Performance evaluation of local features in human classification and detection

Paisitkriangkrai, S; Shen, C; Zhang, J

Performance evaluation of local features in human classification and detection

Paisitkriangkrai, S Shen, C Zhang, J

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Publication Type:: Journal Article
Citation:: IET Computer Vision, 2008, 2 (4), pp. 236 - 246
Issue Date:: 2008-12-18

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Field	Value	Language
dc.contributor.author	Paisitkriangkrai, S	en_US
dc.contributor.author	Shen, C	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.date.issued	2008-12-18	en_US
dc.identifier.citation	IET Computer Vision, 2008, 2 (4), pp. 236 - 246	en_US
dc.identifier.issn	1751-9632	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29695
dc.description.abstract	Detecting pedestrians accurately is the first fundamental step for many computer vision applications such as video surveillance, smart vehicles, intersection traffic analysis and so on. The authors present an experimental study on pedestrian detection using state-of-the-art local feature extraction and support vector machine (SVM) classifiers. The performance of pedestrian detection using region covariance, histogram of oriented gradients (HOG) and local receptive fields (LRF) feature descriptors is experimentally evaluated. The experiments are performed on the DaimlerChrysler benchmarking data set, the MIT CBCL data set and 'Intitut National de Recherche en Informatique et Automatique (INRIA) data set. All can be publicly accessed. The experimental results show that region covariance features with radial basis function kernel SVM and HOG features with quadratic kernel SVM outperform the combination of LRF features with quadratic kernel SVM. Furthermore, the results reveal that both covariance and HOG features perform very well in the context of pedestrian detection. © 2008 The Institution of Engineering and Technology.	en_US
dc.relation.ispartof	IET Computer Vision	en_US
dc.relation.isbasedon	10.1049/iet-cvi:20080026	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Performance evaluation of local features in human classification and detection	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	2	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	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
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	2	en_US

Abstract:

Detecting pedestrians accurately is the first fundamental step for many computer vision applications such as video surveillance, smart vehicles, intersection traffic analysis and so on. The authors present an experimental study on pedestrian detection using state-of-the-art local feature extraction and support vector machine (SVM) classifiers. The performance of pedestrian detection using region covariance, histogram of oriented gradients (HOG) and local receptive fields (LRF) feature descriptors is experimentally evaluated. The experiments are performed on the DaimlerChrysler benchmarking data set, the MIT CBCL data set and 'Intitut National de Recherche en Informatique et Automatique (INRIA) data set. All can be publicly accessed. The experimental results show that region covariance features with radial basis function kernel SVM and HOG features with quadratic kernel SVM outperform the combination of LRF features with quadratic kernel SVM. Furthermore, the results reveal that both covariance and HOG features perform very well in the context of pedestrian detection. © 2008 The Institution of Engineering and Technology.

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http://hdl.handle.net/10453/29695