Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/851
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSeyfioğlu, Mehmet Saygın-
dc.contributor.authorÖzbayoğlu, Ahmet Murat-
dc.contributor.authorGürbüz, Sevgi Zübeyde-
dc.date.accessioned2019-03-25T13:59:06Z
dc.date.available2019-03-25T13:59:06Z
dc.date.issued2018-08
dc.identifier.citationSeyfioğlu, M. S., Özbayoğlu, A. M., & Gürbüz, S. Z. (2018). Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities. IEEE Transactions on Aerospace and Electronic Systems, 54(4), 1709-1723.en_US
dc.identifier.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8283539-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/851-
dc.description.abstractRadar-based activity recognition is a problem that has been of great interest due to applications such as border control and security, pedestrian identification for automotive safety, and remote health monitoring. This paper seeks to show the efficacy of micro-Doppler analysis to distinguish even those gaits whose micro-Doppler signatures are not visually distinguishable. Moreover, a three-layer, deep convolutional autoencoder (CAE) is proposed, which utilizes unsupervised pretraining to initialize the weights in the subsequent convolutional layers. This architecture is shown to be more effective than other deep learning architectures, such as convolutional neural networks and autoencoders, as well as conventional classifiers employing predefined features, such as support vector machines (SVM), random forest, and extreme gradient boosting. Results show the performance of the proposed deep CAE yields a correct classification rate of 94.2% for micro-Doppler signatures of 12 different human activities measured indoors using a 4 GHz continuous wave radar-17.3% improvement over SVM.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofIEEE Transactions on Aerospace and Electronic Systemsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSignaturesen_US
dc.subjectFuture selectionen_US
dc.subjectRadaren_US
dc.subjectNeural networksen_US
dc.subjectMicro-Doppleren_US
dc.subjectGait recognitionen_US
dc.subjectDeep learningen_US
dc.subjectConvolutional autoencoder (CAE)en_US
dc.titleDeep convolutional autoencoder for radar-based classification of similar aided and unaided human activitiesen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.volume54en_US
dc.identifier.issue4en_US
dc.identifier.startpage1709en_US
dc.identifier.endpage1723en_US
dc.authorid0000-0001-7998-5735-
dc.authorid0000-0002-2961-4976-
dc.identifier.wosWOS:000441403600010en_US
dc.identifier.scopus2-s2.0-85041511531en_US
dc.institutionauthorÖzbayoğlu, Ahmet Murat-
dc.institutionauthorSeyfioğlu, Mehmet Saygın-
dc.identifier.doi10.1109/TAES.2018.2799758-
dc.identifier.doi10.1109/TAES.2018.2799758-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeArticle-
item.cerifentitytypePublications-
item.languageiso639-1en-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

130
checked on Mar 23, 2024

WEB OF SCIENCETM
Citations

217
checked on Mar 23, 2024

Page view(s)

228
checked on Mar 25, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.