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3D Dynamic Pose Estimation from Markerless Optical Data

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Abstract

This chapter provides an overview of three-dimensional (3D) dynamic Pose (position and orientation) estimation of human movement without the use of markers or sensors, more commonly known as Markerless Motion Capture (Markerless Mocap). As with Marker-based Motion Capture (Marker-based Mocap), the methods presented estimate the Pose of an underlying multibody subject-specific model comprising rigid segments with anatomically defined local reference frames and joint constraints. In addition, the model has an overlying surface representing the skin, or clothing, depending on the context.

The focus of this chapter is on Markerless Mocap algorithms best suited to biomechanical analyses of human movement. In other words, those techniques appropriate for estimating 3D Pose directly, and accurately, from recorded data. Of all the approaches to Markerless Mocap, 3D-to-3D Pose estimation is most similar to Marker-based Mocap techniques because it requires arrays of multiple, time synchronous, video cameras encircling the capture volume. In addition to the underlying multibody skeletal model that marker-based and markerless techniques have in common, during Markerless Mocap, the subject is identified by a surface model overlying the skeleton. In each frame of motion data, a pixelated surface, comprised of a dense collection of points lying on the surface, is extracted from the scene and registered to the model.

Neither marker-based nor 3D-to-3D Markerless Mocap is typically accurate enough to record the Pose of the bones at a resolution for studying joint dynamics. An alternative markerless approach to joint level biomechanics has emerged. Biplanar videogradiography (or Dynamic Stereo X-ray) uses a 3D-to-2D approach to Markerless Mocap, whereby only two views of the subject are acquired because of space limitations and to minimize radiation exposure. A brief introduction to 3D-to-2D registration will be presented because this is covered in more detail in another chapter.

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Author information

Authors and Affiliations

  1. KinaTrax Inc., Palm Beach, FL, USA

    Steven Cadavid

  2. HAS-Motion Inc, Kingston, ON, Canada

    W. Scott Selbie

  3. C-Motion Inc., Germantown, MD, USA

    W. Scott Selbie

Authors
  1. Steven Cadavid
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  2. W. Scott Selbie
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Correspondence to Steven Cadavid .

Editor information

Editors and Affiliations

  1. Motion & More, Barcelona, Spain

    Bertram Müller

  2. Klinik für Orthopädie und Unfallchirurgi, Universitätsklinikum Heidelberg, Heidelberg, Germany

    Sebastian I. Wolf

  3. Institut for Biomechanics und Orthopedic, German Sport University Cologne, Cologne, Germany

    Gert-Peter Brueggemann

  4. Houston, Texas, USA

    Zhigang Deng

  5. ACRISP, FedUniversity, Cremorne, New South Wales, Australia

    Andrew McIntosh

  6. Wilmington, Delaware, USA

    Freeman Miller

  7. Research, C-Motion, Inc., Germantown, Maryland, USA

    William Scott Selbie

Section Editor information

  1. Has-Motion Inc., 27 Sydenham Street, K7L3G8, Kingston, ON, Canada

    William Scott Selbie

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Cadavid, S., Selbie, W.S. (2018). 3D Dynamic Pose Estimation from Markerless Optical Data. In: Müller, B., et al. Handbook of Human Motion. Springer, Cham. https://doi.org/10.1007/978-3-319-30808-1_160-2

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  • DOI: https://doi.org/10.1007/978-3-319-30808-1_160-2

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  • Print ISBN: 978-3-319-30808-1

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  1. Latest

    3D Dynamic Pose Estimation from Markerless Optical Data
    Published:
    13 October 2017

    DOI: https://doi.org/10.1007/978-3-319-30808-1_160-2

  2. Original

    3D Dynamic Pose Estimation from Markerless Optical Data
    Published:
    17 July 2017

    DOI: https://doi.org/10.1007/978-3-319-30808-1_160-1

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