 |
 |
Deep Learning for Hand Gesture Recognition on Skeletal Data
Abstract : — In this paper, we introduce a new 3D hand gesture recognition approach based on a deep learning model. We introduce a new Convolutional Neural Network (CNN) where sequences of hand-skeletal joints' positions are processed by parallel convolutions; we then investigate the performance of this model on hand gesture sequence classification tasks. Our model only uses hand-skeletal data and no depth image. Experimental results show that our approach achieves a state-of-the-art performance on a challenging dataset (DHG dataset from the SHREC 2017 3D Shape Retrieval Contest), when compared to other published approaches. Our model achieves a 91.28% classification accuracy for the 14 gesture classes case and an 84.35% classification accuracy for the 28 gesture classes case.
Littérature citée [47 références]
https://hal-mines-paristech.archives-ouvertes.fr/hal-01737771
Contributeur : Fabien Moutarde <>
Soumis le : jeudi 22 mars 2018 - 16:02:17
Dernière modification le : vendredi 6 mars 2020 - 01:14:08
Document(s) archivé(s) le : mardi 11 septembre 2018 - 08:24:42
Contributeur : Fabien Moutarde <>
Soumis le : jeudi 22 mars 2018 - 16:02:17
Dernière modification le : vendredi 6 mars 2020 - 01:14:08
Document(s) archivé(s) le : mardi 11 septembre 2018 - 08:24:42
Fichier
DeepLearning-HandSkeletalGestu...
Fichiers produits par l'(les) auteur(s)
