Efficient robot tracking system using single-image-based object detection and position estimation
This study proposes a mother-slave robot tracking system that identifies the target, predicts its location, and tracks it based on a single image. The proposed system utilizes a Convolutional Neural Network (CNN) for object detection, to identify the target robot. The distance and angle between the...
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Published in | ICT express Vol. 10; no. 1; pp. 125 - 131 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
01.02.2024
Elsevier 한국통신학회 |
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Abstract | This study proposes a mother-slave robot tracking system that identifies the target, predicts its location, and tracks it based on a single image. The proposed system utilizes a Convolutional Neural Network (CNN) for object detection, to identify the target robot. The distance and angle between the robots are then calculated through linear regression analysis, which offers a more efficient and cost-effective solution than traditional methods. The performance of the system was evaluated, resulting in an accuracy of 99.59% for object detection, and an average distance error of 2.04% for the estimated location. |
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AbstractList | This study proposes a mother-slave robot tracking system that identifies the target, predicts its location, and tracks it based on a single image. The proposed system utilizes a Convolutional Neural Network (CNN) for object detection, to identify the target robot. The distance and angle between the robots are then calculated through linear regression analysis, which offers a more efficient and cost-effective solution than traditional methods. The performance of the system was evaluated, resulting in an accuracy of 99.59% for object detection, and an average distance error of 2.04% for the estimated location. This study proposes a mother-slave robot tracking system that identifies the target, predicts its location, and tracks it based on a single image. The proposed system utilizes a Convolutional Neural Network (CNN) for object detection, to identify the target robot. The distance and angle between the robots are then calculated through linear regression analysis, which offers a more efficient and cost-effective solution than traditional methods. The performance of the system was evaluated, resulting in an accuracy of 99.59% for object detection, and an average distance error of 2.04% for the estimated location. KCI Citation Count: 0 |
Author | Lim, Dongsun Kim, Hyuntai Kim, Jonghoek |
Author_xml | – sequence: 1 givenname: Dongsun surname: Lim fullname: Lim, Dongsun email: asme12@naver.com organization: Electrical and Electronic Convergence Department, Hongik University, Sejong 30016, Republic of Korea – sequence: 2 givenname: Jonghoek surname: Kim fullname: Kim, Jonghoek email: jonghoek@sejong.ac.kr organization: Defense System Engineering Department, Sejong University, Seoul 05006, Republic of Korea – sequence: 3 givenname: Hyuntai orcidid: 0000-0001-7401-3320 surname: Kim fullname: Kim, Hyuntai email: hyuntai@hongik.ac.kr organization: Electrical and Electronic Convergence Department, Hongik University, Sejong 30016, Republic of Korea |
BackLink | https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART003051564$$DAccess content in National Research Foundation of Korea (NRF) |
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Cites_doi | 10.1109/ACCESS.2021.3059866 10.3390/s23031134 10.1117/1.482677 10.1126/science.287.5459.1788 10.1109/AIM43001.2020.9158946 10.1177/0278364907073777 10.3390/technologies5020034 10.1109/LGRS.2019.2951626 10.1126/science.1244258 10.1016/S0030-4018(01)01572-3 10.1016/j.isatra.2019.03.019 10.1109/TRO.2015.2463671 10.1109/LRA.2021.3137896 10.1109/ICCITECHN.2017.8281816 10.1109/JSEN.2023.3252050 10.1109/LRA.2019.2895393 10.1016/j.jvcir.2016.06.011 10.3390/s20236929 10.1109/CBS.2018.8612279 10.1126/science.1165871 10.1109/ISMA.2009.5164847 10.1109/CVPR.2016.91 10.1109/ICRA.2016.7487258 10.1016/j.compag.2004.01.006 10.1109/FITI52050.2020.9424884 10.1109/RAMECH.2006.252655 |
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Keywords | Deep learning Linear regression tracking PID control Image processing |
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SubjectTerms | Deep learning Image processing Linear regression tracking PID control 전자/정보통신공학 |
Title | Efficient robot tracking system using single-image-based object detection and position estimation |
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