local feature joint angle yaw orientation
