ForegrounddetectionusingGaussianmixturemodels expandallinpage

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vision.ForegroundDetectorSystemobject

Package:vision

DescriptionTheForegroundDetectorSystemobjectcomparesacolororgrayscalevideoframetoabackgroundmodeltodeterminewhetherindividualpixelsarepartofthebackgroundortheforeground.Itthencomputesaforegroundmask.Byusingbackgroundsubtraction,youcandetectforegroundobjectsinanimagetakenfromastationarycamera.

Note:StartinginR2016b,insteadofusingthestepmethodtoperformtheoperationdefinedbytheSystemobject,youcancalltheobjectwitharguments,asifitwereafunction.Forexample,y=step(obj,x)andy=obj(x)performequivalentoperations.

Constructiondetector=vision.ForegroundDetectorreturnsaforegrounddetectorSystemobject,detector.Givenaseriesofeithergrayscaleorcolorvideoframes,theobjectcomputesandreturnstheforegroundmaskusingGaussianmixturemodels(GMM).

detector=vision.ForegroundDetector(Name,Value)returnsaforegrounddetectorSystemobject,detector,witheachspecifiedpropertynamesettothespecifiedvalue.NamecanalsobeapropertynameandValueisthecorrespondingvalue.YoucanspecifyseveralnamevaluepairargumentsinanyorderasName1,Value1,,NameN,ValueN.

CodeGenerationSupport

SupportsMATLAB Functionblock:No

UsingMATLABhosttarget:Generatesplatformdependentlibrary

NotusingMATLABhosttarget:GeneratesportableCcode

SystemObjectsinMATLABCodeGeneration.

CodeGenerationSupport,UsageNotes,andLimitations.

Properties

Adaptlearningrate,specifiedasthecommaseparatedpairconsistingof'AdaptLearningRate'andalogicalscalar'true'or'false'.ThispropertyenablestheobjecttoadaptthelearningrateduringtheperiodspecifiedbytheNumTrainingFramesproperty.Whenyousetthispropertytotrue,theobjectsetstheLearningRatepropertyto1/(currentframenumber).Whenyousetthispropertytofalse,theLearningRatepropertymustbesetateachtimestep.

Numberofinitialvideoframesfortrainingbackgroundmodel,specifiedasthecommaseparatedpairconsistingof'NumTrainingFrames'andaninteger.WhenyousettheAdaptLearningRatetofalse,thispropertywillnotbeavailable.

Learningrateforparameterupdates,specifiedasthecommaseparatedpairconsistingof'LearningRate'andanumericscalar.Specifythelearningratetoadaptmodelparameters.Thispropertycontrolshowquicklythemodeladaptstochangingconditions.Setthispropertyappropriatelytoensurealgorithmstability.

AdaptLearningRateAdaptlearningrate'true'(default)|'false'

NumTrainingFramesNumberofinitialvideoframesfortrainingbackgroundmodel150(default)|integer

LearningRateLearningrateforparameterupdates0.005(default)|numericscalar

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WhenyousetAdaptLearningRatetotrue,theLearningRatepropertytakeseffectonlyafterthetrainingperiodspecifiedbyNumTrainingFramesisover.

WhenyousettheAdaptLearningRatetofalse,thispropertywillnotbeavailable.Thispropertyistunable.

Thresholdtodeterminebackgroundmodel,specifiedasthecommaseparatedpairconsistingof'MinimumBackgroundRatio'andanumericscalar.Setthispropertytorepresenttheminimumoftheaprioriprobabilitiesforpixelstobeconsideredbackgroundvalues.Multimodalbackgroundscannotbehandled,ifthisvalueistoosmall.

NumberofGaussianmodesinthemixturemodel

NumberofGaussianmodesinthemixturemodel,specifiedasthecommaseparatedpairconsistingof'NumGaussians'andapositiveinteger.Typicallythisvalueis3,4or5.Setthisvalueto3orgreatertobeabletomodelmultiplebackgroundmodes.

Initialmixturemodelvariance,specifiedasthecommaseparatedpairconsistingof'InitialVariance'andasanumericscalarorthe'Auto'charactervector.

ImageDataType InitialVariance

double/single (30/255)^2

uint8 30^2

Thispropertyappliestoallcolorchannelsforcolorinputs.

Methodsclone Createforegrounddetectorwithsamepropertyvalues

getNumInputs Numberofexpectedinputstostepmethod

getNumOutputs Numberofoutputsfromstepmethod

isLocked Lockedstatusforinputattributesandnontunableproperties

release Allowpropertyvalueandinputcharacteristicschanges

reset ResettheGMMmodeltoitsinitialstate

step DetectforegroundusingGaussianmixturemodels

Examples

Createsystemobjectstoreadfile.

videoSource=vision.VideoFileReader('viptraffic.avi',...'ImageColorSpace','Intensity','VideoOutputDataType','uint8');

Settingframesto5becauseitisashortvideo.Setinitialstandarddeviation.

MinimumBackgroundRatioThresholdtodeterminebackgroundmodel0.7(default)|numericscalar

NumGaussiansNumberofGaussianmodesinthemixturemodel5(default)|positiveinteger

InitialVarianceInitialmixturemodelvariance'Auto'(default)|numericscalar

DetectMovingCarsInVideo

OpenScript

javascript:void(0);http://www.mathworks.com/help/vision/ref/vision.foregrounddetector-class.html#bsy_s0n-5http://www.mathworks.com/help/vision/ref/vision.foregrounddetector-class.html#bsy_s0n-5http://www.mathworks.com/help/vision/ref/vision.foregrounddetector-class.html#bsy_s0n-5http://www.mathworks.com/help/vision/ref/vision.foregrounddetector.clone.htmlhttp://www.mathworks.com/help/vision/ref/vision.foregrounddetector.getnuminputs.htmlhttp://www.mathworks.com/help/vision/ref/vision.foregrounddetector.getnumoutputs.htmlhttp://www.mathworks.com/help/vision/ref/vision.foregrounddetector.islocked.htmlhttp://www.mathworks.com/help/vision/ref/vision.foregrounddetector.release.htmlhttp://www.mathworks.com/help/vision/ref/vision.foregrounddetector.reset.htmlhttp://www.mathworks.com/help/vision/ref/vision.foregrounddetector.step.htmlmatlab:openExample('vision/TrackCarsExample')

detector=vision.ForegroundDetector(...'NumTrainingFrames',5,...'InitialVariance',30*30);

Performblobanalysis.

blob=vision.BlobAnalysis(...'CentroidOutputPort',false,'AreaOutputPort',false,...'BoundingBoxOutputPort',true,...'MinimumBlobAreaSource','Property','MinimumBlobArea',250);

Insertaborder.

shapeInserter=vision.ShapeInserter('BorderColor','White');

Playresults.Drawboundingboxesaroundcars.

videoPlayer=vision.VideoPlayer();while~isDone(videoSource)frame=step(videoSource);fgMask=step(detector,frame);bbox=step(blob,fgMask);out=step(shapeInserter,frame,bbox);step(videoPlayer,out);end

Releaseobjects.

release(videoPlayer);release(videoSource);

References[1]P.Kaewtrakulpong,R.Bowden,AnImprovedAdaptiveBackgroundMixtureModelforRealtimeTrackingwithShadowDetection,InProc.2ndEuropeanWorkshoponAdvancedVideoBasedSurveillanceSystems,AVBS01,VIDEOBASEDSURVEILLANCESYSTEMS:ComputerVisionandDistributedProcessing(September2001)

[2]Stauffer,C.andGrimson,W.E.L,AdaptiveBackgroundMixtureModelsforRealTimeTracking,ComputerVisionandPatternRecognition,IEEEComputerSocietyConferenceon,Vol.2(06August1999),pp.2246252Vol.2.

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