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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 1/2019

09.10.2018 | Machine Learning Applications for Self-Organized Wireless Networks

Jointly network: a network based on CNN and RBM for gesture recognition

verfasst von: Wentao Cheng, Ying Sun, Gongfa Li, Guozhang Jiang, Honghai Liu

Erschienen in: Neural Computing and Applications | Sonderheft 1/2019

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Abstract

Hand belongs to non-rigid objects and is rich in variety, making gesture recognition more difficult. The essence of dynamic gesture recognition is the classification and recognition of single-frame still images. Therefore, this paper mainly focuses on static gesture recognition. At present, there are some problems in gesture recognition, such as accuracy, real-time or poor robustness. To solve the above problems, in this paper, the Kinect sensor is used to obtain the color and depth gesture samples, and the gesture samples are processed. On this basis, a jointly network of CNN and RBM is proposed for gesture recognition. It mainly uses superposed network of multiple RBMs to carry out unsupervised feature extraction and combined with supervised feature extraction of CNN. Finally, these two features are combined to classify them. The simulation results show that the proposed jointly network has a better performance in identifying simple background gesture samples and the recognition capability of gesture samples in complex background needs to be improved.
Vorheriger Artikel A secure cloud-based solution for real-time monitoring and management of Internet of underwater things (IOUT)
Nächster Artikel An improved image mixed noise removal algorithm based on super-resolution algorithm and CNN

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Metadaten
Titel
Jointly network: a network based on CNN and RBM for gesture recognition
verfasst von
Wentao Cheng
Ying Sun
Gongfa Li
Guozhang Jiang
Honghai Liu
Publikationsdatum
09.10.2018
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe Sonderheft 1/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3775-8

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