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Shape-Motion Based Athlete Tracking for Multilevel Action Recognition

  • Costas Panagiotakis
  • Emmanuel Ramasso
  • Georgios Tziritas
  • Michèle Rombaut
  • Denis Pellerin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4069)

Abstract

An automatic human shape-motion analysis method based on a fusion architecture is proposed for human action recognition in videos. Robust shape-motion features are extracted from human points detection and tracking. The features are combined within the Transferable Belief Model (TBM) framework for action recognition. The TBM-based modelling and fusion process allows to take into account imprecision, uncertainty and conflict inherent to the features. Action recognition is performed by a multilevel analysis. The sequencing is exploited for feedback information extraction in order to improve tracking results. The system is tested on real videos of athletics meetings to recognize four types of jumps: high jump, pole vault, triple jump and long jump.

Keywords

Action Recognition Camera Motion Dynamic Bayesian Network Foreground Pixel Human Action Recognition 
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 2006

Authors and Affiliations

  • Costas Panagiotakis
    • 1
  • Emmanuel Ramasso
    • 2
  • Georgios Tziritas
    • 1
  • Michèle Rombaut
    • 2
  • Denis Pellerin
    • 2
  1. 1.Department of Computer ScienceUniversity of CreteHeraklionGreece
  2. 2.Laboratoire des Images et des SignauxGrenobleFrance

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