ERA: A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos
This work addresses the challenge of screening large volumes of UAV videos for the remote sensing community, though it is incremental as it focuses on dataset creation and benchmarking rather than novel methodology.
The authors tackled the problem of automatic event recognition in unconstrained aerial videos by introducing the ERA dataset, which includes 2,864 videos across 25 event classes, and provided a benchmark using existing deep networks to facilitate progress in this area.
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/