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Object detection is a fundamental step in most of the video analysis applications. There are many research challenges involved in automatic object detection, depending on different scenarios. The most prevalent application of object detection is in the field of multimedia surveillance. In this talk we will discuss the common problems in the object detection in a surveillance video. Further, we will discuss the Gaussian Mixture Model (GMM) based object detection method. While object detection is the basic step of video analysis, higher level semantic interpretation of the scene requires trajectory information. Most of the suspicious event detection methods use tracking as the basic building block. In the second part of the talk, we will discuss particle filter based method of object tracking. To summarize, the aim of the talk is two-fold: (1) Discuss common problems in object detection and tracking (2) Hands on experience of how to use classical methods of GMM and particle filtering in problem solving.
Citation preview
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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