Spatial Measures between Human Poses for Classification and Understanding

  • Søren Hauberg
  • Kim Steenstrup Pedersen
Conference paper

DOI: 10.1007/978-3-642-31567-1_3

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7378)
Cite this paper as:
Hauberg S., Steenstrup Pedersen K. (2012) Spatial Measures between Human Poses for Classification and Understanding. In: Perales F.J., Fisher R.B., Moeslund T.B. (eds) Articulated Motion and Deformable Objects. AMDO 2012. Lecture Notes in Computer Science, vol 7378. Springer, Berlin, Heidelberg


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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

Personalised recommendations