Kanade-Lucas-Tomasi (KLT) Feature Tracker

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Pyramidal feature tracking • Let gL =[gL x gLy ]T be an initial guess at level L ( gL is available from level Lm to level L+1 ) • Residual pixel displacement vector d L =[d L x dL y ]T that minimizes the new image matching error function ε L • Window size (2ω x +1)×(2ω y +1) is constant for all pyramid • gL is used to pre-translate the image patch in 2nd image J → dL is small and therefore easy to compute using <strong>KLT</strong> algorithm Computer Vision (EEE6503) Fall 2009, Yonsei Univ.
Pyramidal feature tracking • Assume that d L is computed, new initial guess g L-1 at level L-1 • Initial guess for the deepest level L m • The final optical flow solution d 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 and 12: Pyramid Implementation Computer Vis
Page 13: Pyramidal feature tracking • A gi
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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