Pedestrian Detection In Image By Machine Learning
| but.event.date | 25.04.2019 | cs |
| but.event.title | Student EEICT 2019 | cs |
| dc.contributor.author | Tilgner, Martin | |
| dc.date.accessioned | 2020-04-16T07:19:35Z | |
| dc.date.available | 2020-04-16T07:19:35Z | |
| dc.date.issued | 2019 | cs |
| dc.description.abstract | This work deals with pedestrian detection via convolutional neural network which can be used in autonomous car driving systems to improve travel safety. The work focuses on the influence of the training dataset on the resulting network behavior. The Faster R-CNN with ResNet 101 as backbone network and the SSDLite with MobileNet v2 as backbone network meta-architectures were selected for parameter testing. Both networks achieved real-time detection while accuracy was 61.92 % for the Faster R-CNN and 31.72 % for the SSDLite. | en |
| dc.format | text | cs |
| dc.format.extent | 409-412 | cs |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Proceedings of the 25st Conference STUDENT EEICT 2019. s. 409-412. ISBN 978-80-214-5735-5 | cs |
| dc.identifier.isbn | 978-80-214-5735-5 | |
| dc.identifier.uri | http://hdl.handle.net/11012/186705 | |
| dc.language.iso | cs | cs |
| dc.publisher | Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií | cs |
| dc.relation.ispartof | Proceedings of the 25st Conference STUDENT EEICT 2019 | 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 | Object detection | en |
| dc.subject | Convolutional Neural Network | en |
| dc.subject | Machine learning | en |
| dc.subject | Faster R-CNN | en |
| dc.subject | SSDLite | en |
| dc.title | Pedestrian Detection In Image By Machine Learning | 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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