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dc.contributor.authorLu, Jen_NZ
dc.contributor.authorShen, Jen_NZ
dc.contributor.authorYan, W-Qen_NZ
dc.contributor.authorBacic, Ben_NZ
dc.date.accessioned2019-03-11T03:11:45Z
dc.date.available2019-03-11T03:11:45Z
dc.date.copyright2017-06en_NZ
dc.identifier.citationInternational Journal of Digital Crime and Forensics (IJDCF), 9(3), 11-27. doi:10.4018/IJDCF.2017070102
dc.identifier.issn1941-6210en_NZ
dc.identifier.issn1941-6229en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/12346
dc.description.abstractThis paper presents an empirical study for human behavior analysis based on three distinct feature extraction techniques: Histograms of Oriented Gradients (HOG), Local Binary Pattern (LBP) and Scale Invariant Local Ternary Pattern (SILTP). The utilized public videos representing spatio-temporal problem area of investigation include INRIA person detection and Weizmann pedestrian activity datasets. For INRIA dataset, both LBP and HOG were able to eliminate redundant video data and show human-intelligible feature visualization of extracted features required for classification tasks. However, for Weizmann dataset only HOG feature extraction was found to work well with classifying five selected activities/exercises (walking, running, skipping, jumping and jacking).en_NZ
dc.languageEnglishen_NZ
dc.publisherIGI Globalen_NZ
dc.relation.urihttp://www.igi-global.com/article/an-empirical-study-for-human-behavior-analysis/182461en_NZ
dc.rightsIGI GLOBAL AUTHORS, UNDER FAIR USE CAN: Post the final typeset PDF (which includes the title page, table of contents and other front materials, and the copyright statement) of their chapter or article (NOT THE ENTIRE BOOK OR JOURNAL ISSUE), on the author or editor's secure personal website and/or their university repository site.
dc.subjectHistograms of Oriented Gradients (HOG); Human Behavior Recognition; Local Binary Pattern (LBP)
dc.titleAn Empirical Study for Human Behavior Analysisen_NZ
dc.typeJournal Article
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.4018/IJDCF.2017070102en_NZ
aut.relation.articlenumber2en_NZ
aut.relation.endpage27
aut.relation.issue3en_NZ
aut.relation.pages26
aut.relation.startpage11
aut.relation.volume9en_NZ
pubs.elements-id219168
aut.relation.journalInternational Journal of Digital Crime and Forensicsen_NZ


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