A Survey of Integrating Deep Learning-Based Missing Person Detection Model into CCTV Systems for Enhanced Identification

Abstract

This study looks at the possibility of increasing identification abilities in closed-circuit video (CCTV) systems using a deep learning-based missing person detection model. Missing individuals are becoming increasingly common, necessitating the development of novel search tactics. This study uses deep learning to augment traditional CCTV systems by using an improved model that can reliably identify and track persons who go missing. Taking a detailed look at current techniques to missing person identification, the literature review shows the shortcomings of existing systems and the potential for improving them through the use of deep learning. The paper examines past methodologies, including facial recognition accuracy, tracking robustness, and system scalability.The integration of computer vision with missing person identification, object tracking technologies, and facial recognition algorithms are among the main topics. The research also investigates privacy, moral, and legal implications of deploying such technology in public.