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|Title:||Diversified Radar Micro-Doppler Simulations as Training Data for Deep Residual Neural Networks||Authors:||Seyfioğlu, Mehmet Saygın
Gürbüz, Sevgi Zübeyde
Amin, Moeness G.
|Keywords:||[No Keywords]||Issue Date:||2018||Publisher:||IEEE||Source:||IEEE Radar Conference -- APR 23-27, 2018 -- Oklahoma City, OK||Series/Report no.:||IEEE Radar Conference||Abstract:||A key challenge in radar micro-Doppler classification is the difficulty in obtaining a large amount of training data due to costs in time and human resources. Small training datasets limit the depth of deep neural networks (DNNs), and, hence, attainable classification accuracy. In this work, a novel method for diversifying Kinect-based motion capture (MOCAP) simulations of human micro-Doppler to span a wider range of potential observations, e.g. speed, body size, and style, is proposed. By applying three transformations, a small set of MOCAP measurements is expanded to generate a large training dataset for network initialization of a 30-layer deep residual neural network. Results show that the proposed training methodology and residual DNN yield improved bottleneck feature performance and the highest overall classification accuracy among other DNN architectures, including transfer learning from the 1.5 million sample ImageNet database.||URI:||https://hdl.handle.net/20.500.11851/6552||ISBN:||978-1-5386-4167-5||ISSN:||1097-5764|
|Appears in Collections:||Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering|
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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