Dynamic Gesture Recognition Algorithm Based on 3D Convolutional Neural Network.
Journal
Computational intelligence and neuroscience
ISSN: 1687-5273
Titre abrégé: Comput Intell Neurosci
Pays: United States
ID NLM: 101279357
Informations de publication
Date de publication:
2021
2021
Historique:
received:
13
07
2021
accepted:
06
08
2021
entrez:
27
8
2021
pubmed:
28
8
2021
medline:
1
9
2021
Statut:
epublish
Résumé
Gesture recognition is one of the important ways of human-computer interaction, which is mainly detected by visual technology. The temporal and spatial features are extracted by convolution of the video containing gesture. However, compared with the convolution calculation of a single image, multiframe image of dynamic gestures has more computation, more complex feature extraction, and more network parameters, which affects the recognition efficiency and real-time performance of the model. To solve above problems, a dynamic gesture recognition model based on CBAM-C3D is proposed. Key frame extraction technology, multimodal joint training, and network optimization with BN layer are used for making the network performance better. The experiments show that the recognition accuracy of the proposed 3D convolutional neural network combined with attention mechanism reaches 72.4% on EgoGesture dataset, which is improved greatly compared with the current main dynamic gesture recognition methods, and the effectiveness of the proposed algorithm is verified.
Identifiants
pubmed: 34447430
doi: 10.1155/2021/4828102
pmc: PMC8384521
doi:
Types de publication
Journal Article
Retracted Publication
Langues
eng
Sous-ensembles de citation
IM
Pagination
4828102Commentaires et corrections
Type : RetractionIn
Informations de copyright
Copyright © 2021 Yuting Liu et al.
Déclaration de conflit d'intérêts
The authors declare that there are no conflicts of interest regarding the publication of this article.
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