Fast and Accurate Human Detection Using a Cascade of Boosted MS-LBP Features

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 th...

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Bibliographic Details
Published inIEEE signal processing letters Vol. 19; no. 10; pp. 676 - 679
Main Authors Xu, Jingsong, Wu, Qiang, Zhang, Jian, Tang, Zhenmin
Format Journal Article
LanguageEnglish
Published IEEE 01.10.2012
Subjects
AdaBoost
Detectors
Feature extraction
human detection
Humans
Machine learning algorithms
MS-LBP
Support vector machines
Training
WLDA
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Summary: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.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2012.2210870