Unveiling Anomaly : Empowering Video Surveillance through Intelligent Anomaly Detection

Abstract

Video surveillance has become a cornerstone of security for public spaces and private property. However, the effectiveness of this approach is hampered by the limitations of manual monitoring. Human analysts face challenges such as fatigue, distraction, and the sheer volume of video data, leading to missed incidents and inefficient use of resources. This research project proposes a revolutionary solution: intelligent anomaly detection through artificial intelligence (AI). This system transcends the constraints of human observation by automatically identifying deviations from established patterns within video footage. The core concept lies in leveraging the power of AI to analyze various aspects of video data. This includes movement analysis, object recognition, and scene dynamics. Through this comprehensive approach, the system can detect anomalous events that might escape human notice – activities such as loitering, intrusions, or suspicious behavior. This project delves into the design and development of this intelligent anomaly detection system. It explores the vast potential of machine learning techniques, specifically focusing on unsupervised learning and deep learning algorithms. These algorithms play a crucial role in modeling normal behavior within video data. The system then utilizes these models to identify deviations that fall outside the established patterns. By flagging these anomalies, the system empowers security personnel to prioritize their attention on critical events. This significantly enhances overall security efficiency by allowing human analysts to focus on investigating the most relevant situations. This research project seeks to contribute significantly to the advancement of video surveillance technology. By harnessing the power of AI and machine learning, this intelligent anomaly detection system offers a promising approach to enhancing security in public spaces and private property.