Enhanced Real-Time Abnormal Event Detection in Video Surveillance for Safety and Security

Surveillance technology is one of the important and critical tools that is used for maintaining security and safety across various situations such as public places, transportation hubs, industrial facilities, and especially smart city infrastructure. However, the sheer volume of visual data generated poses a significant challenge in monitoring and analyzing this information effectively. In this rapidly evolving landscape of surveillance technology, the need for effective abnormal event detection. This paper presents a comprehensive exploration and implementation of abnormal event detection techniques in real-time video monitoring scenarios. Leveraging recent advancements in deep learning and machine learning methodologies, a novel architecture comprising spatial and temporal encoders-decoders, alongside a binary classifier, is proposed. The architecture aims to discern abnormal events from normal activities in surveillance videos by encoding spatial and temporal features and classifying frames as normal or abnormal and cross-entropy loss. The study conducts a mathematical analysis of the proposed framework and discusses its implementation using real-time video data. Furthermore, the paper critically examines related works, emphasizing recent advancements in deep learning-based approaches, unsupervised learning, attention mechanisms, and multimodal fusion techniques in abnormal event detection. The conclusions drawn from this research highlight the potential and challenges in deploying such systems for enhanced security and safety across diverse domains.