A Review of Road Surveillance using Artificial Intelligence

Road surveillance has become highly important in the modern day due to the rise in crime and accident rates. Traditional manual monitoring often limits scalability, slows decision-making, and increases operational costs. There are many studies being carried out, attempting to solve these issues. Although it is difficult to keep up with the increasing number of vehicles on the roads, technology provides ways to improve surveillance systems through the use of machine learning to detect anomalies such as road accidents or fights. This survey paper examines advancements in anomaly detection using machine learning. Additionally, it highlights the limitations in current anomaly detection systems and drawbacks of machine learning models that are trained on real world footage and identifies areas for future improvement, paving the way for more robust ways to detect anomalies such as accidents and fights.