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Monocular Pose Capture with a Depth Camera Using a Sums-of-Gaussians Body Model

  • Daniyar Kurmankhojayev
  • Nils Hasler
  • Christian Theobalt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8142)

Abstract

We present a new markerless generative approach for Human Motion Tracking using a single depth camera. It is based on a Sums of Spatial Gaussians (SoGs) representation for modeling the scene. In contrast to existing systems our approach does not require a multi-view camera setup, exemplar database or training data. The proposed system is accurate, fast and capable of tracking complex motions including 360° turns and self-occlusion of limited duration. The motivation behind our approach is that representing the depth data and a given a priori human model by a SoGs, we can construct an efficient continuously differentiable similarity measure and estimate an optimal pose for each input frame using a local optimization algorithm (Modified Gradient Ascent Linear Search, MGALS).

Keywords

Human Model Depth Image Body Model Depth Camera Camera Intrinsic Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Daniyar Kurmankhojayev
    • 1
  • Nils Hasler
    • 1
  • Christian Theobalt
    • 1
  1. 1.MPI InformatikGermany

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