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Title: Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities
Authors: Seyfioğlu, Mehmet Saygın
Özbayoğlu, Ahmet Murat
Gürbüz, Sevgi Zübeyde
Keywords: Signatures
Future selection
Neural networks
Gait recognition
Deep learning
Convolutional autoencoder (CAE)
Issue Date: Aug-2018
Publisher: IEEE
Source: 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.
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.
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

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