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

Authors :
Hu Liu
Xueqian Xu
Xinqiang Chen
Chaofeng Li
Meilin Wang
Source :
Journal of Marine Science and Engineering, Vol 10, Iss 3, p 444 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

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.

Details

Language :
English
ISSN :
20771312
Volume :
10
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Journal of Marine Science and Engineering
Publication Type :
Academic Journal
Accession number :
edsdoj.bf1165ddffdb43a493659605ec3cd4af
Document Type :
article
Full Text :
https://doi.org/10.3390/jmse10030444