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Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter

Maritime surveillance videos provide crucial on-spot kinematic traffic information (traffic volume, ship speeds, headings, etc.) for varied traffic participants (maritime regulation departments, ship crew, ship owners, etc.) which greatly benefits automated maritime situational awareness and maritim...

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Detalles Bibliográficos
Autores principales: Chen, Xinqiang, Chen, Huixing, Wu, Huafeng, Huang, Yanguo, Yang, Yongsheng, Zhang, Wenhui, Xiong, Pengwen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7039392/
https://www.ncbi.nlm.nih.gov/pubmed/32050581
http://dx.doi.org/10.3390/s20030932
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author Chen, Xinqiang
Chen, Huixing
Wu, Huafeng
Huang, Yanguo
Yang, Yongsheng
Zhang, Wenhui
Xiong, Pengwen
author_facet Chen, Xinqiang
Chen, Huixing
Wu, Huafeng
Huang, Yanguo
Yang, Yongsheng
Zhang, Wenhui
Xiong, Pengwen
author_sort Chen, Xinqiang
collection PubMed
description Maritime surveillance videos provide crucial on-spot kinematic traffic information (traffic volume, ship speeds, headings, etc.) for varied traffic participants (maritime regulation departments, ship crew, ship owners, etc.) which greatly benefits automated maritime situational awareness and maritime safety improvement. Conventional models heavily rely on visual ship features for the purpose of tracking ships from maritime image sequences which may contain arbitrary tracking oscillations. To address this issue, we propose an ensemble ship tracking framework with a multi-view learning algorithm and wavelet filter model. First, the proposed model samples ship candidates with a particle filter following the sequential importance sampling rule. Second, we propose a multi-view learning algorithm to obtain raw ship tracking results in two steps: extracting a group of distinct ship contour relevant features (i.e., Laplacian of Gaussian, local binary pattern, Gabor filter, histogram of oriented gradient, and canny descriptors) and learning high-level intrinsic ship features by jointly exploiting underlying relationships shared by each type of ship contour features. Third, with the help of the wavelet filter, we performed a data quality control procedure to identify abnormal oscillations in the ship positions which were further corrected to generate the final ship tracking results. We demonstrate the proposed ship tracker’s performance on typical maritime traffic scenarios through four maritime surveillance videos.
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spelling pubmed-70393922020-03-09 Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter Chen, Xinqiang Chen, Huixing Wu, Huafeng Huang, Yanguo Yang, Yongsheng Zhang, Wenhui Xiong, Pengwen Sensors (Basel) Article Maritime surveillance videos provide crucial on-spot kinematic traffic information (traffic volume, ship speeds, headings, etc.) for varied traffic participants (maritime regulation departments, ship crew, ship owners, etc.) which greatly benefits automated maritime situational awareness and maritime safety improvement. Conventional models heavily rely on visual ship features for the purpose of tracking ships from maritime image sequences which may contain arbitrary tracking oscillations. To address this issue, we propose an ensemble ship tracking framework with a multi-view learning algorithm and wavelet filter model. First, the proposed model samples ship candidates with a particle filter following the sequential importance sampling rule. Second, we propose a multi-view learning algorithm to obtain raw ship tracking results in two steps: extracting a group of distinct ship contour relevant features (i.e., Laplacian of Gaussian, local binary pattern, Gabor filter, histogram of oriented gradient, and canny descriptors) and learning high-level intrinsic ship features by jointly exploiting underlying relationships shared by each type of ship contour features. Third, with the help of the wavelet filter, we performed a data quality control procedure to identify abnormal oscillations in the ship positions which were further corrected to generate the final ship tracking results. We demonstrate the proposed ship tracker’s performance on typical maritime traffic scenarios through four maritime surveillance videos. MDPI 2020-02-10 /pmc/articles/PMC7039392/ /pubmed/32050581 http://dx.doi.org/10.3390/s20030932 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Xinqiang
Chen, Huixing
Wu, Huafeng
Huang, Yanguo
Yang, Yongsheng
Zhang, Wenhui
Xiong, Pengwen
Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter
title Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter
title_full Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter
title_fullStr Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter
title_full_unstemmed Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter
title_short Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter
title_sort robust visual ship tracking with an ensemble framework via multi-view learning and wavelet filter
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7039392/
https://www.ncbi.nlm.nih.gov/pubmed/32050581
http://dx.doi.org/10.3390/s20030932
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