Improved Estimation of Hand Postures Using Depth Images
Proceedings of the 16th International Conference on Advanced Robotics (ICAR 2013),
pages 1--6,
doi: 10.1109/ICAR.2013.6766485
- Sep 2013
Associated documents :
Hand pose estimation is the task of deriving a
hands articulation from sensory input, here depth images
in particular. A novel approach states pose estimation as an
optimization problem: a high-dimensional hypothesis space is
constructed from a hand model, in which particle swarms search
for the best pose hypothesis. We propose various additions to
this approach. Our extended hand model includes anatomical
constraints of hand motion by applying principal component
analysis (PCA). This allows us to treat pose estimation as a
problem with variable dimensionality. The most important benefit
becomes visible once our PCA-enhanced model is combined with
biased particle swarms. Several experiments show that accuracy
and performance of pose estimation improve significantly.
@InProceedings{HJW13,
author = {Hamester, Dennis and Jirak, Doreen and Wermter, Stefan},
title = {Improved Estimation of Hand Postures Using Depth Images},
booktitle = {Proceedings of the 16th International Conference on Advanced Robotics (ICAR 2013)},
editors = {},
number = {},
volume = {},
pages = {1--6},
year = {2013},
month = {Sep},
publisher = {IEEE},
doi = {10.1109/ICAR.2013.6766485},
}