An experimental study on pedestrian classification using local features

Sakrapee Paisitkriangkrai*, Chunhua Shen, Jian Zhang

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    10 Citations (Scopus)

    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].

    Original languageEnglish
    Title of host publication2008 IEEE International Symposium on Circuits and Systems, ISCAS 2008
    Pages2741-2744
    Number of pages4
    DOIs
    Publication statusPublished - 2008
    Event2008 IEEE International Symposium on Circuits and Systems, ISCAS 2008 - Seattle, WA, United States
    Duration: 18 May 200821 May 2008

    Publication series

    NameProceedings - IEEE International Symposium on Circuits and Systems
    ISSN (Print)0271-4310

    Conference

    Conference2008 IEEE International Symposium on Circuits and Systems, ISCAS 2008
    Country/TerritoryUnited States
    CitySeattle, WA
    Period18/05/0821/05/08

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