Tracking Articulated Hand Motion with Eigen Dynamics Analysis
Abstract
This paper introduces the concept of eigen-dynamics and proposes an eigen dynamics analysis (EDA) method to learn the dynamics of natural hand motion from labelled sets of motion captured with a data glove. The result is parameterized with a high-order stochastic linear dynamic system (LDS) consisting of five lower-order LDS. Each corresponding to one eigen-dynamics. Based on the EDA model, we construct a dynamic Bayesian network (DBN) to analyze the generative process of a image sequence of natural hand motion. Using the DBN, a hand tracking system is implemented. Experiments on both synthesized and real-world data demonstrate the robustness and effectiveness of these techniques.
Cite
Text
Zhou and Huang. "Tracking Articulated Hand Motion with Eigen Dynamics Analysis." IEEE/CVF International Conference on Computer Vision, 2003. doi:10.1109/ICCV.2003.1238472Markdown
[Zhou and Huang. "Tracking Articulated Hand Motion with Eigen Dynamics Analysis." IEEE/CVF International Conference on Computer Vision, 2003.](https://mlanthology.org/iccv/2003/zhou2003iccv-tracking/) doi:10.1109/ICCV.2003.1238472BibTeX
@inproceedings{zhou2003iccv-tracking,
title = {{Tracking Articulated Hand Motion with Eigen Dynamics Analysis}},
author = {Zhou, Hanning and Huang, Thomas S.},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {2003},
pages = {1102-1109},
doi = {10.1109/ICCV.2003.1238472},
url = {https://mlanthology.org/iccv/2003/zhou2003iccv-tracking/}
}