Development of an Intelligent Violence Detection System for Bullying Monitoring Using Deep Learning Models

Abstract

Bullying in schools is a severe problem that has both short- and long-term harmful implications for victims. However, surveillance of bullying, particularly acts of violence such as kicking, pushing, and striking at school, remains inadequate. Using Artificial Intelligence is one of the recommended solutions for detecting incidents of aggression in video footage. Deep learning methods, specifically Convolutional Neural Network and Long Short-Term Memory, are used in this study to construct Artificial Intelligence for detecting acts of aggression. The model can achieve an average accuracy of up to 92%. Based on these accuracy results, the model can be implemented into online intelligent applications. It is envisaged that sophisticated software that detect such acts of aggression will be effective in monitoring bullying incidents and reducing the number of bullying cases in schools.