Moving vehicle detection based on an improved interframe difference and a Gaussian model

Li, W; Yao, J; Dong, T; Li, H; He, X

Moving vehicle detection based on an improved interframe difference and a Gaussian model

Li, W Yao, J Dong, T Li, H He, X

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015, 2016, pp. 969 - 973
Issue Date:: 2016-02-16

Closed Access

	Filename	Description	Size
	1257.pdf	Published version	369.36 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Li, W	en_US
dc.contributor.author	Yao, J	en_US
dc.contributor.author	Dong, T	en_US
dc.contributor.author	Li, H	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.date.issued	2016-02-16	en_US
dc.identifier.citation	Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015, 2016, pp. 969 - 973	en_US
dc.identifier.isbn	9781467390989	en_US
dc.identifier.uri	http://hdl.handle.net/10453/44218
dc.description.abstract	© 2015 IEEE. For moving vehicle detection, this paper presents an algorithm on the basis of an improved interframe differential algorithm and an improved Gaussian model. Firstly, according to a statistical histogram, an interesting region is extracted. Through a mean algorithm, an initial background model is established. The interesting region is divided into several blocks by a self-adaptive method. Secondly, according to an improved interframe difference algorithm, the interesting region is separated roughly. On the basis of these steps, we utilize an improved Gaussian model to separate the rough results precisely. At last, the results are processed by double-threshold background subtracting. Experimental results show this algorithm can detect moving vehicles rapidly and accurately.	en_US
dc.relation.ispartof	Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015	en_US
dc.relation.isbasedon	10.1109/CISP.2015.7408019	en_US
dc.title	Moving vehicle detection based on an improved interframe difference and a Gaussian model	en_US
dc.type	Conference Proceeding
utslib.for	080104 Computer Vision	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	080106 Image Processing	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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2015 IEEE. For moving vehicle detection, this paper presents an algorithm on the basis of an improved interframe differential algorithm and an improved Gaussian model. Firstly, according to a statistical histogram, an interesting region is extracted. Through a mean algorithm, an initial background model is established. The interesting region is divided into several blocks by a self-adaptive method. Secondly, according to an improved interframe difference algorithm, the interesting region is separated roughly. On the basis of these steps, we utilize an improved Gaussian model to separate the rough results precisely. At last, the results are processed by double-threshold background subtracting. Experimental results show this algorithm can detect moving vehicles rapidly and accurately.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/44218