Staff Members - Benha University
Automatic Gun Detection Approach for Video Surveillance
International Journal of Sociotechnology and Knowledge Development • 2020
Publication Information
Authors
Mai K. Galab; Ahmed Taha; Hala H. Zayed
Keywords
Closed Circuit Television, Convolutional Neural Network, Deep Neural Network, Transfer Learning, Video
Surveillance Systems
Journal
International Journal of Sociotechnology and Knowledge Development
Publisher
IGI Global
Volume
Not Available
Issue
Not Available
Pages
Not Available
publication.type
International
Paper Link
Not Available
Supplementary Materials
Not Available
Abstract
Theimmensecrimeratesresultingfromusingpistolshaveledgovernmentstoseeksolutionstodeal
withsuchterroristincidents.Theseincidentshaveanegativeimpactonpublicsecurityandcause
panicamongcitizens.Fromthispoint,facingapandemicofweaponviolencehasbecomeanimportant
researchtopic.Onewaytoreducethiskindofviolenceistopreventitviaremotedetectionandtogive
anappropriateresponseinashorttime.Videosurveillanceistheprocessofmonitoringthebehavior
ofpeopleandobjects.Surveillancesystemscanbeemployedinsecurityapplicationsaslegalevidence.
Moreover,itisusedwidelyinsuspiciousactivitydetectionapplications.Intelligentvideosurveillance
systems(IVSSs)aretheuseofautomaticvideoanalyticstoenhancetheeffectivenessoftraditional
surveillancesystems.WiththerapiddevelopmentinDeepLearning(DL),itisnowwidelyusedto
addresstheproblemsexistingintraditionaldetectiontechniques.Inthisarticle,anapproachtodetect
pistolsandgunsinvideosurveillancesystemsisproposed.Thepresentedapproachdoesnotneed
anyinvasivetoolsintheweapondetectionprocess.ItusesDLintheclassificationandthedetection
processes.TheproposedapproachenhancestheobtainedresultsbyapplyingTransferLearning(TL).
ItemploystwodifferentDLtechniques:AlexNetandGoogLeNet.Experimentalresultsverifythe
adaptabilityofdetectingdifferenttypesofpistolsandguns.Theexperimentswereconductedona
benchmarkgundatabasecalledInternetMovieFirearmsDatabase(IMFDB).Theresultsobtained
suggestthattheproposedapproachispromisingandoutperformsitscounterparts.
withsuchterroristincidents.Theseincidentshaveanegativeimpactonpublicsecurityandcause
panicamongcitizens.Fromthispoint,facingapandemicofweaponviolencehasbecomeanimportant
researchtopic.Onewaytoreducethiskindofviolenceistopreventitviaremotedetectionandtogive
anappropriateresponseinashorttime.Videosurveillanceistheprocessofmonitoringthebehavior
ofpeopleandobjects.Surveillancesystemscanbeemployedinsecurityapplicationsaslegalevidence.
Moreover,itisusedwidelyinsuspiciousactivitydetectionapplications.Intelligentvideosurveillance
systems(IVSSs)aretheuseofautomaticvideoanalyticstoenhancetheeffectivenessoftraditional
surveillancesystems.WiththerapiddevelopmentinDeepLearning(DL),itisnowwidelyusedto
addresstheproblemsexistingintraditionaldetectiontechniques.Inthisarticle,anapproachtodetect
pistolsandgunsinvideosurveillancesystemsisproposed.Thepresentedapproachdoesnotneed
anyinvasivetoolsintheweapondetectionprocess.ItusesDLintheclassificationandthedetection
processes.TheproposedapproachenhancestheobtainedresultsbyapplyingTransferLearning(TL).
ItemploystwodifferentDLtechniques:AlexNetandGoogLeNet.Experimentalresultsverifythe
adaptabilityofdetectingdifferenttypesofpistolsandguns.Theexperimentswereconductedona
benchmarkgundatabasecalledInternetMovieFirearmsDatabase(IMFDB).Theresultsobtained
suggestthattheproposedapproachispromisingandoutperformsitscounterparts.