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Detecting end-effectors on 2.5D data using geometric deformable models: application to human pose estimation

Author
Suau, X.; Ruiz-Hidalgo, J.; Casas, J.
Type of activity
Journal article
Journal
Computer vision and image understanding
Date of publication
2013-03
Volume
117
Number
3
First page
281
Last page
288
DOI
https://doi.org/10.1016/j.cviu.2012.11.006 Open in new window
Repository
http://hdl.handle.net/2117/24311 Open in new window
URL
http://www.sciencedirect.com/science/article/pii/S1077314212001907 Open in new window
Abstract
End-effectors are usually related to the location of limbs, and their reliable detection enables robust body tracking as well as accurate pose estimation. Recent innovation in depth cameras has re-stated the pose estimation problem. We focus on the information provided by these sensors, for which we borrow the name 2.5D data from the Graphics community. In this paper we propose a human pose estimation algorithm based on topological propagation. Geometric Deformable Models are used to carry out s...
Citation
Suau, X.; Ruiz, J.; Casas, J. Detecting end-effectors on 2.5D data using geometric deformable models: application to human pose estimation. "Computer vision and image understanding", Març 2013, vol. 117, núm. 3, p. 281-288.
Keywords
Depth image, End-effector, Extremities, Human pose estimation, Range camera
Group of research
GPI - Image and Video Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants