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Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities

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dc.contributor.author Seyfioğlu, Mehmet Saygın
dc.contributor.author Özbayoğlu, Ahmet Murat
dc.contributor.author Gürbüz, Sevgi Zübeyde
dc.date.accessioned 2019-03-25T13:59:06Z
dc.date.available 2019-03-25T13:59:06Z
dc.date.issued 2018-08
dc.identifier.citation Seyfioğlu, 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.uri https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8283539
dc.identifier.uri http://hdl.handle.net/20.500.11851/851
dc.description.abstract Radar-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.iso eng en_US
dc.publisher IEEE en_US
dc.relation.isversionof 10.1109/TAES.2018.2799758 en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Signatures en_US
dc.subject Future selection en_US
dc.subject Radar en_US
dc.subject Neural networks en_US
dc.subject Micro-Doppler en_US
dc.subject Gait recognition en_US
dc.subject Deep learning en_US
dc.subject Convolutional autoencoder (CAE) en_US
dc.title Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities en_US
dc.type article en_US
dc.relation.journal IEEE Transactions on Aerospace and Electronic Systems en_US
dc.contributor.department TOBB ETU, Faculty of Engineering, Department of Computer Engineering en_US
dc.identifier.volume 54 en_US
dc.identifier.issue 4 en_US
dc.identifier.startpage 1709 en_US
dc.identifier.endpage 1723 en_US
dc.contributor.orcid Özbayoğlu, Ahmet Murat[0000-0001-7998-5735]
dc.contributor.orcid Seyfioğlu, Mehmet Saygın[56246622800]
dc.identifier.wos WOS:000441403600010
dc.identifier.scopus 2-s2.0-85041511531
dc.contributor.tobbetuauthor Özbayoğlu, Ahmet Murat
dc.contributor.tobbetuauthor Seyfioğlu, Mehmet Saygın
dc.identifier.doi 10.1109/TAES.2018.2799758
dc.relation.publicationcategory Uluslararası yayın


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