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

arXiv: https://arxiv.org/abs/2105.13204 · HTML: https://mlsanity.com/paper/2105.13204/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 27 May 2021.

## Builds on (most cited first, 2 of 2)

- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Gesture-based Piloting of an Aerial Robot using Monocular Vision](https://mlsanity.com/paper/1803.00757/) (2018)

## Built on by (most cited first, 1 of 1)

- [Flying Guide Dog: Walkable Path Discovery for the Visually Impaired Utilizing Drones and Transformer-based Semantic Segmentation](https://mlsanity.com/paper/2108.07007/) (2021)

## Abstract (arXiv)

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 is available at: https://github.com/Zrrr1997/Pose2Drone