Deep Learning-Based Automated Classroom Slide Extraction

Zeeshan Azhar ()
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Zeeshan Azhar: Computer Science Department, Barani Institute of Technology, Rawalpindi, Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 2, 380-395

Abstract: Automated extraction of valuable contentfromreal-timeclassroomlecturesholdssignificantpotentialforenhancingeducationalaccessibilityandefficiency.However,capturingthespontaneousinsightsoflivelecturesoftenproveschallengingduetorapidvisualtransitions,instructormovement,anddiverselearningstyles.ThispaperpresentsanovelapproachthatcombinesthestrengthsofYOLOandScale-InvariantFeatureTransform(SIFT)techniquestoautomaticallyextractslidesfromliveclassroomlectures.YOLO,areal-timeobjectdetectionalgorithm,isemployedtoidentifyboardarea,teacher,andotherobjectswithinthevideostream.WhileSIFT,arobustfeature-basedmethod,wasusedtoaccuratelymergekeypointsfrommultiplepicturesofthesameregion.Theproposedmethodinvolvesamulti-stageprocess:first,YOLOdetectsthepotentialplaceoftheteacher,whichoccludedtheboardwithinthevideoframes.Subsequently,theteacherwasremovedfromtheimage.Theboardwasdividedintomultiplesegments,toremoveandmergeredundantcontentScale-invariantfeatureTransform(SIFT)wasemployed.Experimentalresultsonadiversedatasetofclassroomlecturevideosdemonstratedtheeffectivenessoftheproposedmethodinextractingslidesacrossdifferentenvironments,lecturestyles,andrecordingconditions.Thepotentialbenefitsincludeimprovednote-taking,reducedmanualeffortincontentcuration,andenhancedaccessibilitytolecturematerials.Thepresentedapproachcontributestothebroadergoalofleveragingcomputervisionandmachinelearningtechniquestotransformtraditionalclassroomsettingsintomodern,interactive,andadaptivelearningenvironments.

Keywords: DeepLearning; Computervision; Academicassistantsystem; YOLO; Objectdetection; CNN (search for similar items in EconPapers)
Date: 2024
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