  Advancements in deep neural networks have contributed to near perfect results
for many computer vision problems such as object recognition, face recognition
and pose estimation. However, human action recognition is still far from
human-level performance. Owing to the articulated nature of the human body, it
is challenging to detect an action from multiple viewpoints, particularly from
an aerial viewpoint. This is further compounded by a scarcity of datasets that
cover multiple viewpoints of actions. To fill this gap and enable research in
wider application areas, we present a multi-viewpoint outdoor action
recognition dataset collected from YouTube and our own drone. The dataset
consists of 20 dynamic human action classes, 2324 video clips and 503086
frames. All videos are cropped and resized to 720x720 without distorting the
original aspect ratio of the human subjects in videos. This dataset should be
useful to many research areas including action recognition, surveillance and
situational awareness. We evaluated the dataset with a two-stream CNN
architecture coupled with a recently proposed temporal pooling scheme called
kernelized rank pooling that produces nonlinear feature subspace
representations. The overall baseline action recognition accuracy is 74.0%.
