Spatial Measures between Human Poses for Classification and Understanding

  • Søren Hauberg
  • Kim Steenstrup Pedersen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7378)


Statistical analysis of humans, their motion and their behaviour is a very well-studied problem. With the availability of accurate motion capture systems, it has become possible to use such analysis for animation, understanding, compression and tracking of human motion. At the core of the analysis lies a measure for determining the distance between two human poses; practically always, this measure is the Euclidean distance between joint angle vectors. Recent work [7] has shown that articulated tracking systems can be vastly improved by replacing the Euclidean distance in joint angle space with the geodesic distance in the space of joint positions. However, due to the focus on tracking, no algorithms have, so far, been presented for measuring these distances between human poses.

In this paper, we present an algorithm for computing geodesics in the Riemannian space of joint positions, as well as a fast approximation that allows for large-scale analysis. In the experiments we show that this measure significantly outperforms the traditional measure in classification, clustering and dimensionality reduction tasks.


Joint Angle Geodesic Distance Joint Position Latent Variable Model Geodesic Curve 
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 2012

Authors and Affiliations

  • Søren Hauberg
    • 1
  • Kim Steenstrup Pedersen
    • 2
  1. 1.Perceiving SystemsMax Planck Institute for Intelligent SystemsTübingenGermany
  2. 2.Dept. of Computer ScienceUniversity of CopenhagenCopenhagenDenmark

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