WiFi-vision enabled identification via multi-modal gait recognition

This paper proposes GaitFi, a novel multi-modal gait recognition method, which uses WiFi signals and videos for human identification.

Saved in:

Bibliographic Details
Main Author:	Deng, Lang
Other Authors:	Xie Lihua
Format:	Thesis-Master by Coursework
Language:	English
Published:	Nanyang Technological University 2022
Subjects:	Engineering::Electrical and electronic engineering::Computer hardware, software and systems Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Online Access:	https://hdl.handle.net/10356/159528
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-159528
record_format	dspace
spelling	sg-ntu-dr.10356-1595282022-06-27T08:11:53Z WiFi-vision enabled identification via multi-modal gait recognition Deng, Lang Xie Lihua School of Electrical and Electronic Engineering ELHXIE@ntu.edu.sg Engineering::Electrical and electronic engineering::Computer hardware, software and systems Engineering::Electrical and electronic engineering::Electronic systems::Signal processing This paper proposes GaitFi, a novel multi-modal gait recognition method, which uses WiFi signals and videos for human identification. Master of Science (Computer Control and Automation) 2022-06-23T01:06:26Z 2022-06-23T01:06:26Z 2022 Thesis-Master by Coursework Deng, L. (2022). WiFi-vision enabled identification via multi-modal gait recognition. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/159528 https://hdl.handle.net/10356/159528 en 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::Computer hardware, software and systems Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
spellingShingle	Engineering::Electrical and electronic engineering::Computer hardware, software and systems Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Deng, Lang WiFi-vision enabled identification via multi-modal gait recognition
description	This paper proposes GaitFi, a novel multi-modal gait recognition method, which uses WiFi signals and videos for human identification.
author2	Xie Lihua
author_facet	Xie Lihua Deng, Lang
format	Thesis-Master by Coursework
author	Deng, Lang
author_sort	Deng, Lang
title	WiFi-vision enabled identification via multi-modal gait recognition
title_short	WiFi-vision enabled identification via multi-modal gait recognition
title_full	WiFi-vision enabled identification via multi-modal gait recognition
title_fullStr	WiFi-vision enabled identification via multi-modal gait recognition
title_full_unstemmed	WiFi-vision enabled identification via multi-modal gait recognition
title_sort	wifi-vision enabled identification via multi-modal gait recognition
publisher	Nanyang Technological University
publishDate	2022
url	https://hdl.handle.net/10356/159528
_version_	1736856400023257088

WiFi-vision enabled identification via multi-modal gait recognition

Similar Items