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http://dx.doi.org/10.14372/IEMEK.2021.16.5.163

Implementation and Verification of Deep Learning-based Automatic Object Tracking and Handy Motion Control Drone System  

Kim, Youngsoo (Jeonju University)
Lee, Junbeom (Korea Air Force Academy)
Lee, Chanyoung (Korea Air Force Academy)
Jeon, Hyeri (Korea Air Force Academy)
Kim, Seungpil (Korea Air Force Academy)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.16, no.5, 2021 , pp. 163-169 More about this Journal
Abstract
In this paper, we implemented a deep learning-based automatic object tracking and handy motion control drone system and analyzed the performance of the proposed system. The drone system automatically detects and tracks targets by analyzing images obtained from the drone's camera using deep learning algorithms, consisting of the YOLO, the MobileNet, and the deepSORT. Such deep learning-based detection and tracking algorithms have both higher target detection accuracy and processing speed than the conventional color-based algorithm, the CAMShift. In addition, in order to facilitate the drone control by hand from the ground control station, we classified handy motions and generated flight control commands through motion recognition using the YOLO algorithm. It was confirmed that such a deep learning-based target tracking and drone handy motion control system stably track the target and can easily control the drone.
Keywords
Deep learning; Handy motion control; Object detection and tracking; Intelligent drone system;
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