ERA DATASET

Along with the now widespread availability 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 work, we introduce a novel task 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 different environments and at different scales. Moreover, in order to provide a benchmark for this task, we extensively evaluate existing deep networks. We expect that the ERA dataset will facilitate further progress in automatic aerial video comprehension.

The goal of this work is to collect a large, diverse dataset for training models for event understanding in UAV videos. As we gather aerial videos from Youtube, the largest video sharing platform in the world, we are capable of including a large breadth of diversity that would be more challenging than making use of self-collected data. In total, we have gathered and annotated 2,864 videos for 25 classes. Each video sequence is at 24 fps (frames per second), in 5 seconds, and with a spatial size of 640×640 pixels.

Single-frame Classification Models

Video Classification Models

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