Accurate detection of driver urgency using state-of-the-art supervised and unsupervised classification algorithms

This study is to determine the factors affect the accuracy of detection of driver face urgency situations under 2 different of State-of-the-Art classification algorithms, which are supervised and unsupervised.

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Bibliographic Details
Main Author:	Kong,Yuanjie
Other Authors:	Su Rong
Format:	Final Year Project
Language:	English
Published:	Nanyang Technological University 2023
Subjects:	Engineering::Electrical and electronic engineering
Online Access:	https://hdl.handle.net/10356/167924
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-167924
record_format	dspace
spelling	sg-ntu-dr.10356-1679242023-07-07T15:45:40Z Accurate detection of driver urgency using state-of-the-art supervised and unsupervised classification algorithms Kong,Yuanjie Su Rong School of Electrical and Electronic Engineering RSu@ntu.edu.sg Engineering::Electrical and electronic engineering This study is to determine the factors affect the accuracy of detection of driver face urgency situations under 2 different of State-of-the-Art classification algorithms, which are supervised and unsupervised. Bachelor of Engineering (Electrical and Electronic Engineering) 2023-06-05T07:23:54Z 2023-06-05T07:23:54Z 2023 Final Year Project (FYP) Kong, Y. (2023). Accurate detection of driver urgency using state-of-the-art supervised and unsupervised classification algorithms. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167924 https://hdl.handle.net/10356/167924 en P1047-212 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 Kong,Yuanjie Accurate detection of driver urgency using state-of-the-art supervised and unsupervised classification algorithms
description	This study is to determine the factors affect the accuracy of detection of driver face urgency situations under 2 different of State-of-the-Art classification algorithms, which are supervised and unsupervised.
author2	Su Rong
author_facet	Su Rong Kong,Yuanjie
format	Final Year Project
author	Kong,Yuanjie
author_sort	Kong,Yuanjie
title	Accurate detection of driver urgency using state-of-the-art supervised and unsupervised classification algorithms
title_short	Accurate detection of driver urgency using state-of-the-art supervised and unsupervised classification algorithms
title_full	Accurate detection of driver urgency using state-of-the-art supervised and unsupervised classification algorithms
title_fullStr	Accurate detection of driver urgency using state-of-the-art supervised and unsupervised classification algorithms
title_full_unstemmed	Accurate detection of driver urgency using state-of-the-art supervised and unsupervised classification algorithms
title_sort	accurate detection of driver urgency using state-of-the-art supervised and unsupervised classification algorithms
publisher	Nanyang Technological University
publishDate	2023
url	https://hdl.handle.net/10356/167924
_version_	1772827694555201536

Accurate detection of driver urgency using state-of-the-art supervised and unsupervised classification algorithms

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