SHREC'17 Track: 3D Hand Gesture Recognition Using a Depth and Skeletal Dataset

de Smedt, Quentin; Wannous, Hazem; Vandeborre, Jean Philippe; Guerry, Joris; Le Saux, Bertrand; Filliat, David

Document type :

Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes

DOI :

10.2312/3dor.20171049

Title :

SHREC'17 Track: 3D Hand Gesture Recognition Using a Depth and Skeletal Dataset

Author(s) :

de Smedt, Quentin [Auteur]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Lille Douai]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Wannous, Hazem [Auteur]

Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Lille Douai]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Guerry, Joris [Auteur]
ONERA, Université Paris Saclay (COmUE) [Palaiseau]
Le Saux, Bertrand [Auteur]
ONERA, Université Paris Saclay (COmUE) [Palaiseau]
Filliat, David [Auteur]
Unité d'Informatique et d'Ingénierie des Systèmes [U2IS]
Flowing Epigenetic Robots and Systems [Flowers]

Conference title :

3DOR - 10th Eurographics Workshop on 3D Object Retrieval

City :

Lyon

Country :

France

Start date of the conference :

2017-04-23

Publication date :

2017

English keyword(s) :

machine learning
computer graphic
Gesture recognition

HAL domain(s) :

Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]

English abstract : [en]

Hand gesture recognition is recently becoming one of the most attractive field of research in pattern recognition. The objective of this track is to evaluate the performance of recent recognition approaches using a challenging ...
Show more >Hand gesture recognition is recently becoming one of the most attractive field of research in pattern recognition. The objective of this track is to evaluate the performance of recent recognition approaches using a challenging hand gesture dataset containing 14 gestures, performed by 28 participants executing the same gesture with two different numbers of fingers. Two research groups have participated to this track, the accuracy of their recognition algorithms have been evaluated and compared to three other state-of-the-art approaches.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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