Kanade–Lucas–Tomasi feature tracker

In computer vision, the Kanade–Lucas–Tomasi (KLT) feature tracker is an approach to feature extraction. It is proposed mainly for the purpose of dealing with the problem that traditional image registration techniques are generally costly. KLT makes use of spatial intensity information to direct the search for the position that yields the best match. It is faster than traditional techniques for examining far fewer potential matches between the images.

The translational image registration problem can be characterized as follows: Given two functions ${\displaystyle F(x)}$ and ${\displaystyle G(x)}$, representing values at each location ${\displaystyle x}$, where ${\displaystyle x}$ is a vector, in two images, respectively, we wish to find the disparity vector ${\displaystyle h}$ that minimizes some measure of the difference between ${\displaystyle F(x+h)}$ and ${\displaystyle G(x)}$, for ${\displaystyle x}$ in some region of interest ${\displaystyle R}$.

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