VEHICLE TYPE CLASSIFICATION USING PCA WITH SELF-CLUSTERING

Peng, Y; Jin, JS; Luo, S; Xu, M; Cui, Y

VEHICLE TYPE CLASSIFICATION USING PCA WITH SELF-CLUSTERING

Peng, Y Jin, JS Luo, S Xu, M

Cui, Y

Permalink

Publisher:: IEEE Computer Society
Publication Type:: Conference Proceeding
Citation:: 2012 IEEE International Conference on Multimedia and Expo Workshops (ICME), 2012, pp. 384 - 389
Issue Date:: 2012-01

Closed Access

	Filename	Description	Size
	2012004439OK.pdf	Published version	672.94 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	Peng, Y	en_US
dc.contributor.author	Jin, JS	en_US
dc.contributor.author	Luo, S	en_US
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.contributor.author	Cui, Y	en_US
dc.contributor.editor	Zhang, APJ	en_US
dc.date	2012-07-09	en_US
dc.date.issued	2012-01	en_US
dc.identifier.citation	2012 IEEE International Conference on Multimedia and Expo Workshops (ICME), 2012, pp. 384 - 389	en_US
dc.identifier.isbn	978-0-7695-4729-9	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31541
dc.description.abstract	Different conditions, such as occlusions, changes of lighting, shadows and rotations, make vehicle type classification still a challenging task, especially for real-time applications. Most existing methods rely on presumptions on certain conditions, such as lighting conditions and special camera settings. However, these presumptions usually do not work for applications in real world. In this paper, we propose a robust vehicle type classification method based on adaptive multi-class Principal Components Analysis (PCA). We treat car images captured at daytime and night-time separately. Vehicle front is extracted by examining vehicle front width and the location of license plate. Then, after generating eigenvectors to represent extracted vehicle fronts, we propose a PCA method with self-clustering to classify vehicle type. The comparison experiments with the state of art methods and real-time evaluations demonstrate the promising performance of our proposed method. Moreover, as we do not find any public database including sufficient desired images, we built up online our own database including 4924 high-resolution images of vehicle front view for further research on this topic	en_US
dc.publisher	IEEE Computer Society	en_US
dc.relation.ispartof	2012 IEEE International Conference on Multimedia and Expo Workshops (ICME)	en_US
dc.relation.ispartof	2012 IEEE International Conference on Multimedia and Expo Workshops	en_US
dc.relation.isbasedon	10.1109/ICMEW.2012.73	en_US
dc.rights	© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	VEHICLE TYPE CLASSIFICATION USING PCA WITH SELF-CLUSTERING	en_US
dc.type	Conference Proceeding
utslib.location	Piscataway, NJ	en_US
utslib.location.activity	Melbourne Australia	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Melbourne Australia
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 - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Piscataway, NJ	en_US
pubs.start-date	2012-07-09	en_US

Abstract:

Different conditions, such as occlusions, changes of lighting, shadows and rotations, make vehicle type classification still a challenging task, especially for real-time applications. Most existing methods rely on presumptions on certain conditions, such as lighting conditions and special camera settings. However, these presumptions usually do not work for applications in real world. In this paper, we propose a robust vehicle type classification method based on adaptive multi-class Principal Components Analysis (PCA). We treat car images captured at daytime and night-time separately. Vehicle front is extracted by examining vehicle front width and the location of license plate. Then, after generating eigenvectors to represent extracted vehicle fronts, we propose a PCA method with self-clustering to classify vehicle type. The comparison experiments with the state of art methods and real-time evaluations demonstrate the promising performance of our proposed method. Moreover, as we do not find any public database including sufficient desired images, we built up online our own database including 4924 high-resolution images of vehicle front view for further research on this topic

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

http://hdl.handle.net/10453/31541