3D Hand Gesture Recognition by Analysing ...
Type de document :
Communication dans un congrès avec actes
Titre :
3D Hand Gesture Recognition by Analysing Set-of-Joints Trajectories
Auteur(s) :
de Smedt, Quentin [Auteur]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Wannous, Hazem [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Vandeborre, Jean Philippe [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Wannous, Hazem [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Vandeborre, Jean Philippe [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Titre de la manifestation scientifique :
International Conference on Pattern Recognition (ICPR) / UHA3DS 2016 workshop
Ville :
Cancun
Pays :
Mexique
Date de début de la manifestation scientifique :
2016-12-04
Mot(s)-clé(s) en anglais :
Gesture recognition
Riemaniann Manifold
Hand skeleton
Depth image
Riemaniann Manifold
Hand skeleton
Depth image
Discipline(s) HAL :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Résumé en anglais : [en]
Hand gesture recognition is recently becoming one of the most attractive field of research in Pattern Recognition. In this paper, a skeleton-based approach is proposed for 3D hand gesture recognition. Specifically, we ...
Lire la suite >Hand gesture recognition is recently becoming one of the most attractive field of research in Pattern Recognition. In this paper, a skeleton-based approach is proposed for 3D hand gesture recognition. Specifically, we consider the sequential data of hand geometric configuration to capture the hand shape variation, and explore the temporal character of hand motion. 3D Hand gesture are represented as a set of relevant spatiotemporal motion trajectories of hand-parts in an Euclidean space. Trajectories are then interpreted as elements lying on Riemannian manifold of shape space to capture their shape variations and achieve gesture recognition using a linear SVM classifier. The proposed approach is evaluated on a challenging hand gesture dataset containing 14 gestures, performed by 20 participants performing the same gesture with two di↵erent numbers of fingers. Experimental results show that our skeleton-based approach consistently achieves superior performance over a depth-based approach.Lire moins >
Lire la suite >Hand gesture recognition is recently becoming one of the most attractive field of research in Pattern Recognition. In this paper, a skeleton-based approach is proposed for 3D hand gesture recognition. Specifically, we consider the sequential data of hand geometric configuration to capture the hand shape variation, and explore the temporal character of hand motion. 3D Hand gesture are represented as a set of relevant spatiotemporal motion trajectories of hand-parts in an Euclidean space. Trajectories are then interpreted as elements lying on Riemannian manifold of shape space to capture their shape variations and achieve gesture recognition using a linear SVM classifier. The proposed approach is evaluated on a challenging hand gesture dataset containing 14 gestures, performed by 20 participants performing the same gesture with two di↵erent numbers of fingers. Experimental results show that our skeleton-based approach consistently achieves superior performance over a depth-based approach.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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- Desmedt-UHA3DS2016.pdf
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