지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.12
- 수록면
- 1,140 - 1,146 (7page)
- DOI
- 10.5302/J.ICROS.2022.22.0181
이용수
초록· 키워드
오류제보하기
AMR(Autonomous Mobile Robot) is being used to improve working environment through collaboration such as transporting goods between workers. For collaboration such as transporting goods, AMR tracks the workers and carries out goods transport. Object tracking is possible based on a deep learning model trained using big data, built as an object to be tracked. When the worker changes frequently, such as in a work environment, there is a problem in that big data construction and deep learning model learning are required whenever an object to be tracked is changed. There is a need for a method for tracking objects that change frequently while providing small amounts of data. This paper proposes a deep learning-based framework for tracking changeable object. An object to be tracked, such as a worker, is defined as a ToI (Target-of-Interest) object. The proposed framework utilizes a two-stage deep learning model to track a changeable ToI object. In the deep learning model of the first stage, an object of the same type as the ToI object is tracked. In the deep learning model of the second stage, the ToI object is found among the objects being tracked. The position of the ToI object is transformed into the coordinate system of the AMR so that the AMR can track the ToI object. In the experiment, the results of tracking the ToI object by using the proposed method were verified. When tracking ToI objects with a single-stage deep learning model with a small amount of data, the accuracy of tracking the ToI objects decreased according to the amount of data. In the case of the proposed method, the tracking of the ToI object was not affected by the amount of data.
목차
Abstract
Ⅰ. 서론
Ⅱ. 관련 연구
Ⅲ. 변경 가능한 ToI 객체 추적하기 위한 딥러닝 프레임워크 개요
Ⅳ. 실험
Ⅴ. 결론
REFERENCES
참고문헌 (20)
참고문헌 신청
D. Lee, G. Kang, B. Kim, and D. H. Shim, “Assistive delivery robot application for real-world postal services,” IEEE Access, vol. 9, pp. 141981-141998, Oct. 2021.
J. Noh, K. Yang, M. Park, J. Lee, M. Kim, and K. Seo, “LiDAR point cloud augmentation for mobile robot safe navigation in indoor environment,” Journal of Institute of Control, Robotics and Systems (in Korean), vol. 28, no. 1, pp. 52-58, Jan. 2022.
S. Protasov, P. Karpyshev, I. Kalinov, P. Kopanev, N. Mikhailovskiy, A. Sedunin, and D. Tsetserukou, “CNN-based omnidirectional object detection for HermesBot autonomous delivery robot with preliminary frame classification,” Proc. of the 20th International Conference on Advanced Robotics (ICAR), Ljubljana, Slovenia, pp. 517-522, 2021.
J. Hsieh, M. Chiang, C. Fang, and S. Chen, “Online human action recognition using deep learning for indoor smart mobile robots,” Proc. of the 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, pp. 425-433, 2021.
M. Mandal, L. K. Kumar, M. S. Saran, and S. K. Vipparthi, “MotionRec: a unified deep framework for moving object recognition,” Proc. of the IEEE/CVF Winter Conference on Applications of Computer Vision, Snowmass, CO, USA, pp. 2734-2743, 2020.
J. Noh, K. Yang, M. Park, J. Lee, M. Kim, and K. Seo, “LiDAR point cloud augmentation for mobile robot safe navigation in indoor environment,” Journal of Institute of Control, Robotics and Systems (in Korean), vol. 28, no. 1, pp. 52-58, Jan. 2022.
S. Protasov, P. Karpyshev, I. Kalinov, P. Kopanev, N. Mikhailovskiy, A. Sedunin, and D. Tsetserukou, “CNN-based omnidirectional object detection for HermesBot autonomous delivery robot with preliminary frame classification,” Proc. of the 20th International Conference on Advanced Robotics (ICAR), Ljubljana, Slovenia, pp. 517-522, 2021.
J. Hsieh, M. Chiang, C. Fang, and S. Chen, “Online human action recognition using deep learning for indoor smart mobile robots,” Proc. of the 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, pp. 425-433, 2021.
M. Mandal, L. K. Kumar, M. S. Saran, and S. K. Vipparthi, “MotionRec: a unified deep framework for moving object recognition,” Proc. of the IEEE/CVF Winter Conference on Applications of Computer Vision, Snowmass, CO, USA, pp. 2734-2743, 2020.
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