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Journal of Electronic Imaging

Monocular three-dimensional human pose estimation using local-topology preserved sparse retrieval
Author(s): Jialin Yu; Jifeng Sun; Zhiguo Song; Shaoyin Zheng; Bingtian Wei
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Paper Abstract

Estimating three-dimensional (3-D) pose from a single image is usually performed by retrieving pose candidates with two-dimensional (2-D) features. However, pose retrieval usually relies on the acquisition of sufficient labeled data and suffers from low retrieving accuracy. Acquiring a large amount of unconstrained 2-D images annotated with 3-D poses is difficult. To solve these issues, we propose a coupled-source framework that integrates two independent training sources. The first source contains only 3-D poses, and the second source contains images annotated with 2-D poses. For accurate retrieval, we present a local-topology preserved sparse coding (LTPSC) to generate pose candidates, where the estimated 2-D pose of a test image is regarded as features for pose retrieval and represented as a sparse combination of features in the exemplar database. Our LTPSC can ensure that the semantically similar poses are retrieved with larger probabilities. Extensive experiments validate the effectiveness of our method.

Paper Details

Date Published: 13 May 2017
PDF: 15 pages
J. Electron. Imag. 26(3) 033008 doi: 10.1117/1.JEI.26.3.033008
Published in: Journal of Electronic Imaging Volume 26, Issue 3
Show Author Affiliations
Jialin Yu, South China Univ. of Technology (China)
Jifeng Sun, South China Univ. of Technology (China)
Zhiguo Song, South China Univ. of Technology (China)
Shaoyin Zheng, South China Univ. of Technology (China)
Bingtian Wei, South China Univ. of Technology (China)


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