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Communication Dans Un Congrès Année : 2006

Shape-Motion Based Athlete Tracking for Multilevel Action Recognition

Costas Panagiotakis
  • Fonction : Auteur
  • PersonId : 833272
Emmanuel Ramasso
  • Fonction : Auteur
  • PersonId : 833271
Georgios Tziritas
  • Fonction : Auteur
  • PersonId : 833273
Michèle Rombaut
Denis Pellerin

Résumé

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 con°ict 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.
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Dates et versions

hal-00068006 , version 1 (10-05-2006)

Identifiants

Citer

Costas Panagiotakis, Emmanuel Ramasso, Georgios Tziritas, Michèle Rombaut, Denis Pellerin. Shape-Motion Based Athlete Tracking for Multilevel Action Recognition. 4th Conference on Articulated Motion and Deformable Objects, AMDO'06, 2006, Mallorca, Spain. pp.385-394, ⟨10.1007/11789239_40⟩. ⟨hal-00068006⟩
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