  The significant growth of surveillance camera networks necessitates scalable
AI solutions to efficiently analyze the large amount of video data produced by
these networks. As a typical analysis performed on surveillance footage, video
violence detection has recently received considerable attention. The majority
of research has focused on improving existing methods using supervised methods,
with little, if any, attention to the semi-supervised learning approaches. In
this study, a reinforcement learning model is introduced that can outperform
existing models through a semi-supervised approach. The main novelty of the
proposed method lies in the introduction of a semi-supervised hard attention
mechanism. Using hard attention, the essential regions of videos are identified
and separated from the non-informative parts of the data. A model's accuracy is
improved by removing redundant data and focusing on useful visual information
in a higher resolution. Implementing hard attention mechanisms using
semi-supervised reinforcement learning algorithms eliminates the need for
attention annotations in video violence datasets, thus making them readily
applicable. The proposed model utilizes a pre-trained I3D backbone to
accelerate and stabilize the training process. The proposed model achieved
state-of-the-art accuracy of 90.4% and 98.7% on RWF and Hockey datasets,
respectively.
