  Despite significant progress in the development of human action detection
datasets and algorithms, no current dataset is representative of real-world
aerial view scenarios. We present Okutama-Action, a new video dataset for
aerial view concurrent human action detection. It consists of 43 minute-long
fully-annotated sequences with 12 action classes. Okutama-Action features many
challenges missing in current datasets, including dynamic transition of
actions, significant changes in scale and aspect ratio, abrupt camera movement,
as well as multi-labeled actors. As a result, our dataset is more challenging
than existing ones, and will help push the field forward to enable real-world
applications.
