Anomalous Event Detection Based on YOLOv8 for Enhanced Security and Surveillance

This paper focuses on developing a multiple-camera, multiple-location monitoring enhanced security and surveillance system by integrating AI, particularly the machine learning algorithm YOLOv8 incorporated with the Deep Convolutional Neural Network (DCNN) model. The entire system consists of computer-integrated web cameras acting as CCTV cameras, IP cameras, and an autonomous rover for seamless surveillance. The whole system including the rover identifies objects like guns and knives, detects activities such as smoking, and responds effectively. With multiple cameras for real-time surveillance, the project achieves its goals, offering a unique fusion of machine learning and robotics for advanced security management. This research contributes to proactive surveillance systems, marking a notable advancement in security technology for safeguarding commercial infrastructures against diverse threats.