Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/11775
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dc.contributor.authorGüven, A.-
dc.contributor.authorYetik, İ.Ş.-
dc.date.accessioned2024-09-22T13:30:28Z-
dc.date.available2024-09-22T13:30:28Z-
dc.date.issued2024-
dc.identifier.isbn979-835038896-1-
dc.identifier.urihttps://doi.org/10.1109/SIU61531.2024.10601068-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/11775-
dc.descriptionBerdan Civata B.C.; et al.; Figes; Koluman; Loodos; Tarsus Universityen_US
dc.description32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 -- 15 May 2024 through 18 May 2024 -- Mersin -- 201235en_US
dc.description.abstractRecently, providing real-time navigation of unmanned aerial vehicles independent of global positioning systems has become of great importance. The state-of-the-art methods based on deep learning, which give good results in certain datasets, and the existing methods can not provide real-time and good solutions on images with dynamic and fast moving. Moreover, the methods, were developed so far, were focused on object-based tracking algorithms. In this paper, the tracking of the points belonging to the target pattern, found by image matching, was performed with the machine learning model we developed for 10 sequential video images. The features extracted for the machine learning model are: (i) the change between the points of the previous image and the image before that, (ii) the points of interest in the previous image, (iii) the changes found with the homography matrix between sequential images. It was experimentally shown that, point tracking can be achieved with the least error, on avarage about 23 pixels for a 2 mega-pixel resolution image, among the algorithms in the literature that can process more than 30 images per second in a CPU environment of 2 GHz or above. © 2024 IEEE.en_US
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedingsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectapattern detectionen_US
dc.subjectfeature extractionen_US
dc.subjectimage homographyen_US
dc.subjectimage matchingen_US
dc.subjectimage processingen_US
dc.subjectimage-object trackingen_US
dc.subjectimage-point trackingen_US
dc.subjectmachine learningen_US
dc.subjectAir navigationen_US
dc.subjectAntennasen_US
dc.subjectDeep learningen_US
dc.subjectImage matchingen_US
dc.subjectLearning systemsen_US
dc.subjectObject detectionen_US
dc.subjectPixelsen_US
dc.subjectReal time systemsen_US
dc.subjectTracking (position)en_US
dc.subjectApattern detectionen_US
dc.subjectFeatures extractionen_US
dc.subjectImage homographyen_US
dc.subjectImage objectsen_US
dc.subjectImage pointsen_US
dc.subjectImage-object trackingen_US
dc.subjectImage-point trackingen_US
dc.subjectImages processingen_US
dc.subjectMachine-learningen_US
dc.subjectObject Trackingen_US
dc.subjectPoint-trackingen_US
dc.subjectFeature extractionen_US
dc.titleRobust Point Tracking Based on Image Matching and Machine Learning on Video Images Taken From Fast Moving Cameraen_US
dc.title.alternativeHızlı Hareket Eden Kameralara Ait Video Görüntülerinde Görüntü Eşleştirme ve Makine Öğrenimi Tabanlı Gürbüz Nokta Takibien_US
dc.typeConference Objecten_US
dc.departmentTOBB ETÜen_US
dc.identifier.scopus2-s2.0-85200900819en_US
dc.institutionauthor-
dc.identifier.doi10.1109/SIU61531.2024.10601068-
dc.authorscopusid59268046300-
dc.authorscopusid57195245742-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairetypeConference Object-
item.languageiso639-1tr-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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