Human-Computer Interaction for Drone Control through Hand Gesture Recognition with MediaPipe Integration

The growing popularity of drones necessitates more intuitive control methods beyond traditional, two-handed joysticks which lack functionality for intricate maneuvers. This paper proposes a novel hand gesture control system for drones utilizing the MediaPipe framework. Our system leverages MediaPipe’s computer vision to enable natural hand gestures for seamless drone navigation. By integrating machine learning and real-time gesture recognition, users can interact with drones without physical controllers. Experimental trials demonstrate accurate gesture recognition and responsive drone control. This technology offers significant advancements in human-drone interaction, particularly for applications in aerial photography, surveillance, and search and rescue. Hand gesture control with MediaPipe promotes a more accessible and user-friendly drone experience.