3d tracking : chapter4 natural features, model-based tracking
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This document summarizes different methods for monocular model-based 3D tracking of rigid objects using a survey format. It discusses edge-based methods like RAPiD that match model outlines to straight line segments extracted from images. It also reviews optical flow-based methods that track points between frames and techniques that combine optical flow with edge tracking. Additionally, it examines template matching approaches, interest point detection methods like Harris-Stephen and Shi-Tomasi, interest point matching using correlation windows, and pose estimation by tracking planes defined by sets of points. The document provides an overview of key algorithms for model-based 3D tracking and segmentation.
3d tracking : chapter4 natural features, model-based tracking
- 1. Monocular Model-Based 3D Tracking of Rigid Objects: A Survey2008. 12. 15.백운혁Chapter 4. Natural Features, Model-Based Tracking
- 3. AgendaMonocular Model-Based 3D Tracking of Rigid Objects : A SurveyChapter 4. Natural Features, Model-Based Tracking4.1. Edge-Based Methods4.2. Optical Flow-Based Methods4.3. Template Matching4.4. Interest Point-Based Methods4.5. Tracking Without 3D Models
- 4. 4.1 Edge-Based Methodsstraight line segments and to fit the model outlines
- 5. 4.1.1 RAPiD
- 6. 4.1.1 RAPiDOriginControl pointControl point in camera coordinatesMotion
- 7. 4.1.1 RAPiD
- 8. 4.1.1 RAPiDdistanceis vector made of the distances
- 9. 4.1.2 Making RAPiD RobustMinimize the distanceControl points lying on the same object edge are grouped into primitives. And a whole primitive can be rejected from the pose estimation.RANSAC methodologyThe number of edge strength maxima visible
- 10. 4.1.3 Explicit Edge ExtractionThe middle point, the orientation and the length of the segmentOf a model segmentOf a an extracted segmentMahalanobis distanceIs the covariance matrixThe pose is then estimated by minimizing
- 11. 4.2 Optical Flow-Based MethodsIts corresponding location in the next imageThe projection of a point in an image at time
- 12. 4.2.1 Using Optical Flow AloneNormal optical flowFor large motionsCauses error accumulation
- 13. 4.2.2 Combining Optical Flow and EdgesTo avoid error accumulationDepends of the pose and the image spatial gradients at timeIs a vector made of the temporal gradient at the chosen locations
- 14. 4.3 Template MatchingTo register a 2D template to an image under a family of deformations
- 15. 4.3.1 2D TrackingTo find the parameters of some deformation That warps a template into the input image is the pseudo-inverse of the Jacobian matrix of computed at
- 16. 4.4 Interest Point-Based MethodsUse localized featuresRely on matching individual features across images and are therefore easy to robustify against partial occlusions or matching errors
- 17. 4.4.1 Interest Point DetectionHarris-Stephen detector / Shi-Tomasi detectorThe pixels can be classified from the behavior of the eigen values of The coefficients of are the sums over a windowof the first derivatives and of image intensitieswith respect to pixel coordinates
- 18. 4.4.2 Interest Point Matchingto use7x7 correlation windowsreject matches for which measure is less than 0.8search of correspondents for a maximum movement of 50 pixelsKanade-Lucas-Tomasi trackerKeep the points that choose each other
- 19. 4.4.3 Pose Estimation by Tracking PlanesPose Estimation for Planar Structures
- 20. Thanks for your attention