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

Yi Zhong; Yan Huang; Eryk Dutkiewicz; Qiang Wu; Ting Jiang

doi:10.1007/978-981-13-6504-1_67

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

Yi Zhong, Yan Huang^*, Eryk Dutkiewicz, Qiang Wu, Ting Jiang

^*Corresponding author for this work

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

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.

Original language	English
Title of host publication	Communications, Signal Processing, and Systems - Proceedings of the 2018 CSPS Volume II
Subtitle of host publication	Signal Processing
Editors	Qilian Liang, Xin Liu, Zhenyu Na, Wei Wang, Jiasong Mu, Baoju Zhang
Publisher	Springer Verlag
Pages	553-560
Number of pages	8
ISBN (Print)	9789811365034
DOIs	https://doi.org/10.1007/978-981-13-6504-1_67
Publication status	Published - 2020
Externally published	Yes
Event	International Conference on Communications, Signal Processing, and Systems, CSPS 2018 - Dalian, China Duration: 14 Jul 2018 → 16 Jul 2018

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	516
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	International Conference on Communications, Signal Processing, and Systems, CSPS 2018
Country/Territory	China
City	Dalian
Period	14/07/18 → 16/07/18

Keywords

Foliage environment
Human detection
Target classification

Access to Document

10.1007/978-981-13-6504-1_67

Cite this

Zhong, Y., Huang, Y., Dutkiewicz, E., Wu, Q., & Jiang, T. (2020). Differential Evolution FPA-SVM for Target Classification in Foliage Environment Using Device-Free Sensing. In Q. Liang, X. Liu, Z. Na, W. Wang, J. Mu, & B. Zhang (Eds.), Communications, Signal Processing, and Systems - Proceedings of the 2018 CSPS Volume II: Signal Processing (pp. 553-560). (Lecture Notes in Electrical Engineering; Vol. 516). Springer Verlag. https://doi.org/10.1007/978-981-13-6504-1_67

Zhong, Yi ; Huang, Yan ; Dutkiewicz, Eryk et al. / Differential Evolution FPA-SVM for Target Classification in Foliage Environment Using Device-Free Sensing. Communications, Signal Processing, and Systems - Proceedings of the 2018 CSPS Volume II: Signal Processing. editor / Qilian Liang ; Xin Liu ; Zhenyu Na ; Wei Wang ; Jiasong Mu ; Baoju Zhang. Springer Verlag, 2020. pp. 553-560 (Lecture Notes in Electrical Engineering).

@inproceedings{16fb0ed3ba4943fab8c63185ceb951c4,

title = "Differential Evolution FPA-SVM for Target Classification in Foliage Environment Using Device-Free Sensing",

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.",

keywords = "Foliage environment, Human detection, Target classification",

author = "Yi Zhong and Yan Huang and Eryk Dutkiewicz and Qiang Wu and Ting Jiang",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; International Conference on Communications, Signal Processing, and Systems, CSPS 2018 ; Conference date: 14-07-2018 Through 16-07-2018",

year = "2020",

doi = "10.1007/978-981-13-6504-1_67",

language = "English",

isbn = "9789811365034",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Verlag",

pages = "553--560",

editor = "Qilian Liang and Xin Liu and Zhenyu Na and Wei Wang and Jiasong Mu and Baoju Zhang",

booktitle = "Communications, Signal Processing, and Systems - Proceedings of the 2018 CSPS Volume II",

address = "Germany",

}

Zhong, Y, Huang, Y, Dutkiewicz, E, Wu, Q & Jiang, T 2020, Differential Evolution FPA-SVM for Target Classification in Foliage Environment Using Device-Free Sensing. in Q Liang, X Liu, Z Na, W Wang, J Mu & B Zhang (eds), Communications, Signal Processing, and Systems - Proceedings of the 2018 CSPS Volume II: Signal Processing. Lecture Notes in Electrical Engineering, vol. 516, Springer Verlag, pp. 553-560, International Conference on Communications, Signal Processing, and Systems, CSPS 2018, Dalian, China, 14/07/18. https://doi.org/10.1007/978-981-13-6504-1_67

Differential Evolution FPA-SVM for Target Classification in Foliage Environment Using Device-Free Sensing. / Zhong, Yi; Huang, Yan; Dutkiewicz, Eryk et al.
Communications, Signal Processing, and Systems - Proceedings of the 2018 CSPS Volume II: Signal Processing. ed. / Qilian Liang; Xin Liu; Zhenyu Na; Wei Wang; Jiasong Mu; Baoju Zhang. Springer Verlag, 2020. p. 553-560 (Lecture Notes in Electrical Engineering; Vol. 516).

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

TY - GEN

T1 - Differential Evolution FPA-SVM for Target Classification in Foliage Environment Using Device-Free Sensing

AU - Zhong, Yi

AU - Huang, Yan

AU - Dutkiewicz, Eryk

AU - Wu, Qiang

AU - Jiang, Ting

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

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KW - Human detection

KW - Target classification

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DO - 10.1007/978-981-13-6504-1_67

M3 - Conference contribution

AN - SCOPUS:85071511501

SN - 9789811365034

T3 - Lecture Notes in Electrical Engineering

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EP - 560

BT - Communications, Signal Processing, and Systems - Proceedings of the 2018 CSPS Volume II

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A2 - Liu, Xin

A2 - Na, Zhenyu

A2 - Wang, Wei

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PB - Springer Verlag

T2 - International Conference on Communications, Signal Processing, and Systems, CSPS 2018

Y2 - 14 July 2018 through 16 July 2018

ER -

Zhong Y, Huang Y, Dutkiewicz E, Wu Q, Jiang T. Differential Evolution FPA-SVM for Target Classification in Foliage Environment Using Device-Free Sensing. In Liang Q, Liu X, Na Z, Wang W, Mu J, Zhang B, editors, Communications, Signal Processing, and Systems - Proceedings of the 2018 CSPS Volume II: Signal Processing. Springer Verlag. 2020. p. 553-560. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-13-6504-1_67