| Título: | Human Shape-Motion Analysis In Athletics Videos for Coarse To Fine Action/Activity Recognition Using Transferable Belief Model |
| Autores: |
Ramasso, Emmanuel Panagiotakis, Costas Rombaut, Michèle Pellerin, Denis Tziritas, Georgios |
| Fecha: | 2009-04-15 |
| Publicador: | Elcvia: electronics letters on computer vision and image analysis |
| Fuente: |
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| Tipo: | |
| Tema: | Video Analysis; Human Tracking; Action and Activity Recognition; Transferable Belief Model |
| Descripción: | We present an automatic human shape-motion analysis method based on a fusion architecture for humanaction and activity recognition in athletic videos. Robust shape and motion features are extracted fromhuman detection and tracking. The features are combined within the Transferable Belief Model (TBM)framework for two levels of recognition. The TBM-based modelling of the fusion process allows to takeinto account imprecision, uncertainty and conflict inherent to the features. First, in a coarse step, actions areroughly recognized. Then, in a fine step, an action sequence recognition method is used to discriminate activities.Belief on actions are made smooth by a Temporal Credal Filter and action sequences, i.e. activities,are recognized using a state machine, called belief scheduler, based on TBM. The belief scheduler is alsoexploited for feedback information extraction in order to improve tracking results. The system is tested onreal videos of athletics meetings to recognize four types of actions (running, jumping, falling and standing)and four types of activities (high jump, pole vault, triple jump and long jump). Results on actions, activitiesand feedback demonstrate the relevance of the proposed features and as well the efficiency of the proposedrecognition approach based on TBM.Key Words: Video Analysis, Human Tracking, Action and Activity Recognition, Transferable Belief Model. |
| Idioma: | Inglés |