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KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker
KLT is an implementation, in the C programming language, of a feature
tracker that is of interest to the computer vision community.
The tracker is based on the early work of Lucas and
Kanade
[1] and was developed fully by
Tomasi
and Kanade [2], but the only
published, readily accessible description is contained in the paper by
Shi
and Tomasi [3].
Recently, Tomasi proposed a slight modification which
makes the computation symmetric with respect to the two images; the
resulting equation is fully derived
in the unpublished note by
Birchfield
[4].
Briefly, good features are located by examining the minimum
eigenvalue of each 2 by 2 gradient matrix, and features are
tracked using a Newton-Raphson method of minimizing the difference
between the two windows.
Multiresolution tracking allows for even large displacements between
images.
Currently, the affine computation that evaluates the consistency
of features between non-consecutive frames [3]
is not implemented.
References
- [1] Bruce D. Lucas and Takeo Kanade.
An Iterative Image Registration Technique with an
Application to Stereo Vision.
International Joint Conference on Artificial Intelligence,
pages 674-679, 1981.
- [2] Carlo Tomasi and Takeo Kanade.
Detection and Tracking of Point Features.
Carnegie Mellon University Technical Report CMU-CS-91-132,
April 1991.
- [3] Jianbo Shi and Carlo Tomasi.
Good Features to Track.
IEEE Conference on Computer Vision and Pattern Recognition,
pages 593-600, 1994.
- [4] Stan Birchfield.
Derivation of Kanade-Lucas-Tomasi Tracking Equation.
Unpublished, May 1996.
Last updated: February 10, A. D. 1997 by
Stan Birchfield
birchfield@cs.stanford.edu