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dc.contributor.authorHe, Zhenlien_US
dc.contributor.authorNan, Fengtaoen_US
dc.contributor.authorLi, Xinfaen_US
dc.contributor.authorLee, Shin-Jyeen_US
dc.contributor.authorYang, Yunen_US
dc.date.accessioned2020-07-01T05:21:14Z-
dc.date.available2020-07-01T05:21:14Z-
dc.date.issued2020-05-01en_US
dc.identifier.issn1751-956Xen_US
dc.identifier.urihttp://dx.doi.org/10.1049/iet-its.2019.0409en_US
dc.identifier.urihttp://hdl.handle.net/11536/154316-
dc.description.abstractThe legibility of traffic signs has been considered from the beginning of design, and traffic signs are easy to identify for humans. For computer systems, however, identifying traffic signs still poses a challenging problem. Both image-processing and machine-learning algorithms are constantly improving, aimed at better solving this problem. However, with a dramatic increase in the number of traffic signs, labelling a large amount of training data means high cost. Therefore, how to use a small number of labelled traffic sign data reasonably to build an efficient and high-quality traffic sign recognition (TSR) model in the Internet-of-things-based (IOT-based) transport system has been an urgent research goal. Here, the authors propose a novel semi-supervised learning approach combining global and local features for TSR in an IOT-based transport system. In their approach, histograms of oriented gradient, colour histograms (CH), and edge features (EF) are used to build different feature spaces. Meanwhile, on the unlabelled samples, a fusion feature space is found to alleviate the differences between different feature spaces. Extensive evaluations on a collection of signs from the German Traffic Sign Recognition Benchmark (GTSRB) dataset shows that the proposed approach outperforms the others and provides a potential solution for practical applications.en_US
dc.language.isoen_USen_US
dc.subjectobject recognitionen_US
dc.subjecttraffic engineering computingen_US
dc.subjectlearning (artificial intelligence)en_US
dc.subjectfeature extractionen_US
dc.subjectimage classificationen_US
dc.subjectimage fusionen_US
dc.subjectedge detectionen_US
dc.subjectimage colour analysisen_US
dc.subjectIOT-based transport systemen_US
dc.subjectGerman Traffic Sign Recognition Benchmark dataseten_US
dc.subjectglobal featuresen_US
dc.subjectlocal featuresen_US
dc.subjectlabelled traffic sign dataen_US
dc.subjectInternet-of-things-based transport systemen_US
dc.subjectsemisupervised classificationen_US
dc.subjecthigh-quality traffic sign recognition modelen_US
dc.subjecthistograms of oriented gradienten_US
dc.subjectcolour histogramsen_US
dc.subjectedge featuresen_US
dc.subjectfusion feature spaceen_US
dc.titleTraffic sign recognition by combining global and local features based on semi-supervised classificationen_US
dc.typeArticleen_US
dc.identifier.doi10.1049/iet-its.2019.0409en_US
dc.identifier.journalIET INTELLIGENT TRANSPORT SYSTEMSen_US
dc.citation.volume14en_US
dc.citation.issue5en_US
dc.citation.spage323en_US
dc.citation.epage330en_US
dc.contributor.department科技管理研究所zh_TW
dc.contributor.departmentInstitute of Management of Technologyen_US
dc.identifier.wosnumberWOS:000530480700008en_US
dc.citation.woscount1en_US
Appears in Collections:Articles