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
N1 - Publisher Copyright:
© 2020, Springer Nature Singapore Pte Ltd.
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.
KW - Foliage environment
KW - Human detection
KW - Target classification
UR - http://www.scopus.com/inward/record.url?scp=85071511501&partnerID=8YFLogxK
U2 - 10.1007/978-981-13-6504-1_67
DO - 10.1007/978-981-13-6504-1_67
M3 - Conference contribution
AN - SCOPUS:85071511501
SN - 9789811365034
T3 - Lecture Notes in Electrical Engineering
SP - 553
EP - 560
BT - Communications, Signal Processing, and Systems - Proceedings of the 2018 CSPS Volume II
A2 - Liang, Qilian
A2 - Liu, Xin
A2 - Na, Zhenyu
A2 - Wang, Wei
A2 - Mu, Jiasong
A2 - Zhang, Baoju
PB - Springer Verlag
T2 - International Conference on Communications, Signal Processing, and Systems, CSPS 2018
Y2 - 14 July 2018 through 16 July 2018
ER -