Real-time driver action recognition system

Through recent years, the interest of research in the area of human action recognition in computer vision has increased rapidly. The main objective of this FYP is to develop a system for recognizing different gestures. The most important tool used is Extreme Machine Learning Algorithm, because of i...

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Bibliographic Details
Main Author:	Liu, Cong
Other Authors:	Huang Guangbin
Format:	Final Year Project
Language:	English
Published:	2016
Subjects:	DRNTU::Engineering
Online Access:	http://hdl.handle.net/10356/68214
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-68214
record_format	dspace
spelling	sg-ntu-dr.10356-682142023-07-07T16:48:21Z Real-time driver action recognition system Liu, Cong Huang Guangbin School of Electrical and Electronic Engineering DRNTU::Engineering Through recent years, the interest of research in the area of human action recognition in computer vision has increased rapidly. The main objective of this FYP is to develop a system for recognizing different gestures. The most important tool used is Extreme Machine Learning Algorithm, because of its fast speed for analyzing. Two tests were conducted after the training process. The test with samples collected from real-time webcam achieves an accuracy of 50%, while the test with samples from original training database is much higher. Future work could be focused on improving the accuracy and apply the system to a wider field such as whole-body action recognition. Bachelor of Engineering 2016-05-25T01:55:11Z 2016-05-25T01:55:11Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/68214 en Nanyang Technological University 28 p. application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	DRNTU::Engineering
spellingShingle	DRNTU::Engineering Liu, Cong Real-time driver action recognition system
description	Through recent years, the interest of research in the area of human action recognition in computer vision has increased rapidly. The main objective of this FYP is to develop a system for recognizing different gestures. The most important tool used is Extreme Machine Learning Algorithm, because of its fast speed for analyzing. Two tests were conducted after the training process. The test with samples collected from real-time webcam achieves an accuracy of 50%, while the test with samples from original training database is much higher. Future work could be focused on improving the accuracy and apply the system to a wider field such as whole-body action recognition.
author2	Huang Guangbin
author_facet	Huang Guangbin Liu, Cong
format	Final Year Project
author	Liu, Cong
author_sort	Liu, Cong
title	Real-time driver action recognition system
title_short	Real-time driver action recognition system
title_full	Real-time driver action recognition system
title_fullStr	Real-time driver action recognition system
title_full_unstemmed	Real-time driver action recognition system
title_sort	real-time driver action recognition system
publishDate	2016
url	http://hdl.handle.net/10356/68214
_version_	1772825331340673024

Real-time driver action recognition system

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