论文标题
时代：航空视频中事件识别的数据集和深度学习基准
ERA: A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos
论文作者
论文摘要
随着无人驾驶汽车（UAV）的越来越多的使用，还制作了大量航空视频。人类筛选这样的大数据并了解其内容是不现实的。因此，关于自动理解无人机视频的方法论研究至关重要。在本文中，我们在遥感社区中的无约束空中视频中介绍了一个新的事件识别问题，并提出了一个名为ERA的大规模，人类通知的数据集（空中视频中的事件识别），每个视频由2,864个视频组成，每个视频由25个不同类别的唱片公司组成，这些标签与与事件相关的5秒钟相对应。 ERA数据集旨在具有重要的类内变化和类间的相似性，并在各种情况下捕获了动态事件，并且在巨大的尺度上。此外，为了为这项任务提供基准，我们广泛验证了现有的深层网络。我们预计ERA数据集将有助于自动航空视频理解的进一步进展。该网站是https://lcmou.github.io/era_dataset/
Along with 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 their contents. Hence methodological research on the automatic understanding of UAV videos is of paramount importance. In this paper, we introduce a novel problem of event recognition in unconstrained aerial videos in the remote sensing community and present a large-scale, human-annotated dataset, named ERA (Event Recognition in Aerial videos), consisting of 2,864 videos each with a label from 25 different classes corresponding to an event unfolding 5 seconds. The ERA dataset is designed to have a significant intra-class variation and inter-class 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 dataset will facilitate further progress in automatic aerial video comprehension. The website is https://lcmou.github.io/ERA_Dataset/
