Hand Gesture Recognition in Video Sequences Using Deep Convolutional and Recurrent Neural Networks

Falah Obaid*, Amin Babadi, Ahmad Yoosofan

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

368 Lataukset (Pure)

Abstrakti

Deep learning is a new branch of machine learning, which is widely used by researchers in a lot of artificial intelligence applications, including signal processing and computer vision. The present research investigates the use of deep learning to solve the hand gesture recognition (HGR) problem and proposes two models using deep learning architecture. The first model comprises a convolutional neural network (CNN) and a recurrent neural network with a long short-term memory (RNN-LSTM). The accuracy of model achieves up to 82 % when fed by colour channel, and 89 % when fed by depth channel. The second model comprises two parallel convolutional neural networks, which are merged by a merge layer, and a recurrent neural network with a long short-term memory fed by RGB-D. The accuracy of the latest model achieves up to 93 %.

AlkuperäiskieliEnglanti
Sivut57-61
Sivumäärä5
JulkaisuApplied Computer Systems
Vuosikerta25
Numero1
DOI - pysyväislinkit
TilaJulkaistu - toukok. 2020
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Sormenjälki

Sukella tutkimusaiheisiin 'Hand Gesture Recognition in Video Sequences Using Deep Convolutional and Recurrent Neural Networks'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä