NDLI: Learning to estimate human pose with data driven belief propagation

Content Provider	IEEE Xplore Digital Library
Author	Gang Hua Ming-Hsuan Yang Ying Wu
Copyright Year	2005
Description	Author affiliation: ECE Dept., Northwestern Univ., Evanston, IL, USA (Gang Hua)
Abstract	We propose a statistical formulation for 2D human pose estimation from single images. The human body configuration is modeled by a Markov network and the estimation problem is to infer pose parameters from image cues such as appearance, shape, edge, and color. From a set of hand labeled images, we accumulate prior knowledge of 2D body shapes by learning their low-dimensional representations for inference of pose parameters. A data driven belief propagation Monte Carlo algorithm, utilizing importance sampling functions built from bottom-up visual cues, is proposed for efficient probabilistic inference. Contrasted to the few sequential statistical formulations in the literature, our algorithm integrates both top-down as well as bottom-up reasoning mechanisms, and can carry out the inference tasks in parallel. Experimental results demonstrate the potency and effectiveness of the proposed algorithm in estimating 2D human pose from single images.
Sponsorship	IEEE Comput. Soc
Starting Page	747
Ending Page	754
File Size	586506
Page Count	8
File Format	PDF
ISBN	0769523722
ISSN	10636919
DOI	10.1109/CVPR.2005.208
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Belief propagation Monte Carlo methods Inference algorithms Biological system modeling Shape Face detection Predictive models Markov random fields Head
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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