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3D human pose estimation from image using couple sparse coding

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Abstract

Recent studies have demonstrated that high-level semantics in data can be captured using sparse representation. In this paper, we propose an approach to human body pose estimation in static images based on sparse representation. Given a visual input, the objective is to estimate 3D human body pose using feature space information and geometrical information of the pose space. On the assumption that each data point and its neighbors are likely to reside on a locally linear patch of the underlying manifold, our method learns the sparse representation of the new input using both feature and pose space information and then estimates the corresponding 3D pose by a linear combination of the bases of the pose dictionary. Two strategies for dictionary construction are presented: (i) constructing the dictionary by randomly selecting the frames of a sequence and (ii) selecting specific frames of a sequence as dictionary atoms. We analyzed the effect of each strategy on the accuracy of pose estimation. Extensive experiments on datasets of various human activities show that our proposed method outperforms state-of-the-art methods.

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Notes

  1. BVH format created by Biovision Company to describing 3D pose in animation production. http://www.cs.wisc.edu/graphics/Courses/cs-838-1999/Jeff/BVH.html

  2. http://www.poser.com

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Authors and Affiliations

  1. Sharif University of Technology, Tehran, Iran

    Mohammadreza Zolfaghari, Amin Jourabloo, Bahman Pedrood & Mohammad T. Manzuri-Shalmani

  2. Amirkabir University of Technology, Tehran, Iran

    Samira Ghareh Gozlou

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  1. Mohammadreza Zolfaghari
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  2. Amin Jourabloo
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Correspondence to Mohammadreza Zolfaghari.

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Zolfaghari, M., Jourabloo, A., Gozlou, S.G. et al. 3D human pose estimation from image using couple sparse coding. Machine Vision and Applications 25, 1489–1499 (2014). https://doi.org/10.1007/s00138-014-0613-6

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  • DOI: https://doi.org/10.1007/s00138-014-0613-6

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