Real-Time Ship Tracking under Challenges of Scale Variation and Different Visibility Weather Conditions

被引:9
作者
Liu, Hu [1 ]
Xu, Xueqian [2 ]
Chen, Xinqiang [3 ]
Li, Chaofeng [3 ]
Wang, Meilin [3 ]
机构
[1] Zhejiang Ocean Univ, Sch Naval Architecture & Maritime, Zhoushan 316022, Peoples R China
[2] Wuhan Univ Technol, Intelligent Transport Syst Res Ctr ITSC, Wuhan 430063, Peoples R China
[3] Shanghai Maritime Univ, Inst Logist Sci & Engn, Shanghai 201306, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
visual ship tracking; scale-adaptive kernelized correlation filter; poor visibility condition; maritime situation awareness; smart ship; SPEED; IMAGES; MODEL;
D O I
10.3390/jmse10030444
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
Visual ship tracking provides crucial kinematic traffic information to maritime traffic participants, which helps to accurately predict ship traveling behaviors in the near future. Traditional ship tracking models obtain a satisfactory performance by exploiting distinct features from maritime images, which may fail when the ship scale varies in image sequences. Moreover, previous frameworks have not paid much attention to weather condition interferences (e.g., visibility). To address this challenge, we propose a scale-adaptive ship tracking framework with the help of a kernelized correlation filter (KCF) and a log-polar transformation operation. First, the proposed ship tracker employs a conventional KCF model to obtain the raw ship position in the current maritime image. Second, both the previous step output and ship training sample are transformed into a log-polar coordinate system, which are further processed with the correlation filter to determine ship scale factor and to suppress the negative influence of the weather conditions. We verify the proposed ship tracker performance on three typical maritime scenarios under typical navigational weather conditions (i.e., sunny, fog). The findings of the study can help traffic participants efficiently obtain maritime situation awareness information from maritime videos, in real time, under different visibility weather conditions.
引用
收藏
页数:16
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