Ai-Driven Individual Identification in Surveillance Systems: Leveraging Vision Transformers and Graph Neural Networks for Enhanced Drone Surveillance

AI integration with surveillance drones is a revolutionary step towards real-time identification of people, transforming security and monitoring systems. This paper introduces an AI-based framework integrating cutting-edge computer vision, machine learning, and deep learning approaches to identify people from drone captured images. Through the utilization of facial recognition, gait analysis, and contextual information, the system is able to deliver high accuracy even in intricate environments with changing movement and lighting. Major applications are securing public safety, managing disasters, preventing crime, and securing borders. In addition, the suggested framework is developed to process information with efficiency, thus making it scalable and flexible to cope with varying conditions. Particular attention is given to catering to ethical factors, including privacy protection for the individual and data security protocols stringent enough to assuage worries about invasion. This study would like to bring into being an effective, robust, and safe system of secure identification, in turn, ushering in newer applications of AI in surveillance.