Differential Evolution FPA-SVM for Target Classification in Foliage Environment Using Device-Free Sensing

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

Differential Evolution FPA-SVM for Target Classification in Foliage Environment Using Device-Free Sensing

Zhong, Y Huang, Y

Dutkiewicz, E

Wu, Q

Jiang, T

Permalink

Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Electrical Engineering, 2020, 516, pp. 553-560
Issue Date:: 2020-01-01

Closed Access

	Filename	Description	Size
	Zhong2020_Chapter_DifferentialEvolutionFPA-SVMFo.pdf		485.07 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zhong, Y
dc.contributor.author	Huang, Y https://orcid.org/0000-0002-1363-5318
dc.contributor.author	Dutkiewicz, E https://orcid.org/0000-0002-4268-9286
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483
dc.contributor.author	Jiang, T
dc.contributor.editor	Liang, Q
dc.contributor.editor	Liu, X
dc.contributor.editor	Na, Z
dc.contributor.editor	Wang, W
dc.contributor.editor	Mu, J
dc.contributor.editor	Zhang, B
dc.date	2018-07-14
dc.date.accessioned	2021-05-05T04:26:08Z
dc.date.available	2021-05-05T04:26:08Z
dc.date.issued	2020-01-01
dc.identifier.citation	Lecture Notes in Electrical Engineering, 2020, 516, pp. 553-560
dc.identifier.isbn	9789811365034
dc.identifier.issn	1876-1100
dc.identifier.issn	1876-1119
dc.identifier.uri	http://hdl.handle.net/10453/148692
dc.description.abstract	Target classification in foliage environment is a challenging task in realistic due to the high-clutter background and unsettled weather. To detect a particular target, e.g., human, under such an environment, is an indispensable technique with significant application value. Traditional method such as computer vision techniques is hardly leveraged since the working condition is limited. Therefore, in this paper, we attempt to tackle human detection by using the radio frequency (RF) signal with a device-free sensing. To this end, we propose a differential evolution flower pollination algorithm support vector machine (DEFPA-SVM) approach to detect human among other targets, e.g., iron cupboard and wooden board. This task can be formally described as a target classification problem. In our experiment, the proposed DEFPA-SVM can effectively attain the best performance compared to other classical multi-target classification models and achieve a faster convergent speed than the traditional FPA-SVM.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Lecture Notes in Electrical Engineering
dc.relation.ispartof	7th International Conference on Communications, Signal Processing, and Systems (CSPS)
dc.relation.ispartofseries	Proceedings of the 2018 CSPS Volume II: Signal Processing
dc.relation.isbasedon	10.1007/978-981-13-6504-1_67
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Differential Evolution FPA-SVM for Target Classification in Foliage Environment Using Device-Free Sensing
dc.type	Conference Proceeding
utslib.citation.volume	516
utslib.location.activity	China
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/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-05-05T04:26:07Z
pubs.finish-date	2018-07-16
pubs.place-of-publication	Singapore
pubs.publication-status	Published
pubs.start-date	2018-07-14
pubs.volume	516
dc.location	Singapore

Abstract:

Target classification in foliage environment is a challenging task in realistic due to the high-clutter background and unsettled weather. To detect a particular target, e.g., human, under such an environment, is an indispensable technique with significant application value. Traditional method such as computer vision techniques is hardly leveraged since the working condition is limited. Therefore, in this paper, we attempt to tackle human detection by using the radio frequency (RF) signal with a device-free sensing. To this end, we propose a differential evolution flower pollination algorithm support vector machine (DEFPA-SVM) approach to detect human among other targets, e.g., iron cupboard and wooden board. This task can be formally described as a target classification problem. In our experiment, the proposed DEFPA-SVM can effectively attain the best performance compared to other classical multi-target classification models and achieve a faster convergent speed than the traditional FPA-SVM.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/148692