IEEE Trans Pattern Anal Mach Intell. 2017 May;39(5):879-892. doi: 10.1109/TPAMI.2016.2564408. Epub 2016 May 6.
We propose an approach for detecting flying objects such as Unmanned Aerial Vehicles (UAVs) and aircrafts when they occupy a small portion of the field of view, possibly moving against complex backgrounds, and are filmed by a camera that itself moves. We argue that solving such a difficult problem requires combining both appearance and motion cues. To this end we propose a regression-based approach for object-centric motion stabilization of image patches that allows us to achieve effective classification on spatio-temporal image cubes and outperform state-of-the-art techniques. As this problem has not yet been extensively studied, no test datasets are publicly available. We therefore built our own, both for UAVs and aircrafts, and will make them publicly available so they can be used to benchmark future flying object detection and collision avoidance algorithms.
我们提出了一种在小视野中检测飞行物体（如无人机和飞机）的方法，这些物体可能在复杂背景下移动，并且由自身移动的摄像机拍摄。我们认为，解决这样一个难题需要结合外观和运动线索。为此，我们提出了一种基于回归的方法，用于以对象为中心的图像块的运动稳定化，这使我们能够在时空图像立方体上进行有效分类，并超越最先进的技术。由于这个问题尚未得到广泛研究，因此没有公开的测试数据集。因此，我们为无人机和飞机都建立了自己的数据集，并将其公开，以便可以用来对未来的飞行物体检测和避撞算法进行基准测试。
