  Crime rate is increasing proportionally with the increasing rate of the
population. The most prominent approach was to introduce Closed-Circuit
Television (CCTV) camera-based surveillance to tackle the issue. Video
surveillance cameras have added a new dimension to detect crime. Several
research works on autonomous security camera surveillance are currently
ongoing, where the fundamental goal is to discover violent activity from video
feeds. From the technical viewpoint, this is a challenging problem because
analyzing a set of frames, i.e., videos in temporal dimension to detect
violence might need careful machine learning model training to reduce false
results. This research focuses on this problem by integrating state-of-the-art
Deep Learning methods to ensure a robust pipeline for autonomous surveillance
for detecting violent activities, e.g., kicking, punching, and slapping.
Initially, we designed a dataset of this specific interest, which contains 600
videos (200 for each action). Later, we have utilized existing pre-trained
model architectures to extract features, and later used deep learning network
for classification. Also, We have classified our models' accuracy, and
confusion matrix on different pre-trained architectures like VGG16,
InceptionV3, ResNet50, Xception and MobileNet V2 among which VGG16 and
MobileNet V2 performed better.
