Introduction to Object Tracking Presented by Youyou Wang CS643 Texas A&M University

Outlines Introduction Introduction Representation Representation Feature Selection Feature Selection Object Detection Object Detection Object Tracking Object Tracking Future Directions Future Directions

Introduction- 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 - 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.

Outlines Introduction Introduction Representation Representation Shape Shape Appearance Appearance Feature Selection Feature Selection Object Detection Object Detection Object Tracking Object Tracking Future Directions Future Directions

Representation- Shape —Points. —Object silhouette and contour. —Primitive geometric shapes. —Articulated shape models. —Skeletal models.

Representation- Appearance Probability densities of object appearance Templates Active appearance models Multi-view appearance models

Outlines Introduction Introduction Representation Representation Feature Selection Feature Selection Object Detection Object Detection Object Tracking Object Tracking Future Directions Future Directions

Feature Selection Color Color Edge Edge Texture Texture Optical Flow Optical Flow

Outlines Introduction Introduction Representation Representation Feature Selection Feature Selection Object Detection Object Detection Point detector Point detector Background subtraction Background subtraction Image segmentation Image segmentation Supervised learning Supervised learning Object Tracking Object Tracking Future Directions Future Directions

Object Detection- Point Detector Point Detector Point Detector Fine/LowCoarse/High SIFT (Lowe) 2 Find local maximum of: –Difference of Gaussians in space and scale scale x y  DoG  Harris Harris SIFT SIFT KLT KLT

Object Detection- Background Subtraction Background Subtraction Background Subtraction Mixture of Gaussian Mixture of Gaussian Eigen-background Eigen-background

Object Detection- Segmentation Image Segmentation Image Segmentation Mean-shift Mean-shift Graph-cut Graph-cut Active Contour Active Contour

Object Detection-Supervised Learning Supervised Learning Supervised Learning Ada-boosting Ada-boosting SVM SVM

Outlines Introduction Introduction Representation Representation Feature Selection Feature Selection Object Detection Object Detection Object Tracking Object Tracking Point Tracking Point Tracking Kernel Tracking Kernel Tracking Silhouette Tracking Silhouette Tracking Future Directions Future Directions

Object Tracking Point Tracking Point Tracking Kernel Tracking Kernel Tracking Silhouette Tracking Silhouette Tracking

Object Tracking – Point Tracking Deterministic Methods for Correspondence —Proximity —Maximum velocity —Small velocity change —Common motion —Rigidity

Object Tracking – Point Tracking Statistical Methods for Correspondence Kalman Filters Particle Filters x Posterior

Object Tracking – Point Tracking DEhXME&feature=related DEhXME&feature=related

Object Tracking – Kernel Tracking Template and Density-Based Appearance Models Multiview Appearance Models

Object Tracking – Kernel Tracking PWhVh8&feature=related PWhVh8&feature=related

Object Tracking - Silhouette Tracking Shape Matching Contour Tracking

Object Tracking - Silhouette Tracking NRfWZ4&feature=related NRfWZ4&feature=related NRfWZ4&feature=related NRfWZ4&feature=related hkfNVE&feature=related hkfNVE&feature=related

Outlines Introduction Introduction Representation Representation Feature Selection Feature Selection Object Detection Object Detection Object Tracking Object Tracking Future Directions Future 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

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