10.21227/KYZE-DH10
Lichao Mou
Lichao
Mou
German Aerospace Center Technical University of Munich
Yuansheng Hua
Yuansheng
Hua
German Aerospace Center Technical University of Munich
Pu Jin
Pu
Jin
Technical University of Munich
Xiao Xiang Zhu
Xiao Xiang
Zhu
German Aerospace Center Technical University of Munich
ERA Dataset
IEEE DataPort
2020
Artificial Intelligence
Computer Vision
Image Processing
Machine Learning
Transportation
Remote Sensing
Geoscience and Remote Sensing
Social Sciences
Aerial video dataset
unmanned aerial vehicle (UAV)
deep neural networks
event recognition
activity recognition
2020-06-26
Open Access Dataset
Creative Commons Attribution
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 \url{https://lcmou.github.io/ERA_Dataset/}.