Boosted Vehicle Detection Using Local and Global Features

Lin, C-T; Hsu, S-C; Lee, J-F; Yang, C-T

Boosted Vehicle Detection Using Local and Global Features

Lin, C-T

Hsu, S-C Lee, J-F Yang, C-T

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Publisher:: Scientific Research Publishing
Publication Type:: Journal Article
Citation:: Journal of Signal and Information Processing, 2013, 04 (03), pp. 243 - 252
Issue Date:: 2013

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Field	Value	Language
dc.contributor.author	Lin, C-T https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Hsu, S-C	en_US
dc.contributor.author	Lee, J-F	en_US
dc.contributor.author	Yang, C-T	en_US
dc.date.issued	2013	en_US
dc.identifier.citation	Journal of Signal and Information Processing, 2013, 04 (03), pp. 243 - 252	en_US
dc.identifier.issn	2159-4465	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117380
dc.description.abstract	This study presents a boosted vehicle detection system. It first hypothesizes potential locations of vehicles to reduce the computational costs by a statistic of the edge intensity and symmetry, then verifies the accuracy of the hypotheses using AdaBoost and Probabilistic Decision-Based Neural Network (PDBNN) classifiers, which exploit local and global features of vehicles, respectively. The combination of 2 classifiers can be used to learn the complementary relationship between local and global features, and it gains an extremely low false positive rate while maintaining a high detection rate. For the MIT Center for Biological & Computational Learning (CBCL) database, a 96.3% detection rate leads to a false alarm rate of approximately 0.0013%. The objective of this study is to extract the characteristic of vehicles in both local- and global-orientation, and model the implicit invariance of vehicles. This boosted approach provides a more effective solution to handle the problems encountered by conventional background-based detection systems. The experimental results of this study prove that the proposed system achieves good performance in detecting vehicles without background information. The implemented system also extract useful traffic information that can be used for further processing, such as tracking, counting, classification, and recognition.	en_US
dc.publisher	Scientific Research Publishing	en_US
dc.relation.ispartof	Journal of Signal and Information Processing	en_US
dc.relation.isbasedon	10.4236/jsip.2013.43032	en_US
dc.title	Boosted Vehicle Detection Using Local and Global Features	en_US
dc.type	Journal Article
utslib.citation.volume	03	en_US
utslib.citation.volume	04	en_US
utslib.for	0801 Artificial Intelligence and 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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.consider-herdc	false	en_US
pubs.issue	03	en_US
pubs.publication-status	Published	en_US
pubs.volume	04	en_US

Abstract:

This study presents a boosted vehicle detection system. It first hypothesizes potential locations of vehicles to reduce the computational costs by a statistic of the edge intensity and symmetry, then verifies the accuracy of the hypotheses using AdaBoost and Probabilistic Decision-Based Neural Network (PDBNN) classifiers, which exploit local and global features of vehicles, respectively. The combination of 2 classifiers can be used to learn the complementary relationship between local and global features, and it gains an extremely low false positive rate while maintaining a high detection rate. For the MIT Center for Biological & Computational Learning (CBCL) database, a 96.3% detection rate leads to a false alarm rate of approximately 0.0013%. The objective of this study is to extract the characteristic of vehicles in both local- and global-orientation, and model the implicit invariance of vehicles. This boosted approach provides a more effective solution to handle the problems encountered by conventional background-based detection systems. The experimental results of this study prove that the proposed system achieves good performance in detecting vehicles without background information. The implemented system also extract useful traffic information that can be used for further processing, such as tracking, counting, classification, and recognition.

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http://hdl.handle.net/10453/117380