Anomaly Detection in Video Surveillance

As surveillance cameras have become more widely deployed in recent decades, the requirement for effective real-time monitoring to protect public safety has become critical. The bulk of existing cameras, however, provide passive logging services, which causes delays in the detection of abnormal behaviors such as robberies, carjacking, mugging, and terrorism. Human specialists are frequently faced with the issue of having to wait for hours to recognize these occurrences. To address this constraint, our proposal proposes a solution that makes use of artificial intelligence (AI) and machine learning (ML) technologies. The major goal is to automate the detection of anomalous actions in video footage and to notify relevant authorities as soon as they are identified. This proactive strategy not only benefits in the avoidance of criminal activity, but it also allows for the quick apprehending of responsible individuals. Our research aims to modernize video surveillance by minimizing delays in anomaly detection using advanced technology. We want to improve the efficiency and reactivity of video surveillance systems by adding AI and ML, so contributing considerably to the overall safety and security of public spaces.