Event and Activity Recognition in Aerial Videos Using Deep Neural Networks and a New Dataset

Abstract

Unmanned aerial vehicles (UAVs) are now widespread available. Yet the more UAVs there are in the skies, the more video data they create. It is unrealistic for humans to screen such big data and understand their contents. Hence methodological research on UAV video content understanding is of great importance. In this paper, we introduce a novel task of event recognition in unconstrained aerial videos in the remote sensing community and present a dataset for this task. Organized in a rich semantic taxonomy, the proposed dataset covers a wide range of events involving diverse environments and scales. We report results of plenty of deep networks in two ways: single-frame classification and video classification. The dataset and trained models can be downloaded from https://1cmou.github.io/ERA_Dataset/.