Generative Adversarial Networks to Augment Micro-Doppler Signatures for the Classification of Human Activity

Ibrahim Alnujaim; Daegun Oh; Youngwook Kim

doi:10.1109/IGARSS.2019.8898073

Generative Adversarial Networks to Augment Micro-Doppler Signatures for the Classification of Human Activity

Ibrahim Alnujaim
, Daegun Oh
, Youngwook Kim

Division of Intelligent Robot

California State University Fresno

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

19 Scopus citations

Abstract

Collecting a large amount of data for radar requires a significant amount of time, labor, and money. In deep convolutional neural networks, a small dataset causes the problem of overfitting. We herein introduce the employment of data augmentation using generative adversarial networks (GANs) to solve the data deficiency problem. In this study, we tested the feasibility of using generative adversarial networks to generate micro-Doppler signatures for seven human activities. Moreover, we use produced fake images to train deep convolutional neural networks. We found that the use of augmented data improves classification accuracy. In addition, the quality of GAN output was evaluated in terms of classification accuracy.

Original language	English
Title of host publication	2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	9459-9461
Number of pages	3
ISBN (Electronic)	9781538691540
DOIs	https://doi.org/10.1109/IGARSS.2019.8898073
State	Published - Jul 2019
Event	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan Duration: 28 Jul 2019 → 2 Aug 2019

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Country/Territory	Japan
City	Yokohama
Period	28/07/19 → 2/08/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Human activity classification
adversarial generative network
deep learning
microDoppler signatures

Access to Document

10.1109/IGARSS.2019.8898073

Cite this

Alnujaim, I., Oh, D., & Kim, Y. (2019). Generative Adversarial Networks to Augment Micro-Doppler Signatures for the Classification of Human Activity. In 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings (pp. 9459-9461). Article 8898073 (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2019.8898073

Alnujaim, Ibrahim ; Oh, Daegun ; Kim, Youngwook. / Generative Adversarial Networks to Augment Micro-Doppler Signatures for the Classification of Human Activity. 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 9459-9461 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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title = "Generative Adversarial Networks to Augment Micro-Doppler Signatures for the Classification of Human Activity",

abstract = "Collecting a large amount of data for radar requires a significant amount of time, labor, and money. In deep convolutional neural networks, a small dataset causes the problem of overfitting. We herein introduce the employment of data augmentation using generative adversarial networks (GANs) to solve the data deficiency problem. In this study, we tested the feasibility of using generative adversarial networks to generate micro-Doppler signatures for seven human activities. Moreover, we use produced fake images to train deep convolutional neural networks. We found that the use of augmented data improves classification accuracy. In addition, the quality of GAN output was evaluated in terms of classification accuracy.",

keywords = "Human activity classification, adversarial generative network, deep learning, microDoppler signatures",

author = "Ibrahim Alnujaim and Daegun Oh and Youngwook Kim",

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Alnujaim, I, Oh, D & Kim, Y 2019, Generative Adversarial Networks to Augment Micro-Doppler Signatures for the Classification of Human Activity. in 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings., 8898073, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., pp. 9459-9461, 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019, Yokohama, Japan, 28/07/19. https://doi.org/10.1109/IGARSS.2019.8898073

Generative Adversarial Networks to Augment Micro-Doppler Signatures for the Classification of Human Activity. / Alnujaim, Ibrahim; Oh, Daegun; Kim, Youngwook.
2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 9459-9461 8898073 (International Geoscience and Remote Sensing Symposium (IGARSS)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Alnujaim, Ibrahim

AU - Oh, Daegun

AU - Kim, Youngwook

PY - 2019/7

Y1 - 2019/7

N2 - Collecting a large amount of data for radar requires a significant amount of time, labor, and money. In deep convolutional neural networks, a small dataset causes the problem of overfitting. We herein introduce the employment of data augmentation using generative adversarial networks (GANs) to solve the data deficiency problem. In this study, we tested the feasibility of using generative adversarial networks to generate micro-Doppler signatures for seven human activities. Moreover, we use produced fake images to train deep convolutional neural networks. We found that the use of augmented data improves classification accuracy. In addition, the quality of GAN output was evaluated in terms of classification accuracy.

AB - Collecting a large amount of data for radar requires a significant amount of time, labor, and money. In deep convolutional neural networks, a small dataset causes the problem of overfitting. We herein introduce the employment of data augmentation using generative adversarial networks (GANs) to solve the data deficiency problem. In this study, we tested the feasibility of using generative adversarial networks to generate micro-Doppler signatures for seven human activities. Moreover, we use produced fake images to train deep convolutional neural networks. We found that the use of augmented data improves classification accuracy. In addition, the quality of GAN output was evaluated in terms of classification accuracy.

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KW - adversarial generative network

KW - deep learning

KW - microDoppler signatures

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DO - 10.1109/IGARSS.2019.8898073

M3 - Conference contribution

AN - SCOPUS:85077679228

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BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings

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Alnujaim I, Oh D, Kim Y. Generative Adversarial Networks to Augment Micro-Doppler Signatures for the Classification of Human Activity. In 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 9459-9461. 8898073. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2019.8898073

Generative Adversarial Networks to Augment Micro-Doppler Signatures for the Classification of Human Activity

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Keywords

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