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Kanade Lucas Tomasi Tracker PowerPoint Presentation
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Kanade Lucas Tomasi Tracker

Kanade Lucas Tomasi Tracker

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Kanade Lucas Tomasi Tracker

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    1. Kanade Lucas Tomasi Tracker Ankit Gupta (1999183) Vikas Nair (1999219) Supervisor Prof M. Balakrishnan Electrical Engineering Department IIT Delhi

    2. Tracking and its Applications Tracking is to follow features from one frame to another in an image sequence The definition of a feature depends from implementation to implementation Tracking finds many real time applications Survelliance systems Defence applications Robotic arm

    3. A Tracking Example

    4. Kanade Lucas Tomasi Tracker Algorithm proposed by Kanade and Lucas Definition of a good feature extended by Lucas and Tomasi Implementation of the algorithm by vision group at Stanford

    5. KLT Control Graph

    6. KLT Control Graph

    7. Select Good Features Features are dependent on the method We select those features that can be tracked well Optimal by Construction

    8. Select Good Features The image is smoothened by convolving with a Gaussian function The gradient of each window is calculated by convolving it with derivative of Gaussian of sigma A list of the windows is made

    9. Selection Feature windows sorted according to eigenvalues of G matrix calculated as G = ?(ggTw)da Required top features are selected Minimum distance between features is maintained

    10. Properties of G Above the Image noise level Well Conditioned These properties map onto the eigenvalues characteristics

    11. Eigenvalue Characteristics Small, small : Constant intensity profile Large, small : Unidirectional Pattern Large, large : Corners, salt-and-pepper textures

    12. Constant Intensity Profile

    13. Unidirectional pattern

    14. Salt-and-pepper features

    15. Tracking Mathematically Solution of the equation G d = e G = ?(ggTw)da Where G : second order weighted gradient matrix (2?2) e : weighted intensity error (2?1) d : Displacement Vector(2?1) g : Gradient matrix(2?1)

    16. The Pyramid of Images

    17. Tracking Coarsest resolution tracked first Starting point for subsequent resolutions Newton-Raphson iterative minimization between intensities of two windows

    18. Tracking stops when Feature moves by no more than mindist ? is less than ?min Number of iterations exceed the limit Feature is out of bounds Residue is too large

    19. Replace Good Features On the same principle as Select Good Features. Retains existing features i.e. those being tracked well. Keeps a minimum distance between the features.

    20. Parallelism The windows can be threaded The calculation of the G and e The convolution with Gaussian function

    21. Tracking Video

    22. Tracking Video

    23. Thanks