METHODS FOR REAL-TIME DETECTION OF VIOLENCE, CRIME, AND EMERGENCIES THROUGH VIDEO SURVEILLANCE SYSTEMS IN SMART CITIES

Abstract

The widespread deployment of CCTV and sensor networks in smart cities has become a cornerstone of modern urban public safety infrastructure. However, the continuous monitoring of massive video streams by human operators is practically infeasible, leading to delayed detection or complete oversight of violent incidents, criminal activities, and emergency situations. This challenge necessitates the development of automated systems capable of real-time video analysis, anomaly detection, and rapid alert generation. This paper presents a comprehensive review and methodological framework for real-time detection of violence, crime, and emergencies using artificial intelligence and deep learning techniques in smart city video surveillance systems. The study systematically analyzes existing literature, identifying key approaches including three-dimensional convolutional neural networks (3D CNNs) for spatiotemporal feature extraction, hybrid CNN-LSTM architectures for sequential behavior analysis, and YOLO-based object detectors for identifying weapons and dangerous objects. The proposed methodology integrates a two-stage architecture comprising a lightweight spatial encoder (MobileNetV2) for frame-level feature extraction, coupled with a temporal module (3D CNN or LSTM) for sequence classification. Additionally, the framework incorporates edge/fog computing paradigms to enable real-time processing on resource-constrained devices, geographic information system (GIS) integration for spatial visualization of crime hotspots, and automated alert mechanisms for rapid emergency response.