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Zastosuj identyfikator do podlinkowania lub zacytowania tej pozycji: http://hdl.handle.net/20.500.12128/7228
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dc.contributor.authorBernaś, Marcin-
dc.contributor.authorPłaczek, Bartłomiej-
dc.contributor.authorKorski, Wojciech-
dc.contributor.authorLoska, Piotr-
dc.contributor.authorSmyła, Jarosław-
dc.contributor.authorSzymała, Piotr-
dc.date.accessioned2018-11-26T09:46:35Z-
dc.date.available2018-11-26T09:46:35Z-
dc.date.issued2018-
dc.identifier.citationSensors, Vol. 18 (2018), Art. No. 3243pl_PL
dc.identifier.issn1424-8220-
dc.identifier.urihttp://hdl.handle.net/20.500.12128/7228-
dc.description.abstractAbstract This paper reviews low-cost vehicle and pedestrian detection methods and compares their accuracy. The main goal of this survey is to summarize the progress achieved to date and to help identify the sensing technologies that provide high detection accuracy and meet requirements related to cost and ease of installation. Special attention is paid to wireless battery-powered detectors of small dimensions that can be quickly and effortlessly installed alongside traffic lanes (on the side of a road or on a curb) without any additional supporting structures. The comparison of detection methods presented in this paper is based on results of experiments that were conducted with a variety of sensors in a wide range of configurations. During experiments various sensor sets were analyzed. It was shown that the detection accuracy can be significantly improved by fusing data from appropriately selected set of sensors. The experimental results reveal that accurate vehicle detection can be achieved by using sets of passive sensors. Application of active sensors was necessary to obtain satisfactory results in case of pedestrian detection.pl_PL
dc.language.isoenpl_PL
dc.rightsUznanie autorstwa 3.0 Polska*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/pl/*
dc.subjectvehicle detectionpl_PL
dc.subjectpedestrian detectionpl_PL
dc.subjectsensor fusionpl_PL
dc.subjectlow-cost sensorspl_PL
dc.subjectintelligent transport systemspl_PL
dc.subjectmachine learningpl_PL
dc.titleA Survey and Comparison of Low-Cost Sensing Technologies for Road Traffic Monitoringpl_PL
dc.typeinfo:eu-repo/semantics/articlepl_PL
dc.relation.journalSensorspl_PL
dc.identifier.doi10.3390/s18103243-
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Uznanie Autorstwa 3.0 Polska Creative Commons Creative Commons