  Law enforcement and city safety are significantly impacted by detecting
violent incidents in surveillance systems. Although modern (smart) cameras are
widely available and affordable, such technological solutions are impotent in
most instances. Furthermore, personnel monitoring CCTV recordings frequently
show a belated reaction, resulting in the potential cause of catastrophe to
people and property. Thus automated detection of violence for swift actions is
very crucial. The proposed solution uses a novel end-to-end deep learning-based
video vision transformer (ViViT) that can proficiently discern fights, hostile
movements, and violent events in video sequences. The study presents utilizing
a data augmentation strategy to overcome the downside of weaker inductive
biasness while training vision transformers on a smaller training datasets. The
evaluated results can be subsequently sent to local concerned authority, and
the captured video can be analyzed. In comparison to state-of-theart (SOTA)
approaches the proposed method achieved auspicious performance on some of the
challenging benchmark datasets.
