Transactions of Nanjing University of Aeronautics & Astronautics
Machine Learning-Based Gaze-Tracking and Its Application in Quadrotor Control on Mobile Device
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Abstract:
A machine learning-based monocular gaze-tracking technology for mobile devices is proposed. This non-invasive， convenient， and low-cost gaze-tracking method can capture the gaze points of users on the screen of mobile devices in real time. Combined with the quadrotor’s 3D motion control， the user’s gaze information is converted into the quadrotor’s control signal， solving the limitations of previous control methods， which allows the user to manipulate the quadrotor through visual interaction. A complex quadrotor track is set up to test the feasibility of this method. Subjects are asked to intervene their gaze into the control flow to complete the flight tasks. Flight performance is evaluated by comparing with the joystick-based control method. Experimental results show that the proposed method can improve the smoothness and rationality of the quadrotor motion trajectory， and can introduce diversity， convenience， and intuitiveness to the quadrotor control.
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Project Supported:
This work was supported by the National Natural Science Foundation of China （No.51975293）， the Aeronautical Science Foundation of China （No.2019ZD052010）， and the Zhangjiagang Pre-research Fund of China （No.ZKCXY2101）.
Clc Number:
TP249
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