Kanade-Lucas-Tomasi (KLT) Feature Tracker

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<strong>Feature</strong> selection Computer Vision (EEE6503) Fall 2009, Yonsei Univ.
Declaring a feature “Lost Lost” • A feature point falls outside of the image. • An image patch around the tracked point varies too much between image I and image J. (A cost function (SSD) is larger than a threshold). 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 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: Feature selection Computer Vision (
Page 35 and 36: References • Bruce D. Lucas and T

Kanade-Lucas-Tomasi (KLT) Feature Tracker

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