Cost-Effective Foliage Penetration Human Detection under Severe Weather Conditions Based on Auto-Encoder/Decoder Neural Network

Huang, Y; Zhong, Y; Wu, Q; Dutkiewicz, E; Jiang, T

Cost-Effective Foliage Penetration Human Detection under Severe Weather Conditions Based on Auto-Encoder/Decoder Neural Network

Huang, Y

Zhong, Y

Wu, Q

Dutkiewicz, E

Jiang, T

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Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2019, 6 (4), pp. 6190 - 6200
Issue Date:: 2019-08-01

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Field	Value	Language
dc.contributor.author	Huang, Y https://orcid.org/0000-0002-1363-5318	en_US
dc.contributor.author	Zhong, Y https://orcid.org/0000-0002-9309-3407	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Dutkiewicz, E https://orcid.org/0000-0002-4268-9286	en_US
dc.contributor.author	Jiang, T	en_US
dc.date.issued	2019-08-01	en_US
dc.identifier.citation	IEEE Internet of Things Journal, 2019, 6 (4), pp. 6190 - 6200	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131306
dc.description.abstract	© 2014 IEEE. Military surveillance events and rescue activities are vital missions for the Internet-of-Things. To this end, foliage penetration for human detection plays an important role. However, although the feasibility of that mission has been validated, we observe that it still cannot perform promisingly under severe weather conditions, such as rainy, foggy, and snowy days. Therefore, in this paper, experiments are conducted under severe weather conditions based on a proposed deep learning approach. We present an auto-encoder/decoder (Auto-ED) deep neural network that can learn the deep representation and conduct classification task concurrently. Since the property of cost-effective, the device-free sensing techniques are used to address human detection in our case. As we pursue the signal-based mission, two components are involved in the proposed Auto-ED approach. First, an encoder is utilized that encode signal-based inputs into higher dimensional tensors by fractionally strided convolution operations. Then, a decoder is leveraged with convolution operations to extract deep representations and learn the classifier simultaneously. To verify the effectiveness of the proposed approach, we compare it with several machine learning approaches under different weather conditions. Also, a simulation experiment is conducted by adding additive white Gaussian noise to the original target signals with different signal to noise ratios. Experimental results demonstrate that the proposed approach can best tackle the challenge of human detection under severe weather conditions in the high-clutter foliage environment, which indicates its potential application values in the near future.	en_US
dc.relation.ispartof	IEEE Internet of Things Journal	en_US
dc.relation.isbasedon	10.1109/JIOT.2018.2878880	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Cost-Effective Foliage Penetration Human Detection under Severe Weather Conditions Based on Auto-Encoder/Decoder Neural Network	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	6	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	1005 Communications Technologies	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	6	en_US

Abstract:

© 2014 IEEE. Military surveillance events and rescue activities are vital missions for the Internet-of-Things. To this end, foliage penetration for human detection plays an important role. However, although the feasibility of that mission has been validated, we observe that it still cannot perform promisingly under severe weather conditions, such as rainy, foggy, and snowy days. Therefore, in this paper, experiments are conducted under severe weather conditions based on a proposed deep learning approach. We present an auto-encoder/decoder (Auto-ED) deep neural network that can learn the deep representation and conduct classification task concurrently. Since the property of cost-effective, the device-free sensing techniques are used to address human detection in our case. As we pursue the signal-based mission, two components are involved in the proposed Auto-ED approach. First, an encoder is utilized that encode signal-based inputs into higher dimensional tensors by fractionally strided convolution operations. Then, a decoder is leveraged with convolution operations to extract deep representations and learn the classifier simultaneously. To verify the effectiveness of the proposed approach, we compare it with several machine learning approaches under different weather conditions. Also, a simulation experiment is conducted by adding additive white Gaussian noise to the original target signals with different signal to noise ratios. Experimental results demonstrate that the proposed approach can best tackle the challenge of human detection under severe weather conditions in the high-clutter foliage environment, which indicates its potential application values in the near future.

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http://hdl.handle.net/10453/131306