Fast and accurate human detection using a cascade of boosted MS-LBP features

Xu, J; Wu, Q; Zhang, J; Tang, Z

Fast and accurate human detection using a cascade of boosted MS-LBP features

Xu, J Wu, Q

Zhang, J

Tang, Z

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Publication Type:: Journal Article
Citation:: IEEE Signal Processing Letters, 2012, 19 (10), pp. 676 - 679
Issue Date:: 2012-08-31

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Field	Value	Language
dc.contributor.author	Xu, J	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.contributor.author	Tang, Z	en_US
dc.date.issued	2012-08-31	en_US
dc.identifier.citation	IEEE Signal Processing Letters, 2012, 19 (10), pp. 676 - 679	en_US
dc.identifier.issn	1070-9908	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31598
dc.description.abstract	In this letter, a new scheme for generating local binary patterns (LBP) is presented. This Modified Symmetric LBP (MS-LBP) feature takes advantage of LBP and gradient features. It is then applied into a boosted cascade framework for human detection. By combining MS-LBP with Haar-like feature into the boosted framework, the performances of heterogeneous features based detectors are evaluated for the best trade-off between accuracy and speed. Two feature training schemes, namely Single AdaBoost Training Scheme (SATS) and Dual AdaBoost Training Scheme (DATS) are proposed and compared. On the top of AdaBoost, two multidimensional feature projection methods are described. A comprehensive experiment is presented. Apart from obtaining higher detection accuracy, the detection speed based on DATS is 17 times faster than HOG method. © 1994-2012 IEEE.	en_US
dc.relation.ispartof	IEEE Signal Processing Letters	en_US
dc.relation.isbasedon	10.1109/LSP.2012.2210870	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Fast and accurate human detection using a cascade of boosted MS-LBP features	en_US
dc.type	Journal Article
utslib.citation.volume	10	en_US
utslib.citation.volume	19	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1005 Communications Technologies	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 - 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.issue	10	en_US
pubs.publication-status	Published	en_US
pubs.volume	19	en_US

Abstract:

In this letter, a new scheme for generating local binary patterns (LBP) is presented. This Modified Symmetric LBP (MS-LBP) feature takes advantage of LBP and gradient features. It is then applied into a boosted cascade framework for human detection. By combining MS-LBP with Haar-like feature into the boosted framework, the performances of heterogeneous features based detectors are evaluated for the best trade-off between accuracy and speed. Two feature training schemes, namely Single AdaBoost Training Scheme (SATS) and Dual AdaBoost Training Scheme (DATS) are proposed and compared. On the top of AdaBoost, two multidimensional feature projection methods are described. A comprehensive experiment is presented. Apart from obtaining higher detection accuracy, the detection speed based on DATS is 17 times faster than HOG method. © 1994-2012 IEEE.

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http://hdl.handle.net/10453/31598