연구성과
Multi-Target Tracking Considering the Uncertainty of Deep Learning-based Object Detection of Marine Radar Images
- 작성자관리자
- 작성일2023.09.07
- 조회수100
Eunghyun Kim, Jonghwi Kim, Jinwhan Kim
상세내용
BibTeX
- 저자 : Eunghyun Kim, Jonghwi Kim, Jinwhan Kim
- 논문명 : Multi-Target Tracking Considering the Uncertainty of Deep Learning-based Object Detection of Marine Radar Images
- 학회명 : 2023 20th International Conference on Ubiquitous Robots (UR)
- 발간년도 : 2023
- 발간년도 : 2023
- 발간월 : June
- 초록 : In this paper, a multi-target tracking approach that integrates the extended Kalman filter and deep learningbased object detection in marine radar images is presented. The Gaussian YOLOv3 method is utilized for object detection, providing both position measurements and their uncertainties. The extended Kalman filter is employed to estimate the position, heading, and speed of each detected target considering the uncertainty values obtained from the object-detection process. The global nearest neighbor-based data association and a dual filter structure composed of a confirmed track and a reserved track are applied to enhance the robustness of the tracking process. The feasibility of the proposed algorithm is validated through a real-world marine radar dataset collected in a coastal environment.