Gesture recognition based on deep learning
Gesture recognition based on deep learning is a rapidly growing field of research and development that has the potential to revolutionize the way humans interact with computers and machines. Gesture recognition involves using algorithms and techniques to interpret human gestures, such as hand and...
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Nanyang Technological University
2023
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sg-ntu-dr.10356-1700152023-08-25T15:42:49Z Gesture recognition based on deep learning Yang, Chaoran Mohammed Yakoob Siyal School of Electrical and Electronic Engineering EYAKOOB@ntu.edu.sg Engineering::Computer science and engineering Gesture recognition based on deep learning is a rapidly growing field of research and development that has the potential to revolutionize the way humans interact with computers and machines. Gesture recognition involves using algorithms and techniques to interpret human gestures, such as hand and body movements, facial expressions, and vocal intonations, to understand their meaning and intent. Gesture recognition based on deep learning has a wide range of potential applications in fields such as robotics, human-computer interaction, and healthcare. The aim of this dissertation is to design gesture recognition algorithm based on deep learning. Object detection based on convolutional neural network can be divided into one-stage object detection and two-stage object detection. Firstly, this dissertation investigates and introduces literature review, and then based on YOLOV3 and RESNET-50, this dissertation gives the models of two detection methods and the training and testing results of these two models on the dataset. Master of Science (Communications Engineering) 2023-08-22T01:39:31Z 2023-08-22T01:39:31Z 2023 Thesis-Master by Coursework Yang, C. (2023). Gesture recognition based on deep learning. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/170015 https://hdl.handle.net/10356/170015 en application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering Yang, Chaoran Gesture recognition based on deep learning |
description |
Gesture recognition based on deep learning is a rapidly growing field of research and
development that has the potential to revolutionize the way humans interact with
computers and machines. Gesture recognition involves using algorithms and
techniques to interpret human gestures, such as hand and body movements, facial
expressions, and vocal intonations, to understand their meaning and intent. Gesture
recognition based on deep learning has a wide range of potential applications in fields
such as robotics, human-computer interaction, and healthcare.
The aim of this dissertation is to design gesture recognition algorithm based on deep
learning. Object detection based on convolutional neural network can be divided into
one-stage object detection and two-stage object detection. Firstly, this dissertation
investigates and introduces literature review, and then based on YOLOV3 and
RESNET-50, this dissertation gives the models of two detection methods and the
training and testing results of these two models on the dataset. |
author2 |
Mohammed Yakoob Siyal |
author_facet |
Mohammed Yakoob Siyal Yang, Chaoran |
format |
Thesis-Master by Coursework |
author |
Yang, Chaoran |
author_sort |
Yang, Chaoran |
title |
Gesture recognition based on deep learning |
title_short |
Gesture recognition based on deep learning |
title_full |
Gesture recognition based on deep learning |
title_fullStr |
Gesture recognition based on deep learning |
title_full_unstemmed |
Gesture recognition based on deep learning |
title_sort |
gesture recognition based on deep learning |
publisher |
Nanyang Technological University |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/170015 |
_version_ |
1779156497540841472 |