A Self-Selective Correlation Ship Tracking Method for Smart Ocean Systems

Sensors

Xiaojiang Du¹  Mohsen Guizani²  Xu Kang³  Bin Song³  Jie Guo³ 

[1] Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA;Department of Engineering, Qatar University, Doha 2713, Qatar;The State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China;

关键词: ship tracking;    correlation filter;    negative samples mining;    self-selective model;    box regression;   

DOI  :  10.3390/s19040821

来源: DOAJ

【 摘 要 】

In recent years, with the development of the marine industry, the ship navigation environment has become more complicated. Some artificial intelligence technologies, such as computer vision, can recognize, track and count sailing ships to ensure maritime security and facilitate management for Smart Ocean systems. Aiming at the scaling problem and boundary effect problem of traditional correlation filtering methods, we propose a self-selective correlation filtering method based on box regression (BRCF). The proposed method mainly includes: (1) A self-selective model with a negative samples mining method which effectively reduces the boundary effect in strengthening the classification ability of the classifier at the same time; (2) a bounding box regression method combined with a key points matching method for the scale prediction, leading to a fast and efficient calculation. The experimental results show that the proposed method can effectively deal with the problem of ship size changes and background interference. The success rates and precisions were over 8 % higher than Discriminative Scale Space Tracking (DSST) on the marine traffic dataset of our laboratory. In terms of processing speed, the proposed method is higher than DSST by nearly 22 frames per second (FPS).

【 授权许可】

Unknown