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12

WACV
2005
IEEE

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Dynamic Human Pose Estimation using Markov Chain Monte Carlo Approach

13 years 10 months ago

Dynamic Human Pose Estimation using Markov Chain Monte Carlo Approach

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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.

Mun Wai Lee, Ramakant Nevatia

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Automatic Pose Initialization | Human Body Pose | Markov Chain Monte Carlo | WACV 2005 |

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Added	25 Jun 2010
Updated	25 Jun 2010
Type	Conference
Year	2005
Where	WACV
Authors	Mun Wai Lee, Ramakant Nevatia

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