13 May 2017 Monocular three-dimensional human pose estimation using local-topology preserved sparse retrieval
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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.
© 2017 SPIE and IS&T
Jialin Yu, Jifeng Sun, Zhiguo Song, Shaoyin Zheng, Bingtian Wei, "Monocular three-dimensional human pose estimation using local-topology preserved sparse retrieval," Journal of Electronic Imaging 26(3), 033008 (13 May 2017). https://doi.org/10.1117/1.JEI.26.3.033008 . Submission: Received: 17 February 2017; Accepted: 19 April 2017
Received: 17 February 2017; Accepted: 19 April 2017; Published: 13 May 2017
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