  In recent years, surveillance cameras are widely deployed in public places,
and the general crime rate has been reduced significantly due to these
ubiquitous devices. Usually, these cameras provide cues and evidence after
crimes are conducted, while they are rarely used to prevent or stop criminal
activities in time. It is both time and labor consuming to manually monitor a
large amount of video data from surveillance cameras. Therefore, automatically
recognizing violent behaviors from video signals becomes essential. This paper
summarizes several existing video datasets for violence detection and proposes
the RWF-2000 database with 2,000 videos captured by surveillance cameras in
real-world scenes. Also, we present a new method that utilizes both the merits
of 3D-CNNs and optical flow, namely Flow Gated Network. The proposed approach
obtains an accuracy of 87.25% on the test set of our proposed database. The
database and source codes are currently open to access.
