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Boosted Multiple Deformable Trees for Parsing Human Poses

  • Conference paper
Human Motion – Understanding, Modeling, Capture and Animation (HuMo 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4814))

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Abstract

Tree-structured models have been widely used for human pose estimation, in either 2D or 3D. While such models allow efficient learning and inference, they fail to capture additional dependencies between body parts, other than kinematic constraints. In this paper, we consider the use of multiple tree models, rather than a single tree model for human pose estimation. Our model can alleviate the limitations of a single tree-structured model by combining information provided across different tree models. The parameters of each individual tree model are trained via standard learning algorithms in a single tree-structured model. Different tree models are combined in a discriminative fashion by a boosting procedure. We present experimental results showing the improvement of our model over previous approaches on a very challenging dataset.

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Authors and Affiliations

  1. School of Computing Science, Simon Fraser University, Burnaby, BC, Canada

    Yang Wang & Greg Mori

Authors
  1. Yang Wang
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  2. Greg Mori
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Editor information

Ahmed Elgammal Bodo Rosenhahn Reinhard Klette

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© 2007 Springer-Verlag Berlin Heidelberg

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Wang, Y., Mori, G. (2007). Boosted Multiple Deformable Trees for Parsing Human Poses. In: Elgammal, A., Rosenhahn, B., Klette, R. (eds) Human Motion – Understanding, Modeling, Capture and Animation. HuMo 2007. Lecture Notes in Computer Science, vol 4814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75703-0_2

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  • DOI: https://doi.org/10.1007/978-3-540-75703-0_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75702-3

  • Online ISBN: 978-3-540-75703-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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