  Real-time video surveillance, through CCTV camera systems has become
essential for ensuring public safety which is a priority today. Although CCTV
cameras help a lot in increasing security, these systems require constant human
interaction and monitoring. To eradicate this issue, intelligent surveillance
systems can be built using deep learning video classification techniques that
can help us automate surveillance systems to detect violence as it happens. In
this research, we explore deep learning video classification techniques to
detect violence as they are happening. Traditional image classification
techniques fall short when it comes to classifying videos as they attempt to
classify each frame separately for which the predictions start to flicker.
Therefore, many researchers are coming up with video classification techniques
that consider spatiotemporal features while classifying. However, deploying
these deep learning models with methods such as skeleton points obtained
through pose estimation and optical flow obtained through depth sensors, are
not always practical in an IoT environment. Although these techniques ensure a
higher accuracy score, they are computationally heavier. Keeping these
constraints in mind, we experimented with various video classification and
action recognition techniques such as ConvLSTM, LRCN (with both custom CNN
layers and VGG-16 as feature extractor) CNNTransformer and C3D. We achieved a
test accuracy of 80% on ConvLSTM, 83.33% on CNN-BiLSTM, 70% on VGG16-BiLstm
,76.76% on CNN-Transformer and 80% on C3D.
