Robust low complexity feature tracking

In this paper, we present the Kanade-Lucas-Tomasi (KLT) tracking algorithm coupled with a varying integration window, which tracks a small subset of feature points to initialize the approximate motion model between images. For the remaining larger subset of the feature points initial tracking location is predicted by using this motion model, thus improving the tracking result. For an image of size 1000×700, the computational cost is reduced by a factor of 9.5 and tracking 500 features with our method runs in only 64 ms on a commodity 2GHz CPU with 1GB RAM.

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