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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
