Title: An embedded system-based hand-gesture recognition for human-drone interaction

Authors: Khadidja Belattar; Abdelhak Mehadjbia; Abdelkarim Bala; Ahmed Kechida

Addresses: Department of Computer Science, University of Algiers, Algiers, Algeria ' Department of Computer Science, University of Algiers, Algiers, Algeria ' Department of Computer Science, University of Algiers, Algiers, Algeria ' Research Center in Industrial Technologies, Cheraga, Algiers, Algeria

Abstract: Using hand gestures is a natural mode of communication in human-robot interaction. This article presents an embedded command system for human-drone interaction. The main significance of the system is the improvement of human-drone interaction. Three contributions are proposed. The first one is the construction of a static hand-gesture dataset of three basic classes namely: 'start recording', 'take a photo' and 'stop recording'. The second contribution consists of the investigation of different transfer learning-based deep object detectors in both laptop and embedded computer platforms. The third contribution is an embedded hand-gesture recognition system, which allows users interaction, in real-time, with the quad-rotor drone using hand-gestures. The experimental results demonstrate the superiority of the YOLOv5-small model in the embedded recognition system. It yielded 98.32% mean average precision, 93.91% recall, 0.0025 loss and 0.92 recognition speed. Overall, the obtained results are promising and could be exploited in real-time object tracking applications.

Keywords: embedded system; hand gesture recognition; real time recognition; human drone interaction; deep object detectors; transfer learning; YOLOv4; YOLOv5; SSD mobileNetv2; quad-rotor drone.

DOI: 10.1504/IJES.2022.125442

International Journal of Embedded Systems, 2022 Vol.15 No.4, pp.333 - 343

Received: 23 Dec 2021
Received in revised form: 17 Apr 2022
Accepted: 03 May 2022
Published online: 09 Sep 2022 *

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