Sustainable Abnormal Events Detection and Tracking in Surveillance System

Abstract

With the proliferation of surveillance cameras, managing and analyzing vast amounts of video data have become challenging. This paper introduces a sustainable automated approach to detect abnormal events in surveillance footage. Leveraging Convolutional Neural Networks (CNNs) and deep learning techniques, our system identifies unusual activities by analyzing video frames. By automating this process, we reduce the burden of manual monitoring and enable timely responses to security threats. This sustainable solution has broad applications in public safety, security, and crime prevention.