As a result of the increasing use of unmanned aerial vehicles (UAVs), large volumes of aerial videos have been produced. It is unrealistic for humans to screen such big data and understand the contents. Hence, methodological research on the automatic understanding of UAV videos is of paramount importance (Figure 1). In this article, we introduce a novel problem of event recognition in unconstrained aerial videos in the remote sensing community and present the large-scale, human-annotated Event Recognition in Aerial Videos (ERA) data set, consisting of 2,864 videos, each with a label from 25 different classes corresponding to an event unfolding for five seconds. All these videos are collected from YouTube. The ERA data set is designed to have significant intraclass variation and interclass similarity and captures dynamic events in various circumstances and at dramatically various scales. Moreover, to offer a benchmark for this task, we extensively validate existing deep networks. We expect that the ERA data set will facilitate further progress in automatic aerial video comprehension. The data set and trained models can be downloaded from https://lcmou.github.io/ERA_Dataset/.

中文翻译：

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ERA：航拍视频事件识别的数据集和深度学习基准 [软件和数据集]


由于无人机（UAV）的使用越来越多，产生了大量的航拍视频。人类要筛选如此大的数据并理解其中的内容是不现实的。因此，无人机视频自动理解的方法学研究至关重要（图1）。在本文中，我们介绍了遥感领域无约束航拍视频中事件识别的新问题，并提出了大规模、人工注释的航拍视频事件识别 (ERA) 数据集，该数据集由 2,864 个视频组成，每个视频都有一个来自 25 个不同类别的标签，对应于展开五秒钟的事件。所有这些视频均从 YouTube 收集。 ERA 数据集旨在具有显着的类内变异和类间相似性，并捕获各种情况下和各种规模的动态事件。此外，为了为这项任务提供基准，我们广泛验证了现有的深度网络。我们预计 ERA 数据集将促进自动航空视频理解的进一步进展。数据集和训练模型可以从https://lcmou.github.io/ERA_Dataset/下载。