Kanade-Lucas-Tomasi (KLT) Feature Tracker

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Why pyramid representation? • Standard <strong>KLT</strong> algorithm can deal with small pixel displacement. • Solution for this is a pyramidal implementation. • Let I 0 =I be the 0 th level image • The pyramid representation is built recursively. I 0 → I 1 → I 2 → I 3 → I Lm …(Lm: 2~4) • I L-1 : the image at level L-1 I L : the image at level L Computer Vision (EEE6503) Fall 2009, Yonsei Univ.
Pyramidal feature tracking • A given point u in I, find its corresponding location v=u+d. • Corresponding point of u(u 0 ) on the pyramidal image I L is u L • Simple overall pyramid tracking algorithm d Lm is computed at the pyramid level L m d Lm-1 is computed with an initial guess of d Lm at L m-1 This continues up to the level 0 Computer Vision (EEE6503) Fall 2009, Yonsei Univ.
Page 1 and 2: Kanade-Lucas Kanade Lucas-Tomasi To
Page 3 and 4: Motivation Image sequence Computer
Page 5 and 6: Motivation Feature point tracking C
Page 7 and 8: 3D Reconstruction results Computer
Page 9 and 10: Problem Statement • I, J : 1 st a
Page 11: Pyramid Implementation Computer Vis
Page 15 and 16: Pyramidal feature tracking • Assu
Page 17 and 18: Standard KLT optical flow computati
Page 19 and 20: Standard KLT optical flow computati
Page 21 and 22: Iterative KLT algorithm Computer Vi
Page 23 and 24: Iterative KLT optical flow computat
Page 25 and 26: Computer Vision (EEE6503) Fall 2009
Page 27 and 28: Feature selection 1. Compute the G
Page 29 and 30: Feature selection Computer Vision (
Page 31 and 32: Feature selection Computer Vision (
Page 33 and 34: Declaring a feature “Lost Lost”
Page 35 and 36: References • Bruce D. Lucas and T

Kanade-Lucas-Tomasi (KLT) Feature Tracker

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