3d tracking : chapter4 natural features, model-based tracking

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  • 1. Monocular Model-Based 3D Tracking of Rigid Objects: A Survey
    2008. 12. 15.
    Chapter 4. Natural Features, Model-Based Tracking

2. 3. Agenda
Monocular Model-Based 3D Tracking of Rigid Objects : A Survey
Chapter 4. Natural Features, Model-Based Tracking
4.1. Edge-Based Methods
4.2. Optical Flow-Based Methods
4.3. Template Matching
4.4. Interest Point-Based Methods
4.5. Tracking Without 3D Models
4. 4.1 Edge-Based Methods
straight line segments and to fit the model outlines
5. 4.1.1 RAPiD
6. 4.1.1 RAPiD
Control point
Control point in camera coordinates
7. 4.1.1 RAPiD
8. 4.1.1 RAPiD
is vector made of the distances
9. 4.1.2 Making RAPiD Robust
Minimize the distance
Control points lying on the same object edge are grouped into primitives. And a whole primitive can be rejected from the pose estimation.
RANSAC methodology
The number of edge strength maxima visible
10. 4.1.3 Explicit Edge Extraction
The middle point, the orientation and the length of the segment
Of amodel segment
Of aan extracted segment
Mahalanobis distance
Is the covariance matrix
The pose is then estimated by minimizing
11. 4.2 Optical Flow-Based Methods
Its corresponding location in the next image
The projection of a point in an image at time
12. 4.2.1 Using Optical Flow Alone
Normal optical flow
For large motions
Causes error accumulation
13. 4.2.2 Combining Optical Flow and Edges
To avoid error accumulation
Depends of the pose and the image spatial gradients at time
Is a vector made of the temporal gradient at the chosen locations
14. 4.3 Template Matching
To register a 2D template to an image under a family of deformations
15. 4.3.1 2D Tracking
To find the parametersof 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 Methods
Use localized features
Rely on matching individual features across images and are therefore easy to robustify against partial occlusions or matching errors
17. 4.4.1 Interest Point Detection
Harris-Stephen detector / Shi-Tomasi detector
The pixels can be classified from the behavior of the eigen values of
The coefficients ofare the sums over a window
of the first derivativesand of image intensities
with respect topixel coordinates
18. 4.4.2 Interest Point Matching
to use7x7 correlation windows
reject matches for which measure is less than 0.8
search of correspondents for a maximum movement of 50 pixels
Kanade-Lucas-Tomasi tracker
Keep the points that choose each other
19. 4.4.3 Pose Estimation by Tracking Planes
Pose Estimation for Planar Structures
20. Thanks for your attention