Introduction to Object Introduction to Object TrackingTracking

Presented by Youyou WangPresented by Youyou WangCS643 Texas A&M UniversityCS643 Texas A&M University

OutlinesOutlines

IntroductionIntroduction RepresentationRepresentation Feature SelectionFeature Selection Object DetectionObject Detection Object TrackingObject Tracking Future DirectionsFuture Directions

Introduction- ObjectivesIntroduction- Objectives

Object tracking is an important task within the field of computer vision. motion-based recognition automated surveillance video indexing human-computer interaction traffic monitoring vehicle navigation

Introduction - ProblemsIntroduction - Problems

—loss of information caused by projection of the 3D world on a 2D image,

—noise in images, —complex object motion, —nonrigid or articulated nature of objects, —partial and full object occlusions, —complex object shapes, —scene illumination changes, —real-time processing requirements.

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IntroductionIntroduction RepresentationRepresentation

ShapeShape AppearanceAppearance

Feature SelectionFeature Selection Object DetectionObject Detection Object TrackingObject Tracking Future DirectionsFuture Directions

Representation- ShapeRepresentation- Shape

—Points.

—Object silhouette and contour.

—Primitive geometric shapes.

—Articulated shape models.

—Skeletal models.

Representation- AppearanceRepresentation- AppearanceProbability densities of object appearance

TemplatesActive appearance modelsMulti-view appearance models

OutlinesOutlines

IntroductionIntroduction RepresentationRepresentation Feature SelectionFeature Selection Object DetectionObject Detection Object TrackingObject Tracking Future DirectionsFuture Directions

Feature SelectionFeature SelectionColorColorEdgeEdgeTextureTextureOptical FlowOptical Flow

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IntroductionIntroduction RepresentationRepresentation Feature SelectionFeature Selection Object DetectionObject Detection

Point detectorPoint detector Background subtractionBackground subtraction Image segmentationImage segmentation Supervised learningSupervised learning

Object TrackingObject Tracking Future DirectionsFuture Directions

Object Detection- Point DetectorObject Detection- Point Detector

Point DetectorPoint Detector

2

2,

( , ) x x y

x y x y y

I I IM w x y

I I I

Fine/Low Coarse/High• SIFT (Lowe)2

Find local maximum of:– Difference of Gaussians in

space and scale

scale

x

y

DoG

D

oG

HarrisHarris

SIFTSIFT

KLTKLT

Object Detection- Background Object Detection- Background SubtractionSubtraction

Background SubtractionBackground SubtractionMixture of GaussianMixture of GaussianEigen-backgroundEigen-background

Object Detection- SegmentationObject Detection- Segmentation

Image SegmentationImage SegmentationMean-shiftMean-shiftGraph-cutGraph-cutActive ContourActive Contour

Object Detection-Supervised Object Detection-Supervised LearningLearning

Supervised LearningSupervised LearningAda-boostingAda-boostingSVMSVM

OutlinesOutlines

IntroductionIntroduction RepresentationRepresentation Feature SelectionFeature Selection Object DetectionObject Detection Object TrackingObject Tracking

Point TrackingPoint Tracking Kernel TrackingKernel Tracking Silhouette TrackingSilhouette Tracking

Future DirectionsFuture Directions

Object Tracking

Point TrackingPoint Tracking Kernel TrackingKernel Tracking Silhouette TrackingSilhouette Tracking

Object Tracking – Point TrackingObject Tracking – Point Tracking

Deterministic Methods for Correspondence —Proximity —Maximum velocity —Small velocity change —Common motion —Rigidity

Object Tracking – Point TrackingObject Tracking – Point Tracking

Statistical Methods for CorrespondenceKalman FiltersParticle Filters

x

Posterior

Object Tracking – Point TrackingObject Tracking – Point Tracking

http://www.youtube.com/watch?http://www.youtube.com/watch?v=6TG_pDEhXME&feature=relatedv=6TG_pDEhXME&feature=related

Object Tracking – Kernel TrackingObject Tracking – Kernel Tracking

Template and Density-Based Appearance Models

Multiview Appearance Models

Object Tracking – Kernel TrackingObject Tracking – Kernel Tracking

http://www.youtube.com/watch?http://www.youtube.com/watch?v=tbHWvPWhVh8&feature=relatedv=tbHWvPWhVh8&feature=related

Object Tracking - Silhouette Object Tracking - Silhouette TrackingTracking

Shape Matching Contour Tracking

Object Tracking - Silhouette Object Tracking - Silhouette TrackingTracking

http://www.youtube.com/watch?v=Ohttp://www.youtube.com/watch?v=OpDjjNRfWZ4&feature=relatedpDjjNRfWZ4&feature=related

http://www.youtube.com/watch?http://www.youtube.com/watch?v=WIoGdhkfNVE&feature=relatedv=WIoGdhkfNVE&feature=related

OutlinesOutlines

IntroductionIntroduction RepresentationRepresentation Feature SelectionFeature Selection Object DetectionObject Detection Object TrackingObject Tracking Future DirectionsFuture Directions

Future Direction

Directions Integration of contextual information. Online Learning

Problems smoothness of motion minimal amount of occlusion illumination constancy high contrast with respect to background

Thank YouThank You