Autonomous Security and Surveillance System using Deep Learning and Face Tracking

Security and surveillance are a major concern in today's world, and thus the need for the creation of intelligent and autonomous monitoring systems. This paper discusses a new security and surveillance system that utilizes real-time face tracking and deep learning to improve monitoring efficiency and reliability. The system consists of a high-resolution webcam on a robot arm driven by MG995 servo motors and an Arduino microcontroller. Face detection is achieved with a Convolutional Neural Network (CNN)-based model to ensure high accuracy detection even at low illumination. A real-time object tracking algorithm is used to keep the focus of the camera on detected faces, enabling the camera to change its position dynamically and ensure constant monitoring. Addition of a PID-based servo control algorithm provides maximum movement accuracy with reduced lag and improved response time. The system is made such that it requires minimal human intervention, and hence it is a viable solution for automated security solutions. Performance tests show that the proposed system provides an average accuracy of 92.5% in face tracking and response latency of around 100ms. Power efficiency and Hardware-software optimization also contribute to the success of the system in real-world applications. Multi-person tracking, integration with IoT-based security networks, and AI-based decision-making capabilities are future areas of exploration. This research is a showcase of how robotics and artificial intelligence can be combined to create intelligent security solutions with reliability and automation.