Pose2Drone: A Skeleton-Pose-based Framework for Human-Drone Interaction

• Published in: 2021 29th European Signal Processing Conference (EUSIPCO) pp. 776 - 780
• Main Authors: Marinov, Zdravko, Vasileva, Stanka, Wang, Qing, Seibold, Constantin, Zhang, Jiaming, Stiefelhagen, Rainer
• Format: Conference Proceeding
• Language: English
• Published: EURASIP 23.08.2021
• Subjects: Face and Gesture recognition; Gesture recognition; Human-Drone Interaction; Monocular Distance Estimation; Pose estimation; Sensor phenomena and characterization; Signal processing; Statistical analysis; Surveillance; UAV
• ISSN: 2076-1465
• DOI: 10.23919/EUSIPCO54536.2021.9616116

Drones have become a common tool, which is utilized in many tasks such as aerial photography, surveillance, and delivery. However, operating a drone requires more and more interaction with the user. A natural and safe method for Human-Drone Interaction (HDI) is using gestures. In this paper, we introduce an HDI framework building upon skeleton-based pose estimation. Our framework provides the functionality to control the movement of the drone with simple arm gestures and to follow the user while keeping a safe distance. We also propose a monocular distance estimation method, which is entirely based on image features and does not require any additional depth sensors. To perform comprehensive experiments and quantitative analysis, we create a customized testing dataset. The experiments indicate that our HDI framework can achieve an average of 93.5% accuracy in the recognition of 11 common gestures. The code will be made publicly available to foster future research.