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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/4394BDS
Repositorysid.inpe.br/sibgrapi/2020/09.15.17.37
Last Update2020:09.15.17.37.12 ams2@ecomp.poli.br
Metadatasid.inpe.br/sibgrapi/2020/09.15.17.37.12
Metadata Last Update2020:10.28.20.46.49 administrator
Citation KeySantosBastMaciLima:2020:CoVeHi
TitleCounting Vehicle with High-Precision in Brazilian Roads Using YOLOv3 and Deep SORT
FormatOn-line
Year2020
DateNov. 7-10, 2020
Access Date2021, Jan. 19
Number of Files1
Size1356 KiB
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Author1 Santos, Adson M.
2 Bastos-Filho, Carmelo J. A.
3 Maciel, Alexandre M. A.
4 Lima, Estanislau
Affiliation1 University of Pernambuco
2 University of Pernambuco
3 University of Pernambuco
4 University of Pernambuco
EditorMusse, Soraia Raupp
Cesar Junior, Roberto Marcondes
Pelechano, Nuria
Wang, Zhangyang (Atlas)
e-Mail Addressams2@ecomp.poli.br
Conference NameConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
Conference LocationVirtual
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2020-09-15 17:37:12 :: ams2@ecomp.poli.br -> administrator ::
2020-10-28 20:46:49 :: administrator -> ams2@ecomp.poli.br :: 2020
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Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
KeywordsComputer Vision, Vehicle Count, Traffic Monitoring System, Object Detection, Multiple Object Tracking.
AbstractThe Brazilian National Department of Transport Infrastructure (DNIT) maintains the National Traffic Counting Plan (PNCT). The main goal of PNCT is to evaluate the current flow of traffic on federal highways aiming to define public policies. However, DNIT still performs the quantitative classificatory surveys not automated or with invasive equipment. It is crucial for conducting traffic studies to search for more modern solutions to accomplish a higher number of automated non-invasive, and low-cost classificatory surveys. This paper proposes a system that uses YOLOv3 for object detection and the Deep SORT for multiple objects tracking algorithms. From the results over real-world videos collected in Brazilian roads, we obtained a precision above 90% in the global vehicle count. We also show that our proposal outperformed other previously proposed tools with 99.15% precision in public datasets. We believe this paper's proposal allows the development of a traffic analysis tool to be used for the automation of the volumetric traffic surveys, enabling to improve the DNIT agility and generating economy for the public coffers.
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zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/4394BDS
Languageen
Target FileArticle Sibgrapi_2020___Counting Vehicle with High-Precision_Final_CamaraReady.pdf
User Groupams2@ecomp.poli.br
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/43G4L9S
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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