Artificial intelligence-based human gesture tracking control techniques of Tello EDU Quadrotor Drone

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.