Shape-Motion based athlete tracking for multilevel action recognition

Authors:
Costas Panagiotakis;Emmanuel Ramasso;Georgios Tziritas;Michèle Rombaut;Denis Pellerin
Affiliations:
Department of Computer Science, University of Crete, Heraklion, Greece;Laboratoire des Images et des Signaux, Grenoble, France;Department of Computer Science, University of Crete, Heraklion, Greece;Laboratoire des Images et des Signaux, Grenoble, France;Laboratoire des Images et des Signaux, Grenoble, France
Venue:
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Year:
2006

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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