  We propose a new algorithm for real-time detection and tracking of elliptic
patterns suitable for real-world robotics applications. The method fits
ellipses to each contour in the image frame and rejects ellipses that do not
yield a good fit. The resulting detection and tracking method is lightweight
enough to be used on robots' resource-limited onboard computers, can deal with
lighting variations and detect the pattern even when the view is partial. The
method is tested on an example application of an autonomous UAV landing on a
fast-moving vehicle to show its performance indoors, outdoors, and in
simulation on a real-world robotics task. The comparison with other well-known
ellipse detection methods shows that our proposed algorithm outperforms other
methods with the F1 score of 0.981 on a dataset with over 1500 frames. The
videos of experiments, the source codes, and the collected dataset are provided
with the paper at https://theairlab.org/landing-on-vehicle .
