eprintid: 19010
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/90/10
datestamp: 2024-06-04 14:11:27
lastmod: 2024-06-04 14:11:27
status_changed: 2024-06-04 14:04:38
type: article
metadata_visibility: show
creators_name: Iskandar, M.
creators_name: Bingi, K.
creators_name: Rajanarayan Prusty, B.
creators_name: Omar, M.
creators_name: Ibrahim, R.
title: Artificial Intelligence-Based Human Gesture Tracking Control Techniques of Tello EDU Quadrotor Drone
ispublished: pub
keywords: Aircraft detection; Artificial intelligence; Gesture recognition, Control techniques; Future improvements; Gesture tracking; Human gestures; Programming framework; Programming hardware; Quad rotors; Signal acquisitions; Tracking controls; Tracking techniques, Drones
note: cited By 4; Conference of 2023 International Conference on Green Energy, Computing and Intelligent Technology, GEn-CITy 2023 ; Conference Date: 10 July 2023 Through 12 July 2023; Conference Code:195673
abstract: This paper comprehensively reviews significant research on various artificial intelligence-based human gesture tracking techniques for the Tello EDU quadrotor drone. The gestures derived from image acquisition techniques include hand, eye, face, and body. Further, the methods include signal acquisition through leap motion and an electroencephalogram. The framework for developing the algorithm with various gestures is also demonstrated. The review table presents a thorough overview of the studies linked to the various human gesture-based techniques. It encompasses details such as the algorithm type, quantity of poses and landmarks, programming language, hardware and framework employed, and validation particulars. Furthermore, it comprehensively analyzes potential areas for future research and improvements within this field. © The Institution of Engineering & Technology 2023.
date: 2023
publisher: Institution of Engineering and Technology
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181541922&doi=10.1049%2ficp.2023.1770&partnerID=40&md5=3b2015eb659c045359d08d519786487e
id_number: 10.1049/icp.2023.1770
full_text_status: none
publication: IET Conference Proceedings
volume: 2023
number: 11
pagerange: 123-128
refereed: TRUE
issn: 27324494
citation:   Iskandar, M. and Bingi, K. and Rajanarayan Prusty, B. and Omar, M. and Ibrahim, R.  (2023) Artificial Intelligence-Based Human Gesture Tracking Control Techniques of Tello EDU Quadrotor Drone.  IET Conference Proceedings, 2023 (11).  pp. 123-128.  ISSN 27324494