# Real-Time Ellipse Detection for Robotics Applications

arXiv: https://arxiv.org/abs/2102.12670 · HTML: https://mlsanity.com/paper/2102.12670/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 39 references that are in the map.
- First posted on arXiv 25 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [Ellipse R-CNN: Learning to Infer Elliptical Object from Clustering and Occlusion](https://mlsanity.com/paper/2001.11584/) (2020)
- [Team NimbRo at MBZIRC 2017: Fast Landing on a Moving Target and Treasure Hunting with a Team of MAVs](https://mlsanity.com/paper/1811.05471/) (2018)
- [A Fast Ellipse Detector Using Projective Invariant Pruning](https://mlsanity.com/paper/1608.07470/) (2016)

## Built on by (most cited first, 3 of 3)

- [Deformable One-Dimensional Object Detection for Routing and Manipulation](https://mlsanity.com/paper/2201.06775/) (2022)
- [Efficient Spatial Representation and Routing of Deformable One-Dimensional Objects for Manipulation](https://mlsanity.com/paper/2202.06172/) (2022)
- [Detection and Physical Interaction with Deformable Linear Objects](https://mlsanity.com/paper/2205.08041/) (2022)

## Abstract (arXiv)

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 .