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AMDO
2006
Springer

Shape-Motion Based Athlete Tracking for Multilevel Action Recognition

10 years 3 months ago
Shape-Motion Based Athlete Tracking for Multilevel Action Recognition
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.
Costas Panagiotakis, Emmanuel Ramasso, Georgios Tz
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2006
Where AMDO
Authors Costas Panagiotakis, Emmanuel Ramasso, Georgios Tziritas, Michèle Rombaut, Denis Pellerin
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