An Experimental Evaluation of Local Features for Pedestrian Classification
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Paisitkriangkrai, Sakrapee; Shen, Chunhua; Zhang, Jian (Andrew)
Description
The ability to detect pedestrians is a first important step in many computer vision applications such as video surveillance. 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...[Show more]
dc.contributor.author | Paisitkriangkrai, Sakrapee | |
---|---|---|
dc.contributor.author | Shen, Chunhua | |
dc.contributor.author | Zhang, Jian (Andrew) | |
dc.coverage.spatial | Adelaide Australia | |
dc.date.accessioned | 2015-12-07T22:55:11Z | |
dc.date.created | December 3-5 2007 | |
dc.identifier.isbn | 0769530672 | |
dc.identifier.uri | http://hdl.handle.net/1885/28285 | |
dc.description.abstract | The ability to detect pedestrians is a first important step in many computer vision applications such as video surveillance. 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]. | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE Inc) | |
dc.relation.ispartofseries | Digital Image Computing: Techniques and Applications (DICTA 2007) | |
dc.source | Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications | |
dc.source.uri | http://dicta2007.infoeng.flinders.edu.au/ | |
dc.subject | Keywords: computer vision applications; Data sets; Digital image computing; Experimental evaluations; Experimental results; Experimental studies; feature descriptors; Local features; Pedestrian classification; Pedestrian detection; Radial-basis function (RBF); Rece | |
dc.title | An Experimental Evaluation of Local Features for Pedestrian Classification | |
dc.type | Conference paper | |
local.description.notes | Imported from ARIES | |
local.description.refereed | Yes | |
dc.date.issued | 2007 | |
local.identifier.absfor | 080100 - ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING | |
local.identifier.ariespublication | u4010714xPUB57 | |
local.type.status | Published Version | |
local.contributor.affiliation | Paisitkriangkrai, Sakrapee, University of New South Wales | |
local.contributor.affiliation | Shen, Chunhua, College of Engineering and Computer Science, ANU | |
local.contributor.affiliation | Zhang, Jian (Andrew), College of Engineering and Computer Science, ANU | |
local.description.embargo | 2037-12-31 | |
local.bibliographicCitation.startpage | 53 | |
local.bibliographicCitation.lastpage | 60 | |
local.identifier.doi | 10.1109/DICTA.2007.4426775 | |
dc.date.updated | 2015-12-07T12:53:33Z | |
local.identifier.scopusID | 2-s2.0-44949130013 | |
Collections | ANU Research Publications |
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01_Paisitkriangkrai_An_Experimental_Evaluation_of_2007.pdf | 92.44 kB | Adobe PDF | ||
02_Paisitkriangkrai_An_Experimental_Evaluation_of_2007.pdf | 283.16 kB | Adobe PDF |
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