YOLOv8-Powered Intelligent Surveillance: An Integrated Real-Time Framework for Crowd Management, Crime Prevention, and Workplace Safety Monitoring using AI and ML

Abstract

The evolving complexity of urban environments and the effectiveness of traditional CCTV surveillance is making it increasingly difficult to ensure public safety, effective crowd management, crime prevention, and workplace security solutions. However, the traditional approach to surveillance is largely manual, leading to late reactions, missed events, and scalability issues. The intelligent surveillance system based on the YOLOv8 object detection algorithm is designed to enhance workplace safety, prevent crimes, manage crowds, and achieve face recognition in real time within a single AI platform. This paper introduces the concept of an intelligent surveillance framework that combines real-time face recognition, workplace safety monitoring, crowd management, and crime prevention through the use of YOLOv8 object detection algorithms within a single AI-driven solution. The framework employs the YOLOv8 algorithm for object detection, identifying people, weapons, suspicious activities, abandoned objects, and workplace safety violations, and issuing automatic alerts to facilitate swift decision-making. The proposed framework provides an integrated platform of multiple surveillance functionalities as opposed to the existing surveillance systems, where each surveillance task is monitored separately, which provides overall situational awareness using the existing CCTV. The model was trained with surveillance images annotated and tested with Precision, Recall, F1-score, Accuracy, and mAP@0.5. An overall detection accuracy of 92.4%, a precision of 92.4%, a recall of 89.7%, an F1-score of 91.0%, and an mAP@0.5 of 93.2% have been achieved during experimental evaluation. Moreover, the framework's average inference latency is 18ms per frame, which guarantees that it can be used in real-time surveillance applications without compromising the accuracy of its detection results when deployed in various surveillance environments. The proposed system is versatile and feasible for implementation in smart city systems, transportation hubs, industrial production sites, and various organizational environments, and can enable smart surveillance by merging multiple security functions into a single framework based on the YOLOv8 object detection model.