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A 3D Dynamic Model of Human Actions for Probabilistic Image Tracking

  • Ignasi Rius
  • Daniel Rowe
  • Jordi Gonzàlez
  • Xavier Roca
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3522)

Abstract

In this paper we present a method suitable to be used for human tracking as a temporal prior in a particle filtering framework such as CONDENSATION [5]. This method is for predicting feasible human postures given a reduced set of previous postures and will drastically reduce the number of particles needed to track a generic high-articulated object. Given a sequence of preceding postures, this example-driven transition model probabilistically matches the most likely postures from a database of human actions. Each action of the database is defined within a PCA-like space called UaSpace suitable to perform the probabilistic match when searching for similar sequences. So different, but feasible postures of the database become the new predicted poses.

Keywords

Human Motion Human Posture Human Body Model Probabilistic Search Feasible Posture 
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 2005

Authors and Affiliations

  • Ignasi Rius
    • 1
  • Daniel Rowe
    • 1
  • Jordi Gonzàlez
    • 1
  • Xavier Roca
    • 1
  1. 1.Centre de Visió per Computador/Department of Computer ScienceUniversitat Autònoma de BarcelonaBellaterra, BarcelonaSpain

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