Hand Gesture Recognition Based on Single-Shot Multibox Detector Deep Learning
Joint Authors
Liu, Peng
Li, Xiangxiang
Cui, Haiting
Li, Shanshan
Yuan, Yafei
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-12-30
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Telecommunications Engineering
Abstract EN
Hand gesture recognition is an intuitive and effective way for humans to interact with a computer due to its high processing speed and recognition accuracy.
This paper proposes a novel approach to identify hand gestures in complex scenes by the Single-Shot Multibox Detector (SSD) deep learning algorithm with 19 layers of a neural network.
A benchmark database with gestures is used, and general hand gestures in the complex scene are chosen as the processing objects.
A real-time hand gesture recognition system based on the SSD algorithm is constructed and tested.
The experimental results show that the algorithm quickly identifies humans’ hands and accurately distinguishes different types of gestures.
Furthermore, the maximum accuracy is 99.2%, which is significantly important for human-computer interaction application.
American Psychological Association (APA)
Liu, Peng& Li, Xiangxiang& Cui, Haiting& Li, Shanshan& Yuan, Yafei. 2019. Hand Gesture Recognition Based on Single-Shot Multibox Detector Deep Learning. Mobile Information Systems،Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1193738
Modern Language Association (MLA)
Liu, Peng…[et al.]. Hand Gesture Recognition Based on Single-Shot Multibox Detector Deep Learning. Mobile Information Systems No. 2019 (2019), pp.1-7.
https://search.emarefa.net/detail/BIM-1193738
American Medical Association (AMA)
Liu, Peng& Li, Xiangxiang& Cui, Haiting& Li, Shanshan& Yuan, Yafei. Hand Gesture Recognition Based on Single-Shot Multibox Detector Deep Learning. Mobile Information Systems. 2019. Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1193738
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1193738