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Gaussian-Like Spatial Priors for Articulated Tracking

  • Søren Hauberg
  • Stefan Sommer
  • Kim Steenstrup Pedersen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6311)

Abstract

We present an analysis of the spatial covariance structure of an articulated motion prior in which joint angles have a known covariance structure. From this, a well-known, but often ignored, deficiency of the kinematic skeleton representation becomes clear: spatial variance not only depends on limb lengths, but also increases as the kinematic chains are traversed. We then present two similar Gaussian-like motion priors that are explicitly expressed spatially and as such avoids any variance coming from the representation. The resulting priors are both simple and easy to implement, yet they provide superior predictions.

Keywords

Tangent Space Joint Angle Kinematic Chain Average Standard Deviation Likelihood Model 
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 2010

Authors and Affiliations

  • Søren Hauberg
    • 1
  • Stefan Sommer
    • 1
  • Kim Steenstrup Pedersen
    • 1
  1. 1.The eScience Centre, Dept. of Computer ScienceUniversity of Copenhagen 

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