TY  - JOUR
Y1  - 2023///
AV  - none
A1  - Iskandar, M.
A1  - Bingi, K.
A1  - Rajanarayan Prusty, B.
A1  - Omar, M.
A1  - Ibrahim, R.
SP  - 123
PB  - Institution of Engineering and Technology
KW  - Aircraft detection; Artificial intelligence; Gesture recognition
KW  -  Control techniques; Future improvements; Gesture tracking; Human gestures; Programming framework; Programming hardware; Quad rotors; Signal acquisitions; Tracking controls; Tracking techniques
KW  -  Drones
VL  - 2023
TI  - Artificial Intelligence-Based Human Gesture Tracking Control Techniques of Tello EDU Quadrotor Drone
N1  - 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
JF  - IET Conference Proceedings
IS  - 11
SN  - 27324494
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181541922&doi=10.1049%2ficp.2023.1770&partnerID=40&md5=3b2015eb659c045359d08d519786487e
EP  - 128
ID  - scholars19010
N2  - 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.
ER  -