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Tutorial 7Object Detection and Tracking

Mukesh Saini

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LayoutObject detection

ChallengesGMM based method

TrackingChallengesParticle filter based tracking

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Object DetectionGoal: To detect the regions of the image that are

semantically important to us:PeopleVehicleBuildings

ApplicationCrowd managementTraffic managementVideo compression, video surveillance, vision-

based control, human-computer interfaces, medical imaging, augmented reality, and robotics…

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Object Detection in ImagesSubjectively definedGenerally template basedMainly done by image segmentation

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Object Detection in VideosRelatively Moving – ObjectRelatively Static - Background

The goal here is to differentiate the moving object from background!

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Surveillance VideoStatic cameraBackground relatively staticSubtract the background image from current

imageIdeally this will leave the moving objectsThis is not an ideal world…

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Feature BasedObjects are modeled in terms of featuresFeatures are chosen to  handle changes in

illumination, size and orientationShape based – Very hard Color based – Low cost but not accurate

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Template BasedExample template are givenObject detection becomes matching featuresImage subtraction, correlation

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Motion BasedModel backgroundSubtract from the current imageLeft are moving objects Remember! This is not a real world…

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Problems in Modeling BackgroundAcquisition noiseIllumination variationClutterNew object introduced into backgroundObject may not move continuously

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Outline of Object DetectionDetermine the background and foreground

pixelsDraw contours around foreground pixelsUse heuristics to merge these contours

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Ideal WorldSingle value modeling of backgroundAnything different is foreground

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Static BackgroundEach pixel resulted from a particular surface

under particular lighteningSingle Gaussian is enough ( )

IfPixel belongs to background, else foreground

,

*5.2tP

Background ForegroundForeground

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Whenever a pixel matches the background Gaussian, update the background model i.e.IfThenStandard deviation updated accordingly

Illumination Variation

tP tt *5.2ttt )1(1

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ClutterThink of tree leaves…Multiple surfaces, still part of backgroundGaussian Mixture ModelUpdate each Gaussian after matching

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Static Object IntroducedThink of flower pot…Background model should adapt to this

changeUse Gaussian for new surface as well

Few extra Gaussians for the foreground

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Measuring PersistenceModeled as prior weight wMore persistent Gaussians belong to

backgroundIf a new pixel does not match to any exiting

Gaussians, least persistent Gaussian is replaced with a new Gaussian with:

And standard variation = a large value

tt P

t

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Background SelectionA background Gaussian will have

More persistence – high wLess variation – low Sort Gaussians wrt

Pick top k Gaussians as background such that

If pixel belongs to one of these, it’s a background pixel

Twk

iik

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minarg

ttw /

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Adaptive Background ModelEvery pixel is modeled as mixture of

GaussiansMore persistent Gaussians belong to

background and others to foregroundThe Gaussians are updated after each frame

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Connecting the DotsThe output of background modeling is a

binary imageDilation/Erosion can further reduce noiseContour drawingBounding boxes

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Revisit the problemsProblems

Slow moving background – clutterNew object introduced into backgroundIllumination variationObject may not move continuously

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Thank YouQ & A