Year: 2024
Type: article
Abstract: Recent progress in hand gesture recognition has introduced several natural and intuitive approaches to drone control. However, effectively maneuvering drones in complex environments remains challenging. Drone movements are governed by four independent factors: roll, yaw, pitch, and throttle. Each factor includes three distinct behaviors—increase, decrease, and neutral—necessitating hand gesture vocabularies capable of expressing at least 81 combinations for comprehensive drone control in diverse scenarios. In this paper, we introduce a new set of hand gestures for precise drone control, leveraging an RGB camera sensor. These gestures are categorized into motion-based and posture-based types for efficient management. Then, we develop a lightweight hand gesture recognition algorithm capable of real-time operation on even edge devices, ensuring accurate and timely recognition. Subsequently, we integrate hand gesture recognition into a drone simulator to execute 81 commands for drone flight. Overall, the proposed hand gestures and recognition system offer natural control for complex drone maneuvers.
Source: Applied Sciences
DOI: 10.3390/app142210230
Authors: Guhnoo Yun, Hwykuen Kwak, Dong Hwan Kim
Institution: Korea Institute of Science and Technology
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
OpenAlex ID: W4404145114