Kanade-Lucas-Tomasi (KLT) Feature Tracker

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Standard <strong>KLT</strong> optical flow computation • Goal is to find that minimizes the matching function • To find the optimum • Let us substitute by its 1 st order Taylor expansion about the point frame difference Image gradient Computer Vision (EEE6503) Fall 2009, Yonsei Univ.
Standard <strong>KLT</strong> optical flow computation 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 and 14: Pyramidal feature tracking • A gi
Page 15 and 16: Pyramidal feature tracking • Assu
Page 17: 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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