Smart CCTV Face Detection System Based on the Internet of Things and Artificial Intelligence

Abstract

The high rate of motor vehicle theft in Bandung City indicates that conventional security surveillance systems still have limitations in providing effective and responsive monitoring. Therefore, this study aims to design and implement a smart Closed Circuit Television (CCTV) face detection system based on the Internet of Things (IoT) and Artificial Intelligence (AI) capable of performing real-time monitoring and face detection. The research employed the User Centered Design (UCD) method, which consists of user needs identification, system design, implementation, and evaluation. The system was developed using a Raspberry Pi as the IoT edge device, a camera for image acquisition, the InsightFace algorithm for face recognition, and Firebase and the Telegram Bot API for data storage and notification delivery. The novelty of this study lies in the development of a smart CCTV system that integrates the User Centered Design (UCD) method, Internet of Things (IoT) technology, the InsightFace algorithm based on Artificial Intelligence (AI), an automatic pan-tilt module, and Telegram notifications into a single real-time security monitoring platform. The results show that the system achieved a face recognition accuracy of 92%, delivered Telegram notifications with an average response time of 2.8 seconds, and performed object tracking using the pan-tilt module with a response time of less than 0.5 seconds. In addition, the system successfully detected unknown faces, synchronized data to Firebase instantly, managed video storage automatically through a rolling buffer mechanism, and provided a responsive web-based dashboard. Based on the overall testing results, the developed system effectively improved security monitoring, facilitated remote surveillance, and met user requirements in accordance with the User Centered Design (UCD) approach.