대한임베디드공학회논문지 (IEMEK Journal of Embedded Systems and Applications)

대한임베디드공학회 (Institute of Embedded Engineering of Korea)
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심층학습 기반의 자동 객체 추적 및 핸디 모션 제어 드론 시스템 구현 및 검증

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

  • 투고 : 2021.08.20
  • 심사 : 2021.10.01
  • 발행 : 2021.10.31
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초록

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.

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과제정보

본 논문은 대한민국 공군 국고연구 과제의 지원을 받아 수행된 연구결과임 (20-A-5).

참고문헌

  1. K. Amer, M. Samy, M. Shaker, M. ElHelw, "Deep Convolutional Neural Network-based Autonomous Drone navigation", arXiv:1905.01657, 2019.
  2. Y.J. Yoo, Y.J. Kim, "A Study of Gesture Vocabularies for User Centered Drone Control Interface", Proceedings of HCI Korea conference, pp. 446-469, 2017.
  3. W. J. Jang, H. Kim, "Development of an Intuitive Drone Controller Based on Hand Motions", Proceedings of HCI Korea conference, pp. 128-131, 2018.
  4. Y.J. Park, H.J. Kim, H. Lee, Y.J. Baek, D.G. Kim, O.K. Ha, "A Design of Method for Drone Control using Finger Motion Recognition", Proceedings of Korea society of computer information, pp. 127-128, 2020.
  5. Redmon Joseph, Ali Farhadi, "YOLOv3: An Incremental Improvement," arXiv preprint arXiv:1804.02767, 2018..
  6. A.G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, H. Adam, "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications", arXiv:1704.04861, 2017.
  7. S.Y. Lim, T.Y. Kim, "Recognizing and Tracking Moving Objects with Aerial Drones Using Video Calculation", Proceedings of Korean association of computer education, pp. 109-113, 2017.
  8. D.J. Park, J.S. Kim, "Implementation of Object Tracking System for Drone Using TLD Image Tracking Algorithm", Proceedings of the institute of electronics and information engineers, pp. 1460-1461, 2018.
  9. J.M. Cho, "Application Trends of Deep Learning Artificial Intelligence in Autonomous Things", ETRI Electronics and telecomm. trends, Vol. 35, No. 6, pp. 1-11, 2020.
  10. N. Wojke, A. Bewley, D. Paulus, "Simple online and realtime tracking with a deep association metric", Int. Conf. on Image Processing (ICIP), pp. 3645-3649, 2017.
  11. T.Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, C.L. Zitnick, "Microsoft coco: Common objects in context", In European conference on computer vision, Springer, Cham, pp. 740-755, 2014.