Hand Gesture Controlled Drones: An Open Source Library

TL;DR

This paper introduces an open-source computer vision-based library enabling drone control through hand gestures, offering an intuitive, agent-less interface that overcomes range and interference issues of traditional methods.

Contribution

It presents a novel open-source framework for gesture-based drone control using computer vision, including a gesture recognition system and safety features.

Findings

Gesture recognition accuracy exceeds 90% in well-lit conditions within 3 ft.

The framework effectively translates five specific gestures into drone commands.

Open-source library and datasets are provided for further research and development.

Abstract

Drones are conventionally controlled using joysticks, remote controllers, mobile applications, and embedded computers. A few significant issues with these approaches are that drone control is limited by the range of electromagnetic radiation and susceptible to interference noise. In this study we propose the use of hand gestures as a method to control drones. We investigate the use of computer vision methods to develop an intuitive way of agent-less communication between a drone and its operator. Computer vision-based methods rely on the ability of a drone's camera to capture surrounding images and use pattern recognition to translate images to meaningful and/or actionable information. The proposed framework involves a few key parts toward an ultimate action to be taken. They are: image segregation from the video streams of front camera, creating a robust and reliable image recognition based on segregated images, and finally conversion of classified gestures into actionable drone movement, such as takeoff, landing, hovering and so forth. A set of five gestures are studied in this work. Haar feature-based AdaBoost classifier is employed for gesture recognition. We also envisage safety of the operator and drone's action calculating the distance based on computer vision for this task. A series of experiments are conducted to measure gesture recognition accuracies considering the major scene variabilities, illumination, background, and distance. Classification accuracies show that well-lit, clear background, and within 3 ft gestures are recognized correctly over 90%. Limitations of current framework and feasible solutions for better gesture recognition are discussed, too. The software library we developed, and hand gesture data sets are open-sourced at project website.