An experimental study on pedestrian classification using local features

Paisitkriangkrai, S; Shen, C; Zhang, J

An experimental study on pedestrian classification using local features

Paisitkriangkrai, S Shen, C Zhang, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Symposium on Circuits and Systems, 2008, pp. 2741 - 2744
Issue Date:: 2008-09-19

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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	2008-09-19	en_US
dc.identifier.citation	Proceedings - IEEE International Symposium on Circuits and Systems, 2008, pp. 2741 - 2744	en_US
dc.identifier.isbn	9781424416844	en_US
dc.identifier.issn	0271-4310	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29983
dc.description.abstract	This paper presents 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 both the benchmarking dataset used in [1] and the MIT CBCL dataset. Both can be publicly accessed. The experimental results show that region covariance features with radial basis function (RBF) kernel SVM and HOG features with quadratic kernel SVM outperform the combination of LRF features with quadratic kernel SVM reported in [1]. ©2008 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Symposium on Circuits and Systems	en_US
dc.relation.isbasedon	10.1109/ISCAS.2008.4542024	en_US
dc.title	An experimental study on pedestrian classification using local features	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Seattle, WA	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.publication-status	Published	en_US

Abstract:

This paper presents 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 both the benchmarking dataset used in [1] and the MIT CBCL dataset. Both can be publicly accessed. The experimental results show that region covariance features with radial basis function (RBF) kernel SVM and HOG features with quadratic kernel SVM outperform the combination of LRF features with quadratic kernel SVM reported in [1]. ©2008 IEEE.

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http://hdl.handle.net/10453/29983