AUTOMATED INTEGRATION OF DETECTION ALGORITHMS, TARGET RECOGNITION AND NOTIFICATION TO SECURITY CAMERA SUPERVISORS

Abstract

This paper presents an automated system designed to detect and recognize targets using security cameras.The system alerts supervisors upon detecting suspicious behavior. The system applies deep learning algorithms - particularly convolutional neural networks (CNNs) - by implementing the YOLOv11n architecture for real-time object detection and analysis, enabling it to process and analyze video footage efficiently. The results indicate that the system accurately identifies objects and behaviors, thereby enhancing the reliability of surveillance efforts. Notably, the system not only reduces the workload of supervisors but also provides an intelligent solution for improving security, ultimately increasing the effectiveness of management in vulnerable areas. These findings underscore the promising potential of artificial intelligence (AI) technology in the security sector.