An experimental study on pedestrian classification using local features

Simple item page
dc.contributor.authorPaisitkriangkrai, S.
dc.contributor.authorShen, C.
dc.contributor.authorZhang, J.
dc.contributor.conferenceIEEE International Symposium on Circuits and Systems (2008 : Seattle, WA)
dc.date.issued2008
dc.description.abstractThis 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].
dc.description.statementofresponsibilitySakrapee Paisitkriangkrai, Chunhua Shen and Jian Zhang
dc.identifier.citationProceedings of the IEEE International Symposium on Circuits and Systems (ISCAS 2008), held in Seattle, WA, 18-21 May 2008: pp.2741-2744
dc.identifier.doi10.1109/ISCAS.2008.4542024
dc.identifier.isbn9781424416837
dc.identifier.issn0271-4310
dc.identifier.orcidShen, C. [0000-0002-8648-8718]
dc.identifier.urihttp://hdl.handle.net/2440/68946
dc.language.isoen
dc.publisherIEEE
dc.publisher.placeOnline
dc.relation.ispartofseriesIEEE International Symposium on Circuits and Systems
dc.rights©2008 IEEE
dc.source.urihttps://doi.org/10.1109/iscas.2008.4542024
dc.titleAn experimental study on pedestrian classification using local features
dc.typeConference paper
pubs.publication-statusPublished

Files

Collections

Computer Science publications