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Human Detection and Activity Classification Based on Micro-Doppler Signatures Using Deep Convolutional Neural Networks

Title
Human Detection and Activity Classification Based on Micro-Doppler Signatures Using Deep Convolutional Neural Networks
Authors
Kim, YoungwookMoon, Taesup
Issue Date
2016-01
Citation
IEEE Geoscience and Remote Sensing Letters, 13(1), 8-12
Type
Article
Article Type
Article
Keywords
Activity ClassificationsClassification (of Information)Classification BoundaryConvolutionConvolutional Neural NetworkDeep LearningDomain KnowledgeDoppler RadarHigh-AccuracyHuman ActivitiesHuman Activity ClassificationHuman DetectionLearning AlgorithmsMicro-DopplerNeural Networks
ISSN
1545-598X
Abstract
We propose the use of deep convolutional neural networks (DCNNs) for human detection and activity classification based on Doppler radar. Previously, proposed schemes for these problems remained in the conventional supervised learning paradigm that relies on the design of handcrafted features. Whereas these schemes attained high accuracy, the requirement for domain knowledge of each problem limits the scalability of the proposed schemes. In this letter, we present an alternative deep learning approach. We apply the DCNN, one of the most successful deep learning algorithms, directly to a raw micro-Doppler spectrogram for both human detection and activity classification problem. The DCNN can jointly learn the necessary features and classification boundaries using the measured data without employing any explicit features on the micro-Doppler signals. We show that the DCNN can achieve accuracy results of 97.6% for human detection and 90.9% for human activity classification. © 2015 IEEE.
URI
http://hdl.handle.net/20.500.11750/5128
DOI
10.1109/LGRS.2015.2491329
Publisher
Institute of Electrical and Electronics Engineers Inc.
Files:
There are no files associated with this item.
Collection:
ETC1. Journal Articles


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