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dc.creatorKang, X
dc.creatorSong, B
dc.creatorGuo, J
dc.creatorDu, X
dc.creatorGuizani, M
dc.date.accessioned2020-12-16T18:03:43Z
dc.date.available2020-12-16T18:03:43Z
dc.date.issued2019-02-02
dc.identifier.issn1424-8220
dc.identifier.issn1424-8220
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/4556
dc.identifier.other30781563 (pubmed)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/4574
dc.description.abstract© 2019 by the authors. Licensee MDPI, Basel, Switzerland. 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).
dc.format.extent821-821
dc.language.isoen
dc.relation.haspartSensors (Switzerland)
dc.relation.isreferencedbyMDPI AG
dc.rightsCC BY
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectship tracking
dc.subjectcorrelation filter
dc.subjectnegative samples mining
dc.subjectself-selective model
dc.subjectbox regression
dc.titleA self-selective correlation ship tracking method for smart ocean systems
dc.typeArticle
dc.type.genreJournal Article
dc.relation.doi10.3390/s19040821
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.creator.orcidDu, Xiaojiang|0000-0003-4235-9671
dc.date.updated2020-12-16T18:03:36Z
refterms.dateFOA2020-12-16T18:03:44Z


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