Real-Time Object Detection and Tracking for Drone Using the Yolo Algorithm

Abstract

The development of autonomous systems for drone journalism represents a significant leap forward in modern media coverage.It aims to revolutionize journalistic practices by integrating real-time object detection and tracking capabilities using the YOLOv8 algorithm.The primary objective is to create a reliable and user-friendly system that enables drones to autonomously capture footage of journalists in action, eliminating the need for manual piloting and enhancing efficiency and safety in journalistic drone operations.Utilizing the YOLOv8 algorithm, the system empowers drones to autonomously identify and track journalists, ensuring automatic footage capture from diverse perspectives and angles.Key features of the proposed system include automated flight controls, user-friendly interfaces for seamless drone operation and monitoring, and robust safety measures to minimize the risk of accidents and errors.By streamlining the technical aspects of drone piloting, journalists can focus their efforts on content creation, resulting in higher-quality and more relevant journalistic coverage.In conclusion, the integration of real-time object detection and tracking with drones using the YOLO algorithm represents a significant leap forward in autonomous drone journalism, empowering journalists to capture compelling footage efficiently and safely while enriching the storytelling experience for audiences worldwide.