  Fight detection in videos is an emerging deep learning application with
today's prevalence of surveillance systems and streaming media. Previous work
has largely relied on action recognition techniques to tackle this problem. In
this paper, we propose a simple but effective method that solves the task from
a new perspective: we design the fight detection model as a composition of an
action-aware feature extractor and an anomaly score generator. Also,
considering that collecting frame-level labels for videos is too laborious, we
design a weakly supervised two-stage training scheme, where we utilize
multiple-instance-learning loss calculated on video-level labels to train the
score generator, and adopt the self-training technique to further improve its
performance. Extensive experiments on a publicly available large-scale dataset,
UBI-Fights, demonstrate the effectiveness of our method, and the performance on
the dataset exceeds several previous state-of-the-art approaches. Furthermore,
we collect a new dataset, VFD-2000, that specializes in video fight detection,
with a larger scale and more scenarios than existing datasets. The
implementation of our method and the proposed dataset will be publicly
available at https://github.com/Hepta-Col/VideoFightDetection.
