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Human Pose Estimation and Tracking

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Time-of-Flight and Structured Light Depth Cameras

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Abstract

Human pose estimation and tracking is one of the most intriguing yet challenging applications of consumer depth cameras. After an overview of common human hand and body models, we introduce approaches for pose recovery from a single frame, starting from the popular method based on Random Decision Forests proposed by Shotton et al. and used for the Microsoft Kinect. Various pose tracking approaches are then presented to recover the human pose configuration over time from a sequence of frames. We discuss some of the main solutions available today, including algorithms based on numerical optimization methods, filtering approaches, and recent advances based on Markov Random Fields.

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Author information

Authors and Affiliations

  1. Department of Information Engineering, University of Padova, Padova, Italy

    Pietro Zanuttigh, Giulio Marin, Fabio Dominio & Ludovico Minto

  2. Aquifi Inc., Palo Alto, CA, USA

    Carlo Dal Mutto

  3. 3D Everywhere s.r.l., Padova, Italy

    Guido Maria Cortelazzo

Authors
  1. Pietro Zanuttigh
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  2. Giulio Marin
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  3. Carlo Dal Mutto
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  4. Fabio Dominio
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  5. Ludovico Minto
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  6. Guido Maria Cortelazzo
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Zanuttigh, P., Marin, G., Dal Mutto, C., Dominio, F., Minto, L., Cortelazzo, G.M. (2016). Human Pose Estimation and Tracking. In: Time-of-Flight and Structured Light Depth Cameras. Springer, Cham. https://doi.org/10.1007/978-3-319-30973-6_8

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  • DOI: https://doi.org/10.1007/978-3-319-30973-6_8

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  • Print ISBN: 978-3-319-30971-2

  • Online ISBN: 978-3-319-30973-6

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