• DocumentCode
    3207543
  • Title

    View independent human body pose estimation from a single perspective image

  • Author

    Parameswaran, Vasu ; Chellappa, Rama

  • Author_Institution
    Center for Autom. Res., Maryland Univ., College Park, MD, USA
  • Volume
    2
  • fYear
    2004
  • fDate
    27 June-2 July 2004
  • Abstract
    Recovering the 3D coordinates of various joints of the human body from an image is a critical first step for several model-based human tracking and optical motion capture systems. Unlike previous approaches that have used a restrictive camera model or assumed a calibrated camera, our work deals with the general case of a perspective uncalibrated camera and is thus well suited for archived video. The input to the system is an image of the human body and correspondences of several body landmarks, while the output is the set of 3D coordinates of the landmarks in a body-centric coordinate system. Using ideas from 3D model based invariants, we set up a polynomial system of equations in the unknown head pitch, yaw and roll angles. If we are able to make the often-valid assumption that the torso twist is small, there are finite numbers of solutions to the head-orientation that can be computed readily. Once the head orientation is computed, the epipolar geometry of the camera is recovered, leading to solutions to the 3D joint positions. Results are presented on synthetic and real images.
  • Keywords
    biomechanics; image motion analysis; object detection; physiological models; polynomials; 3D coordinates; body-centric coordinate system; epipolar geometry; equation polynomial system; human body pose estimation; model-based human tracking; optical motion capture systems; perspective uncalibrated camera; real images; single perspective image; synthetic images; torso twist; Biological system modeling; Cameras; Computational geometry; Equations; Head; Humans; Joints; Polynomials; Torso; Tracking;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on
  • ISSN
    1063-6919
  • Print_ISBN
    0-7695-2158-4
  • Type

    conf

  • DOI
    10.1109/CVPR.2004.1315139
  • Filename
    1315139