GPU Accelerated Likelihoods for Stereo-Based Articulated Tracking

  • Rune Møllegaard Friborg
  • Søren Hauberg
  • Kenny Erleben
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6554)


For many years articulated tracking has been an active research topic in the computer vision community. While working solutions have been suggested, computational time is still problematic. We present a GPU implementation of a ray-casting based likelihood model that is orders of magnitude faster than a traditional CPU implementation. We explain the non-intuitive steps required to attain an optimized GPU implementation, where the dominant part is to hide the memory latency effectively. Benchmarks show that computations which previously required several minutes, are now performed in few seconds.


CUDA GPU Computing Articulated Tracking Particle Filtering 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Rune Møllegaard Friborg
    • 1
  • Søren Hauberg
    • 1
  • Kenny Erleben
    • 1
  1. 1.The eScience Centre, Dept. of Computer ScienceUniversity of CopenhagenDenmark

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