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 .

翻译：我们提出一种新的算法,用于实时探测和跟踪适合现实世界机器人应用的椭圆形图案。 这种方法适合图像框中每个轮廓的省略图案, 拒绝产生良好效果的省略图案。 结果的检测和跟踪方法轻到足以用于机器人在机上资源有限的计算机上, 可以处理照明变异, 并检测模式, 即使视图是局部的。 该方法在自动无人机降落在快速移动的飞行器上, 以展示其室内、 室外和模拟真实世界机器人任务的性能。 与其他众所周知的椭圆形探测方法的比较表明, 我们提议的算法方法优于1500个框架以上的数据集上的0. 981 F1分的其他方法。 实验录像、 源代码 和所收集的数据集都与 https://theairlab.org/landing- on- moble 的论文一起提供。