Skeleton-Based Dynamic Hand Gesture Recognition
Type de document :
Communication dans un congrès avec actes
DOI :
Titre :
Skeleton-Based Dynamic Hand Gesture Recognition
Auteur(s) :
de Smedt, Quentin [Inventeur (brevet)]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Institut TELECOM/TELECOM Lille1
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Wannous, Hazem [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Institut TELECOM/TELECOM Lille1
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]
Institut TELECOM/TELECOM Lille1
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Institut TELECOM/TELECOM Lille1
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Wannous, Hazem [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Institut TELECOM/TELECOM Lille1
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]
Institut TELECOM/TELECOM Lille1
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Titre de la manifestation scientifique :
Computer Vision and Pattern Recognition Workshops (CVPRW), 2016 IEEE Conference on
Ville :
Las Vegas
Pays :
Etats-Unis d'Amérique
Date de début de la manifestation scientifique :
2016-06-26
Date de publication :
2016-07-01
Discipline(s) HAL :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Résumé en anglais : [en]
In this paper, a new skeleton-based approach is proposed for 3D hand gesture recognition. Specifically, we exploit the geometric shape of the hand to extract an effective de-scriptor from hand skeleton connected joints ...
Lire la suite >In this paper, a new skeleton-based approach is proposed for 3D hand gesture recognition. Specifically, we exploit the geometric shape of the hand to extract an effective de-scriptor from hand skeleton connected joints returned by the Intel RealSense depth camera. Each descriptor is then encoded by a Fisher Vector representation obtained using a Gaussian Mixture Model. A multi-level representation of Fisher Vectors and other skeleton-based geometric features is guaranteed by a temporal pyramid to obtain the final feature vector, used later to achieve the classification by 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 different 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 >In this paper, a new skeleton-based approach is proposed for 3D hand gesture recognition. Specifically, we exploit the geometric shape of the hand to extract an effective de-scriptor from hand skeleton connected joints returned by the Intel RealSense depth camera. Each descriptor is then encoded by a Fisher Vector representation obtained using a Gaussian Mixture Model. A multi-level representation of Fisher Vectors and other skeleton-based geometric features is guaranteed by a temporal pyramid to obtain the final feature vector, used later to achieve the classification by 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 different 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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- DeSmedtCVPRW2016.pdf
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