Yun, Guhnoo Kwak, Hwykuen Kim, Dong Hwan 2024-11-13T15:30:10Z 2024-11-13T15:30:10Z 2024-11-12 2024-11 https://pubs.kist.re.kr/handle/201004/151054 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. English MDPI Single-Handed Gesture Recognition with RGB Camera for Drone Motion Control Article 10.3390/app142210230 1 Applied Sciences-basel, v.14, no.22, pp.1 - 15 Applied Sciences-basel 14 22 1 15 Y scie scopus 001366701600001 Chemistry, Multidisciplinary Engineering, Multidisciplinary Materials Science, Multidisciplinary Physics, Applied Chemistry Engineering Materials Science Physics Article hand gesture recognition (HGR) drone motion control human-drone interaction