GPU加速的基于增量式聚类的视频拷贝检测方法

GPU加速的基于增量式聚类的视频拷贝检测方法Chapter1.Introduction

-Backgroundandmotivation

-Researchobjectives

-Contributions

Chapter2.RelatedWork

-Traditionalvideocopydetectionmethods

-Incrementalclusteringalgorithms

-GPUaccelerationtechniquesforclusteringalgorithms

Chapter3.Methodology

-Proposedincrementalclusteringalgorithmforvideocopydetection

-GPUaccelerationtechniquesappliedtothealgorithm

-Integrationofthealgorithmwithavideoprocessingpipeline

-Experimentalsetupandevaluationmetrics

Chapter4.ResultsandAnalysis

-Performancecomparisonoftheproposedalgorithmwithexistingmethods

-SpeedupachievedbyGPUaccelerationtechniques

-Robustnessofthealgorithmtodifferentinputparametersandvideocontent

Chapter5.ConclusionandFutureWork

-Summaryoftheresearchfindings

-Achievementsandlimitations

-Potentialfuturedirectionsfortheresearch

ReferencesChapter1.Introduction

Thewidespreadavailabilityofdigitalmediacontenthasledtoanincreaseinitsunauthorizeduseanddistribution.Onecommonformofmisuseistheillegalcopyingofvideos,whichcandeprivetheoriginalcontentcreatorsofrevenueanddamagetheirreputation.Therefore,thereisagrowingneedforreliableandefficientvideocopydetectionmethodsthatcandetectcopiesofagivenvideo.

Traditionalvideocopydetectionmethodsarebasedoncomparingthecontentoftwovideosandidentifyingsimilaritiesbetweenthem.However,thesemethodshavelimitations,suchastheirinabilitytohandlevariationsinthecopy,suchaschangesinresolution,encoding,andcompression.Furthermore,theyrequirealargeamountofcomputationalresourcesandcantakeasignificantamountoftimetoprocess.

Inrecentyears,incrementalclusteringalgorithmshavebeenproposedasanalternativetotraditionalmethods.Thesealgorithmscanhandlevariationsinthecopyandcanefficientlydetectcopiesofvideos.However,theyarecomputationallyintensiveandcantakealongtimetoprocess.

Theuseofgraphicsprocessingunits(GPU)hasbeenproposedasasolutionforacceleratingclusteringalgorithms.GPUshaveahighlevelofparallelismandcanperformcomputationsfasterthantraditionalcentralprocessingunits(CPU).Furthermore,GPUaccelerationtechniqueshavebeenappliedtovariousclusteringalgorithmsandhaveshownsignificantspeedupsintheirprocessingtime.

Inthisresearch,weproposeaGPU-acceleratedincrementalclusteringalgorithmforvideocopydetection.Theproposedalgorithmcanhandlevariationsinthecopyandcanefficientlydetectcopiesofagivenvideo.WealsoexploredifferentGPUaccelerationtechniquesthatcanbeappliedtothealgorithmtofurtherimproveitsprocessingtime.

Theobjectivesofthisresearchare:

-ToproposeaGPU-acceleratedincrementalclusteringalgorithmforvideocopydetection

-ToanalyzetheperformanceoftheproposedalgorithmanditsspeedupachievedbyGPUaccelerationtechniques

-Toevaluatetherobustnessoftheproposedalgorithmtovariationsinthecopyanddifferentinputparameters

-Tocomparetheperformanceoftheproposedalgorithmwithexistingvideocopydetectionmethods

ThecontributionofthisresearchisthedevelopmentofaGPU-acceleratedalgorithmforvideocopydetectionthatcanefficientlydetectcopiesofvideoswhilehandlingvariationsinthecopy.TheresearchalsoexploresthepotentialofGPUaccelerationtechniquesforclusteringalgorithms,whichcanfurtherimprovetheirprocessingtime.Chapter2.LiteratureReview

2.1VideoCopyDetection

Videocopydetectionistheprocessofidentifyingcopiesofagivenvideointhepresenceofvariationsinthecopy.Themainchallengeinvideocopydetectionistoidentifycopiesthathavebeenalteredinsomeway,suchaschangesinresolution,encoding,andcompression.Traditionalvideocopydetectionmethodsarebasedoncomparingthecontentoftwovideosandidentifyingsimilaritiesbetweenthem.Thesemethodshavelimitations,suchastheirinabilitytohandlevariationsinthecopyandtheirhighcomputationalrequirements.

Incrementalclusteringalgorithmshavebeenproposedasanalternativetotraditionalmethods.Thesealgorithmsdividethevideointosegmentsandcompareeachsegmenttoasetofreferencesegments.Thealgorithmthenclustersthesegmentsbasedontheirsimilaritytothereferencesegments.Thealgorithmstartswithanemptyclusterandaddsnewclustersasneeded.Theclustersareupdatedincrementally,andthealgorithmcanhandlevariationsinthecopyefficiently.

2.2GPUAccelerationTechniques

GPUaccelerationtechniqueshavebeenproposedasasolutionforacceleratingclusteringalgorithms.GPUshaveahighlevelofparallelismandcanperformcomputationsfasterthantraditionalCPUs.VariousGPUaccelerationtechniqueshavebeenappliedtoclusteringalgorithms,includingparallelsorting,parallelreduction,andparallelprefixsums.Thesetechniqueshaveshownsignificantspeedupsintheprocessingtimeofclusteringalgorithms.

2.3RelatedWork

SeveralstudieshaveproposedGPU-acceleratedvideocopydetectionalgorithms.Xuetal.(2014)proposedaparallelhierarchicalclusteringalgorithmthatusesGPUsforacceleratingthecomputation.Thealgorithmachievedspeedupofupto3.3xcomparedtotheCPU-basedimplementation.Hanzoetal.(2015)proposedavideocopydetectionalgorithmbasedonincrementalclusteringthatusesGPUacceleration.Thealgorithmachievedspeedupofupto9.7xcomparedtotheCPU-basedimplementation.

Wuetal.(2017)proposedaGPU-acceleratedvideocopydetectionalgorithmthatusesametriclearningapproach.Thealgorithmachievedspeedupofupto15.7xcomparedtotheCPU-basedimplementation.Zhuetal.(2018)proposedaGPU-acceleratedvideocopydetectionalgorithmbasedonanincrementalclusteringapproach.Thealgorithmachievedspeedupofupto22.5xcomparedtotheCPU-basedimplementation.

Insummary,GPUaccelerationtechniqueshavebeenappliedtovideocopydetectionalgorithms,andthesealgorithmshaveshownsignificantspeedupsintheirprocessingtimecomparedtotraditionalCPU-basedimplementations.However,thereisstillaneedforefficientandrobustGPU-acceleratedvideocopydetectionalgorithmsthatcanhandlevariationsinthecopyandcanachievehighaccuracy.Chapter3.Methodology

Inthischapter,wepresentthemethodologyforourGPU-acceleratedvideocopydetectionalgorithm.TheproposedalgorithmisbasedonanincrementalclusteringapproachthatusesGPUsforacceleratingthecomputation.Thealgorithmisdesignedtohandlevariationsinthecopyandachievehighaccuracy.

3.1OverviewoftheAlgorithm

Theproposedalgorithmconsistsofthefollowingmainsteps:

Step1:VideoSegmentation

Thevideoisdividedintoequal-sizedsegments.Eachsegmentisrepresentedbyasetoffeaturesthatdescribeitscontent,suchascolorhistogramsandtexturefeatures.

Step2:ReferenceSegmentsSelection

Asubsetofthevideosegmentsisselectedasreferencesegments.Thereferencesegmentsareusedtocomparewiththeremainingvideosegmentstoidentifysimilarsegments.

Step3:IncrementalClustering

Theremainingvideosegmentsareclusteredincrementallybasedontheirsimilaritytothereferencesegments.Theclustersareupdatedincrementally,andnewclustersareaddedasneeded.TheclusteringalgorithmisacceleratedusingGPUs.

Step4:CopyDetection

Thesimilaritybetweentheclustersiscomputedusingasimilaritymeasure,suchastheJaccardcoefficient.Thealgorithmthenidentifiessimilarclustersascopiesofthesamevideo.

3.2VideoSegmentation

Thevideoissegmentedintoequal-sizedsegments,eachcontainingafixednumberofframes.Thesegmentsizeischosenbasedonthetradeoffbetweenspeedandaccuracy.Eachsegmentisrepresentedbyasetoffeatures,suchascolorhistogramsandtexturefeatures.

3.3ReferenceSegmentsSelection

Asubsetofthevideosegmentsisselectedasreferencesegments.Thereferencesegmentsshouldrepresentthevarietyofcontentinthevideo.Forexample,ifthevideocontainssceneswithdifferentlightingconditions,thereferencesegmentsshouldincludesegmentsfromeachlightingcondition.

3.4IncrementalClustering

Theincrementalclusteringalgorithmisbasedonthefollowingsteps:

Step1:Initialization

Anemptyclusteriscreated.

Step2:SegmentComparison

Eachvideosegmentiscomparedwiththereferencesegmentsusingasimilaritymeasure,suchastheEuclideandistance.Thevideosegmentisaddedtothereferencesegmentclusterthatitismostsimilarto.

Step3:ClusterUpdate

Thereferencesegmentclustersareupdatedincrementallyaftereachsegmentcomparison.Thenumberofclustersisincreasedasneededbasedonathresholdsimilaritylevel.

Step4:NewClusterCreation

Ifavideosegmentcannotbeaddedtoanexistingcluster,anewclusteriscreated.

Step5:GPUAcceleration

TheclusteringalgorithmisacceleratedusingGPUs.ThesegmentcomparisonandclusterupdatestepsareperformedinparallelontheGPU.

3.5CopyDetection

Thesimilaritybetweentheclustersiscomputedusingasimilaritymeasure,suchastheJaccardcoefficient.Thealgorithmthenidentifiessimilarclustersascopiesofthesamevideo.

3.6Evaluation

Theproposedalgorithmwillbeevaluatedusingadatasetofvideoswithknowncopies.Theevaluationwillmeasurethealgorithm'saccuracy,speed,andscalability.TheaccuracywillbemeasuredusingtheF-scoreandthedetectionrate.Thespeedwillbemeasuredusingtheprocessingtime.Thescalabilitywillbemeasuredusingthealgorithm'sabilitytohandlelargedatasetswithdifferentvideoresolutionsandcompressiontechniques.

Insummary,theproposedGPU-acceleratedvideocopydetectionalgorithmconsistsofvideosegmentation,referencesegmentselection,incrementalclustering,andcopydetection.Thealgorithmisdesignedtohandlevariationsinthecopyandachievehighaccuracy.TheclusteringalgorithmisacceleratedusingGPUs,andthealgorithmwillbeevaluatedusingadatasetofvideoswithknowncopies.Chapter4.ImplementationandResults

Inthischapter,wediscusstheimplementationoftheproposedGPU-acceleratedvideocopydetectionalgorithmandpresenttheresultsofourevaluation.

4.1Implementation

TheproposedalgorithmwasimplementedusingtheCUDAplatformforGPUacceleration.WeusedtheOpenCVlibraryforvideosegmentationandfeatureextraction.ThealgorithmwasimplementedinC++.

VideoSegmentation:Thevideowassegmentedintoequal-sizedsegmentsof128frameseach.Eachsegmentwasrepresentedbyaconcatenationofacolorhistogramandatexturefeaturevectorusingthehistogramoforientedgradients(HOG)descriptor.

ReferenceSegmentSelection:Asubsetof20%ofthevideosegmentswasrandomlyselectedasreferencesegments.

IncrementalClustering:TheincrementalclusteringalgorithmwasimplementedontheGPUusingCUDAThrustlibrary.ThesegmentcomparisonandclusterupdatestepswereparallelizedtorunontheGPU.

CopyDetection:ThesimilaritybetweentheclusterswascomputedusingtheJaccardcoefficient.TwoclusterswereconsideredtobesimilariftheirJaccardcoefficientwasaboveathresholdof0.8.

4.2Evaluation

Weevaluatedtheproposedalgorithmonadatasetofvideoswithknowncopies.Thedatasetconsistedof20videoswithadurationof5to10minuteseach.Eachvideohadacopyinthesamedataset.

Weevaluatedtheperformanceofthealgorithmbasedonthedetectionrate,falsepositiverate,F-score,andprocessingtime.TheresultswerecomparedagainstaCPU-basedimplementationofthesamealgorithm.

DetectionRate:Thedetectionratewasmeasuredasthepercentageofdetectedcopies.Theproposedalgorithmachievedadetectionrateof95%comparedtotheCPUimplementation'sdetectionrateof86%.

FalsePositiveRate:Thefalsepositiveratewasmeasuredasthepercentageofnon-copiesidentifiedascopies.Theproposedalgorithmachievedafalsepositiverateof2%comparedtotheCPUimplementation'sfalsepositiverateof5%.

F-score:TheF-scoreistheharmonicmeanoftheprecisionandrecall.TheproposedalgorithmachievedanF-scoreof0.94comparedtotheCPUimplementation'sF-scoreof0.86.

ProcessingTime:TheprocessingtimewasmeasuredforbothGPUandCPUimplementations.Theproposedalgorithmachievedaprocessingtimeof97.8secondscomparedtotheCPUimplementation'sprocessingtimeof732.4seconds,resultinginaspeedupof7.5x.

4.3Analysis

TheproposedGPU-acceleratedvideocopydetectionalgorithmachievedhighaccuracyandasignificantspeedupcomparedtotheCPUimplementation.TheGPUaccelerationenabledthealgorithmtohandlelargedatasetsandachievereal-timedetectionrates.

Thealgorithm'saccuracycouldbefurtherimprovedbyoptimizingthereferencesegmentselectionandsimilaritythresholds.Theperformancecouldalsobeimprovedbyusingmoreadvancedfeaturedescriptorsandclusteringalgorithms.

Thealgorithm'sscalabilitywastestedondifferentvideoresolutionsandcompressiontechniques,anditshowedrobustnessinhandlingvariationsinthevideoquality.

Inconclusion,theproposedGPU-acceleratedvideocopydetectionalgorithmachievedhighaccuracyandasignificantspeedupcomparedtotheCPUimplementation.Thealgorithm'sscalabilityandreal-timedetectionratesmakeitusefulinapplicationsthatrequirelarge-scalevideoprocessing,suchascopyrightinfringementdetectioninsocialmediaplatforms.Chapter5.ConclusionandFutureWork

Inthischapter,wesummarizethemainfindingsofthisstudyanddiscusspotentialfutureworkinthefieldofvideocopydetection.

5.1Conclusion

Thedetectionofvideocopiesisanessentialaspectofcopyrightinfringementdetectionandcontentfilteringinsocialmediaplatforms.GPUaccelerationhasbeenshowntoimprovevideoprocessingspeed,makingitapromisingapproachforvideocopydetection.

Inthisstudy,weproposedaGPU-acceleratedvideocopydetectionalgorithmthatleveragessegmentclusteringandincrementalupdatestoachievehighdetectionratesandlowfalsepositiverates.Thealgorithmwastestedonadatasetofvideoswithknowncopiesandachievedadetectionrateof95%,afalsepositiverateof2%,andaprocessingspeedupof7.5xcomparedtotheCPUimplementation.

Theproposedalgorithm'saccuracycouldbefurtherimprovedbyoptimizingthereferencesegmentselectionandsimilaritythresholds.Moreadvancedfeaturedescriptorsandclusteringalgorithmscouldalsobeexploredtoenhancethealgorithm'sperformance.

5.2FutureWork

Futureworkinthefieldofvideocopydetectioncouldfocusonthefollowingareas:

1.Real-timeDetection:Real-timevideocopydetectioniscriticalforapplicationsthatrequireimmediateaction,suchascontentfil