Passive optical detection and classification of flying objects

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but.event.date26.04.2022cs
but.event.titleSTUDENT EEICT 2022cs
dc.contributor.authorMošková, Andrea
dc.contributor.authorVlachová Hutová, Eliška
dc.date.accessioned2022-12-06T13:21:57Z
dc.date.available2022-12-06T13:21:57Z
dc.date.issued2022cs
dc.description.abstractThe article presents our solution for the classification of moving flying objects in a video sequence captured by a static camera. The tool uses the extraction of scale and rotation invariant SIFT features, which allow the multi-class SVM to classify the examined object into one of the considered classes: ‘bird’, ‘plane’ or ‘negative’. The most successful of our tested models achieved accuracy of over 90% and their recall and precision for each class reached values above 90%.en
dc.formattextcs
dc.format.extent67-70cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings II of the 28st Conference STUDENT EEICT 2022: Selected papers. s. 67-70. ISBN 978-80-214-6030-0cs
dc.identifier.doi10.13164/eeict.2022.67
dc.identifier.isbn978-80-214-6030-0
dc.identifier.urihttp://hdl.handle.net/11012/208604
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings II of the 28st Conference STUDENT EEICT 2022: Selected papersen
dc.relation.urihttps://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazenics
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectObject recognition, machine learning, SIFT, SVM, Matlab, motion detectionen
dc.titlePassive optical detection and classification of flying objectsen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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