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Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications pp 149–187Cite as

A Survey on Human Motion Analysis from Depth Data

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8200))

Abstract

Human pose estimation has been actively studied for decades. While traditional approaches rely on 2d data like images or videos, the development of Time-of-Flight cameras and other depth sensors created new opportunities to advance the field. We give an overview of recent approaches that perform human motion analysis which includes depth-based and skeleton-based activity recognition, head pose estimation, facial feature detection, facial performance capture, hand pose estimation and hand gesture recognition. While the focus is on approaches using depth data, we also discuss traditional image based methods to provide a broad overview of recent developments in these areas.

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Authors and Affiliations

  1. University of Kentucky, 329 Rose St., Lexington, KY, 40508, U.S.A.

    Mao Ye, Qing Zhang & Ruigang Yang

  2. Microsoft, One Microsoft Way, Redmond, WA, 98052, U.S.A.

    Liang Wang

  3. SRI International Sarnoff, 201 Washington Rd, Princeton, NJ, 08540, U.S.A.

    Jiejie Zhu

  4. University of Bonn, Roemerstrasse 164, 53117, Bonn, Germany

    Juergen Gall

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  2. Qing Zhang
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  1. Pattern Recognition Group, University of Siegen, Siegen, Germany

    Marcin Grzegorzek

  2. Max-lanck-Institute, Graphics, Vision & Video Gruop, Saarbrücken, Germany

    Christian Theobalt

  3. Multimedia Information Processing Group, University of Kiel, Kiel, Germany

    Reinhard Koch

  4. Computer Graphics and Multimedia Systems Group, University of Siegen, Siegen, Germany

    Andreas Kolb

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Ye, M., Zhang, Q., Wang, L., Zhu, J., Yang, R., Gall, J. (2013). A Survey on Human Motion Analysis from Depth Data. In: Grzegorzek, M., Theobalt, C., Koch, R., Kolb, A. (eds) Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications. Lecture Notes in Computer Science, vol 8200. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44964-2_8

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