  The task of action recognition in dark videos is useful in various scenarios,
e.g., night surveillance and self-driving at night. Though progress has been
made in the action recognition task for videos in normal illumination, few have
studied action recognition in the dark. This is partly due to the lack of
sufficient datasets for such a task. In this paper, we explored the task of
action recognition in dark videos. We bridge the gap of the lack of data for
this task by collecting a new dataset: the Action Recognition in the Dark
(ARID) dataset. It consists of over 3,780 video clips with 11 action
categories. To the best of our knowledge, it is the first dataset focused on
human actions in dark videos. To gain further understandings of our ARID
dataset, we analyze the ARID dataset in detail and exhibited its necessity over
synthetic dark videos. Additionally, we benchmarked the performance of several
current action recognition models on our dataset and explored potential methods
for increasing their performances. Our results show that current action
recognition models and frame enhancement methods may not be effective solutions
for the task of action recognition in dark videos.
