Three Dimensional Monocular Human Motion Analysis in End-Effector Space

  • Søren Hauberg
  • Jerome Lapuyade
  • Morten Engell-Nørregård
  • Kenny Erleben
  • Kim Steenstrup Pedersen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5681)


In this paper, we present a novel approach to three dimensional human motion estimation from monocular video data. We employ a particle filter to perform the motion estimation. The novelty of the method lies in the choice of state space for the particle filter. Using a non-linear inverse kinematics solver allows us to perform the filtering in end-effector space. This effectively reduces the dimensionality of the state space while still allowing for the estimation of a large set of motions. Preliminary experiments with the strategy show good results compared to a full-pose tracker.


State Space Motion Estimation Human Motion Inverse Kinematic Visual Measurement 
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 2009

Authors and Affiliations

  • Søren Hauberg
    • 1
  • Jerome Lapuyade
    • 1
  • Morten Engell-Nørregård
    • 1
  • Kenny Erleben
    • 1
  • Kim Steenstrup Pedersen
    • 1
  1. 1.The eScience Center, Dept. of Computer ScienceUniversity of CopenhagenDenmark

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