Open AccessA Ship Target Detection and Tracking Algorithm Based on Graph Matching
Author(s) -
Gaoyue Li,
Yulong Qiao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1873/1/012056
Subject(s) - kalman filter , computer vision , artificial intelligence , computer science , extended kalman filter , graph , frame (networking) , algorithm , sensor fusion , nonlinear system , matching (statistics) , position (finance) , feature (linguistics) , tracking (education) , filter (signal processing) , mathematics , theoretical computer science , psychology , telecommunications , pedagogy , linguistics , statistics , physics , philosophy , finance , quantum mechanics , economics
This paper proposes a target detection and tracking system based on Hungarian graph matching algorithm and Kalman filter. Specifically, the algorithm converts continuous frame images of a detection network such as YOLOv3 into graph structure data as input information, and expresses the position information between the target and the target as points and lines in the figure. Then the Kalman filter is used to predict the position of the target in the next frame, and then the largest match between the picture constructed by the current frame and the picture constructed by the next frame is determined by the metric method of the fusion of the apparent feature and the motion feature, to achieve the purpose of tracking the target. For ships doing nonlinear motion, this paper proposes to improve the traditional Kalman filter system to an unscented Kalman filter system based on UT transformation. Experimental data shows that this method effectively improves the tracking effect of ships doing nonlinear motion.