Vehicle detection on aerial images by extracting corner features for rotational invariant shape matching

Wang, S

Vehicle detection on aerial images by extracting corner features for rotational invariant shape matching

Wang, S

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 11th IEEE International Conference on Computer and Information Technology, CIT 2011, 2011, pp. 171 - 175
Issue Date:: 2011-11-01

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Field	Value	Language
dc.contributor.author	Wang, S	en_US
dc.date.issued	2011-11-01	en_US
dc.identifier.citation	Proceedings - 11th IEEE International Conference on Computer and Information Technology, CIT 2011, 2011, pp. 171 - 175	en_US
dc.identifier.isbn	9780769543888	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19207
dc.description.abstract	Vehicle detection from aerial images has been extensively studied in many research papers and it is an important component of an intelligent transportation system. In the meantime, it is still a difficult problem with many open questions due to challenges caused by various factors such as low resolution of the aerial images, features restricted to a particular type of car, noise from other objects or object shadows, and occulsion in urban environments. By investigating several benchmark methods and frameworks in the literature, this paper proposes a novel feature fusion framework which successfully implements an effective vehicle detection method based on shadow detection followed by a rotational invariant shape matching of corner features. Promising results are obtained from the experiments. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings - 11th IEEE International Conference on Computer and Information Technology, CIT 2011	en_US
dc.relation.isbasedon	10.1109/CIT.2011.56	en_US
dc.title	Vehicle detection on aerial images by extracting corner features for rotational invariant shape matching	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Pafos, Cyprus	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Vehicle detection from aerial images has been extensively studied in many research papers and it is an important component of an intelligent transportation system. In the meantime, it is still a difficult problem with many open questions due to challenges caused by various factors such as low resolution of the aerial images, features restricted to a particular type of car, noise from other objects or object shadows, and occulsion in urban environments. By investigating several benchmark methods and frameworks in the literature, this paper proposes a novel feature fusion framework which successfully implements an effective vehicle detection method based on shadow detection followed by a rotational invariant shape matching of corner features. Promising results are obtained from the experiments. © 2011 IEEE.

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http://hdl.handle.net/10453/19207