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.
Human-Computer Interaction for Drone Control through Hand Gesture Recognition with MediaPipe Integration
2024-09-27
597933 byte
Conference paper
Electronic Resource
English
Human-Robot Interaction Through Egocentric Hand Gesture Recognition
Springer Verlag | 2025
|Computer Vision Implementation in a Hand Gesture-Based Indoor Drone Guidance System
Springer Verlag | 2025
|