Vehicle Classification Using Inductive Loops Sensors
| but.event.date | 27.04.2017 | cs |
| but.event.title | Student EEICT 2017 | cs |
| dc.contributor.author | Halachkin, Aliaksei | |
| dc.date.accessioned | 2020-05-07T09:40:29Z | |
| dc.date.available | 2020-05-07T09:40:29Z | |
| dc.date.issued | 2017 | cs |
| dc.description.abstract | This project is dedicated to the problem of vehicle classification using inductive loop sensors. Developed classifier is based on nearest neighbors and logistic regression models and achieves 94 % accuracy on classification scheme with 9 vehicle classes. | en |
| dc.format | text | cs |
| dc.format.extent | 302-304 | cs |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Proceedings of the 23st Conference STUDENT EEICT 2017. s. 302-304. ISBN 978-80-214-5496-5 | cs |
| dc.identifier.isbn | 978-80-214-5496-5 | |
| dc.identifier.uri | http://hdl.handle.net/11012/187114 | |
| dc.language.iso | en | cs |
| dc.publisher | Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií | cs |
| dc.relation.ispartof | Proceedings of the 23st Conference STUDENT EEICT 2017 | en |
| dc.relation.uri | http://www.feec.vutbr.cz/EEICT/ | cs |
| dc.rights | © Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií | cs |
| dc.rights.access | openAccess | en |
| dc.subject | inductive loops | en |
| dc.subject | nearest neighbors | en |
| dc.subject | logistic regression | en |
| dc.subject | vehicle classification | en |
| dc.title | Vehicle Classification Using Inductive Loops Sensors | en |
| dc.type.driver | conferenceObject | en |
| dc.type.status | Peer-reviewed | en |
| dc.type.version | publishedVersion | en |
| eprints.affiliatedInstitution.department | Fakulta elektrotechniky a komunikačních technologií | cs |
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