3D Hand Pose Detection in Egocentric {RGB-D} Images
We focus on the task of everyday hand pose estimation
from egocentric viewpoints. For this task, we show
that depth sensors are particularly informative for extracting
near-field interactions of the camera wearer with his/her environment.
Despite the recent advances in full-body pose estimation
using Kinect-like sensors, reliable monocular hand
pose estimation in RGB-D images is still an unsolved problem.
The problem is considerably exacerbated when analyzing
hands performing daily activities from a first-person
viewpoint, due to severe occlusions arising from object manipulations
and a limited field-of-view. Our system addresses
these difficulties by exploiting strong priors over viewpoint
and pose in a discriminative tracking-by-detection framework.
Our priors are operationalized through a photorealistic
synthetic model of egocentric scenes, which is used to
generate training data for learning depth-based pose classifiers.
We evaluate our approach on an annotated dataset
of real egocentric object manipulation scenes and compare
to both commercial and academic approaches. Our method
provides state-of-the-art performance for both hand detection
and pose estimation in egocentric RGB-D images.
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Text Reference
Grégory Rogez, James Steven Supan\vc i\vc , Maryam Khademi, J.M.M. Montiel, and Deva Ramanan. 3d hand pose detection in egocentric RGB-D images. CoRR, 2014. URL: http://arxiv.org/abs/1412.0065.BibTeX Reference
@article{RogezSKMR_ECCV_2014,author = "Rogez, Gr{\'e}gory and Supan{\vc}i{\vc}, James Steven and Khademi, Maryam and Montiel, J.M.M. and Ramanan, Deva",
title = "3D Hand Pose Detection in Egocentric {RGB-D} Images",
journal = "CoRR",
volume = "abs/1412.0065",
year = "2014",
url = "http://arxiv.org/abs/1412.0065",
tag = "people"
}