Compact gesture recognition algorithm using machine learning

Gesture recognition is an important human-computer interaction tool that has been studied since the 1980s. From the very beginning with data gloves, gesture recognition has evolved to machine learning based gesture recognition, and the accuracy and application of gesture recognition has increased dr...

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Bibliographic Details
Main Author:	Yang, Ye Kun
Other Authors:	Kim Tae Hyoung
Format:	Thesis-Master by Coursework
Language:	English
Published:	Nanyang Technological University 2022
Subjects:	Engineering::Electrical and electronic engineering
Online Access:	https://hdl.handle.net/10356/155038
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-155038
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spelling	sg-ntu-dr.10356-1550382023-07-04T16:34:33Z Compact gesture recognition algorithm using machine learning Yang, Ye Kun Kim Tae Hyoung School of Electrical and Electronic Engineering THKIM@ntu.edu.sg Engineering::Electrical and electronic engineering Gesture recognition is an important human-computer interaction tool that has been studied since the 1980s. From the very beginning with data gloves, gesture recognition has evolved to machine learning based gesture recognition, and the accuracy and application of gesture recognition has increased dramatically. In this work,the classical LeNet-5 algorithm architecture is used. By tuning different parameters in Windows Caffe platform, 95% accuracy is obtained for the recognition of gesture numbers from 0 to 5. The hardware is implemented in FPGA through Vivado design kit, and finally real time gesture display and result output is achieved. Master of Science (Electronics) 2022-02-06T23:48:37Z 2022-02-06T23:48:37Z 2021 Thesis-Master by Coursework Yang, Y. K. (2021). Compact gesture recognition algorithm using machine learning. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/155038 https://hdl.handle.net/10356/155038 en ISM-DISS-02329 application/pdf Nanyang Technological University
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	Engineering::Electrical and electronic engineering
spellingShingle	Engineering::Electrical and electronic engineering Yang, Ye Kun Compact gesture recognition algorithm using machine learning
description	Gesture recognition is an important human-computer interaction tool that has been studied since the 1980s. From the very beginning with data gloves, gesture recognition has evolved to machine learning based gesture recognition, and the accuracy and application of gesture recognition has increased dramatically. In this work,the classical LeNet-5 algorithm architecture is used. By tuning different parameters in Windows Caffe platform, 95% accuracy is obtained for the recognition of gesture numbers from 0 to 5. The hardware is implemented in FPGA through Vivado design kit, and finally real time gesture display and result output is achieved.
author2	Kim Tae Hyoung
author_facet	Kim Tae Hyoung Yang, Ye Kun
format	Thesis-Master by Coursework
author	Yang, Ye Kun
author_sort	Yang, Ye Kun
title	Compact gesture recognition algorithm using machine learning
title_short	Compact gesture recognition algorithm using machine learning
title_full	Compact gesture recognition algorithm using machine learning
title_fullStr	Compact gesture recognition algorithm using machine learning
title_full_unstemmed	Compact gesture recognition algorithm using machine learning
title_sort	compact gesture recognition algorithm using machine learning
publisher	Nanyang Technological University
publishDate	2022
url	https://hdl.handle.net/10356/155038
_version_	1772828194595930112

Compact gesture recognition algorithm using machine learning

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