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IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2
Dynamic Human Pose Estimation using Markov Chain Monte Carlo Approach
Breckenridge, Colorado
January 05-January 07
ISBN: 0-7695-2271-8
ASCII Text
x 
 
Mun Wai Lee, Ramakant Nevatia, "Dynamic Human Pose Estimation using Markov Chain Monte Carlo Approach," Applications of Computer Vision and the IEEE Workshop on Motion and Video Computing, IEEE Workshop on, vol. 2, pp. 168-175, IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2, 2005.
 
 
BibTex
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@article{ 10.1109/ACVMOT.2005.43,
author = {Mun Wai Lee and Ramakant Nevatia},
title = {Dynamic Human Pose Estimation using Markov Chain Monte Carlo Approach},
journal ={Applications of Computer Vision and the IEEE Workshop on Motion and Video Computing, IEEE Workshop on},
volume = {2},
year = {2005},
isbn = {0-7695-2271-8},
pages = {168-175},
doi = {http://doi.ieeecomputersociety.org/10.1109/ACVMOT.2005.43},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Applications of Computer Vision and the IEEE Workshop on Motion and Video Computing, IEEE Workshop on
TI - Dynamic Human Pose Estimation using Markov Chain Monte Carlo Approach
SN - 0-7695-2271-8
SP168
EP175
A1 - Mun Wai Lee,
A1 - Ramakant Nevatia,
PY - 2005
KW - null
VL - 2
JA - Applications of Computer Vision and the IEEE Workshop on Motion and Video Computing, IEEE Workshop on
ER -
 
Mun Wai Lee, University of Southern California, Los Angeles
Ramakant Nevatia, University of Southern California, Los Angeles
This paper addresses the problem of tracking human body pose in monocular video including automatic pose initialization and re-initialization after tracking failures caused by partial occlusion or unreliable observations. We proposed a method based on data-driven Markov chain Monte Carlo (DD-MCMC) that uses bottom-up techniques to generate state proposals for pose estimation and initialization. This method allows us to exploit different image cues and consolidate the inferences using a representation known as the proposal maps. We present experimental results with an indoor video sequence.
Citation:
Mun Wai Lee, Ramakant Nevatia, "Dynamic Human Pose Estimation using Markov Chain Monte Carlo Approach," wacv-motion, vol. 2, pp.168-175, IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2, 2005
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