Human Shape-Motion Analysis In Athletics Videos for Coarse To Fine Action/Activity Recognition Using Transferable BeliefModel
Ramasso, Emmanuel (GIPSA-lab. Images and Signal Department (Grenoble, França))
Panagiotakis, Costas (University of Crete (Heraklion, Grècia). Department of Computer Science)
Rombaut, Michèle (GIPSA-lab. Images and Signal Department (Grenoble, França))
Pellerin, Denis (GIPSA-lab. Images and Signal Department (Grenoble, França))
Tziritas, Georgios (University of Crete (Heraklion, Grècia). Department of Computer Science)

Fecha:	2008
Resumen:	We present an automatic human shape-motion analysis method based on a fusion architecture for human action and activity recognition in athletic videos. Robust shape and motion features are extracted from human 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 take into account imprecision, uncertainty and conflict inherent to the features. First, in a coarse step, actions are roughly 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 also 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 actions (running, jumping, falling and standing) and four types of activities (high jump, pole vault, triple jump and long jump). Results on actions, activities and feedback demonstrate the relevance of the proposed features and as well the efficiency of the proposed recognition approach based on TBM.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Anàlisi de Vídeo ; Seguiment Humà ; Reconeixement d'acció i activitat ; Model de Creences transferibles ; Análisis de Vídeo ; Seguimiento Humano ; Reconocimiento de acción y actividad ; Modelo de Creencias transferibles ; Video Analysis ; Human Tracking ; Action and Activity Recognition ; Transferable Belief Model
Publicado en:	ELCVIA : Electronic Letters on Computer Vision and Image Analysis, V. 7 n. 4 (2008) p. 32-50, ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.es/article/view/v7-n4-ramasso-panagiotakis-rombaut-et-al
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/132010
DOI: 10.5565/rev/elcvia.163

19 p, 658.6 KB

El registro aparece en las colecciones:
Artículos > Artículos publicados > ELCVIA
Artículos > Artículos de investigación