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.백운혁
    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 a model segment
    Of a an 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 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 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 of are the sums over a window
    of the first derivatives and of image intensities
    with respect to pixel 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