  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. It can detect complete, partial, and imperfect ellipses in
extreme weather and lighting conditions and is lightweight enough to be used on
robots' resource-limited onboard computers. The method is used on an example
application of 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.
