Data-Driven Importance Distributions for Articulated Tracking

  • Søren Hauberg
  • Kim Steenstrup Pedersen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6819)


We present two data-driven importance distributions for particle filter-based articulated tracking; one based on background subtraction, another on depth information. In order to keep the algorithms efficient, we represent human poses in terms of spatial joint positions. To ensure constant bone lengths, the joint positions are confined to a non-linear representation manifold embedded in a high-dimensional Euclidean space. We define the importance distributions in the embedding space and project them onto the representation manifold. The resulting importance distributions are used in a particle filter, where they improve both accuracy and efficiency of the tracker. In fact, they triple the effective number of samples compared to the most commonly used importance distribution at little extra computational cost.


Articulated tracking Importance Distributions Particle Filtering Spatial Human Motion Models 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

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

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