Kanade-Lucas-Tomasi (KLT) Feature Tracker

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Flow chart Input 1 st image Select feature points Save feature points Input 2 nd image Track feature points Delete lost feature points Save tracked points Replace 1 st image with 2 nd image Replace 2 st image with next image Select feature points to replace lost feature points Save feature points Computer Vision (EEE6503) Fall 2009, Yonsei Univ.
References • Bruce D. Lucas and Takeo Kanade. “An Iterative Image Registration Technique with an Application to Stereo Vision”. IJCAI, pages 674-679, 1981. • J. Shi and C. Tomasi, "Good Features to Track," CVPR'94 • Jean-Yves Bouguet, “Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the algorithm”, Intel Corporation Microprocessor Research Labs. • http://www.ces.clemson.edu/~stb/klt/ C++ code KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker • OpenCV: CalcOpticalFlowLK, CalcOpticalFlowPyrLK • http://vision.ucla.edu//MASKS/labs.html Matlab code An Invitation to 3D Vision 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 and 32: Feature selection Computer Vision (
Page 33: Declaring a feature “Lost Lost”

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