Kanade-Lucas-Tomasi (KLT) Feature Tracker

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<strong>Feature</strong> selection • Goal is to find the location of v on J corresponding to u on I. How to detect u on I • Find the point u whose G matrix is non-singular The minimum eigenvalue of G must larger than a threshold. Computer Vision (EEE6503) Fall 2009, Yonsei Univ.
<strong>Feature</strong> selection 1. Compute the G matrix and its minimum eigenvalue λ m at every pixel in the image I. 2. Call λ max the maximum value of λ m over the whole image. 3. Retain the image pixels that have a λ m value larger than a threshold. 4. Retain the local maximum pixels (a pixel is kept if its λ m value is larger than that of any other pixel in its 3×3 neighborhood). 5. Keep the subset of those pixels so that the minimum distance between any pair of pixels is larger than a given threshold distance. 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: Computer Vision (EEE6503) Fall 2009
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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