Intelligent pedestrian detection system in semi-dark environment

Gan, Y; Al-Jumaily, A

Intelligent pedestrian detection system in semi-dark environment

Gan, Y Al-Jumaily, A

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Publication Type:: Conference Proceeding
Citation:: SoCPaR 2009 - Soft Computing and Pattern Recognition, 2009, pp. 598 - 603
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	Gan, Y	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	SoCPaR 2009 - Soft Computing and Pattern Recognition, 2009, pp. 598 - 603	en_US
dc.identifier.isbn	9780769538792	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10881
dc.description.abstract	Computer vision techniques have been widely used in various applications. In recent years, as energy efficiency have gradually become a important issues, computer vision techniques can be integrated into a smart control system that helps increase the energy efficiency by controlling the turn on of the light based on human detection . However, implement such system that detect walking human in a semi-dark environment remains a challenge. This paper proposes a novel detection technique combining movement analysis and SVM classifier to tackle this problem. This technique consist of a few steps: a statistical background model to segment moving objects as foreground, followed by an analysis model to generate pedestrian candidates based on the movement of foreground objects and lastly a SVM classifier that verify the pedestrian candidates based on the shape features. © 2009 IEEE.	en_US
dc.relation.ispartof	SoCPaR 2009 - Soft Computing and Pattern Recognition	en_US
dc.relation.isbasedon	10.1109/SoCPaR.2009.118	en_US
dc.title	Intelligent pedestrian detection system in semi-dark environment	en_US
dc.type	Conference Proceeding
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Malaysia	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Computer vision techniques have been widely used in various applications. In recent years, as energy efficiency have gradually become a important issues, computer vision techniques can be integrated into a smart control system that helps increase the energy efficiency by controlling the turn on of the light based on human detection . However, implement such system that detect walking human in a semi-dark environment remains a challenge. This paper proposes a novel detection technique combining movement analysis and SVM classifier to tackle this problem. This technique consist of a few steps: a statistical background model to segment moving objects as foreground, followed by an analysis model to generate pedestrian candidates based on the movement of foreground objects and lastly a SVM classifier that verify the pedestrian candidates based on the shape features. © 2009 IEEE.

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http://hdl.handle.net/10453/10881