ISSN 0914-4935
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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pp. 2245-2262
S&M1928 Research Paper of Special Issue

Realization of Person Following and Gesture Recognition with a Quadrotor System

Neng-Sheng Pai, Yue-Han Zhou, Pi-Yun Chen, Wei-Lun Chen , and Shih-An Chen

(Received November 29, 2018; Accepted May 8, 2019)

Keywords: quadrotor, gesture recognition, tracking-learning-detection, fixed point cruising, fuzzy-PID controller, nonlinear support vector machine, extended Kalman filter

In this paper, the design of a quadrotor vehicle having a person following and observed system, which uses human gesture recognition, is described. The system has three operating functions, namely; object tracking, human gesture recognition, and fixed point cruising. The tracking-learning-detection algorithm was used to allow the autonomous tracking of the object by image. An extended Kalman filter provides an estimate of the current position of the quadrotor vehicle and a fuzzy-proportional integral derivative (PID) controller provides position error compensation. The principle of the human gesture recognition system is as follows. A background model is first built from images using a Gaussian mixture model to detect the foreground image. A nonlinear support vector machine is then employed to recognize changes of gesture and establish interactivity between the vehicle and the user. The coordinates of the vehicle are marked using a GPS for fixed point cruising. The coordinates and parameters of the points are set so that the quadrotor vehicle can follow them during cruising. Lastly, all of the functions are incorporated into the person following and gesture recognition system in the quadrotor. The experimental results show the feasibility of the above-mentioned methods and can help us easily recognize the various gestures in this study.

Corresponding author: Pi-Yun Chen




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