  This paper summarizes the TinyAction challenge which was organized in
ActivityNet workshop at CVPR 2021. This challenge focuses on recognizing
real-world low-resolution activities present in videos. Action recognition task
is currently focused around classifying the actions from high-quality videos
where the actors and the action is clearly visible. While various approaches
have been shown effective for recognition task in recent works, they often do
not deal with videos of lower resolution where the action is happening in a
tiny region. However, many real world security videos often have the actual
action captured in a small resolution, making action recognition in a tiny
region a challenging task. In this work, we propose a benchmark dataset,
TinyVIRAT-v2, which is comprised of naturally occuring low-resolution actions.
This is an extension of the TinyVIRAT dataset and consists of actions with
multiple labels. The videos are extracted from security videos which makes them
realistic and more challenging. We use current state-of-the-art action
recognition methods on the dataset as a benchmark, and propose the TinyAction
Challenge.
