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.
Target classification Human detection Foliage environment
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This research is supported by NSFC 61671075 and NSFC 61631003.
Andriyenko A, Schindler K, Roth S. Discrete-continuous optimization for multi-target tracking. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). New York: IEEE; 2012. p. 1926–33.Google Scholar
Chan T-K, Kuga Y, Ishimaru A. Experimental studies on circular SAR imaging in clutter using angular correlation function technique. IEEE Trans Geosci Remote Sens. 1999;37(5):2192–7.CrossRefGoogle Scholar
Frolind P-O, Gustavsson A, Lundberg M, Ulander LM. Circular-aperture VHF-band synthetic aperture radar for detection of vehicles in forest concealment. IEEE Trans Geosci Remote Sens. 2012;50(4):1329–39.CrossRefGoogle Scholar
Geng Y, Chen J, Fu R, Bao G, Pahlavan K. Enlighten wearable physiological monitoring systems: on-body RF characteristics based human motion classification using a support vector machine. IEEE Trans Mob Comput. 2016;15(3):656–71.CrossRefGoogle Scholar
Huang Y, Sheng H, Liu Y, Zheng Y, Xiong Z. Person re-identification by unsupervised color spatial pyramid matching. In: International conference on knowledge science, engineering and management. Berlin: Springer; 2015. p. 799–810.CrossRefGoogle Scholar
Huang Y, Sheng H, Xiong Z. Person re-identification based on hierarchical bipartite graph matching. In: 2016 IEEE international conference on image processing (ICIP). New York: IEEE; 2016. p. 4255–9.Google Scholar
Huang Y, Sheng H, Zheng Y, Xiong Z. Deepdiff: learning deep difference features on human body parts for person re-identification. Neurocomputing. 2017;241:191–203.CrossRefGoogle Scholar
Huang Y, Xu J, Wu Q, Zheng Z, Zhang Z, Zhang J. Multi-pseudo regularized label for generated data in person re-identification. IEEE Trans Image Process. 2019;28(3):1391–1403.MathSciNetCrossRefGoogle Scholar
Joon Oh S, Benenson R, Fritz M, Schiele B. Person recognition in personal photo collections. In: Proceedings of the IEEE international conference on computer vision. 2015. p. 3862–70.Google Scholar
Milan A, Roth S, Schindler K. Continuous energy minimization for multitarget tracking. IEEE Trans Pattern Anal Mach Intell. 2014;36(1):58–72.CrossRefGoogle Scholar
Muñoz-Marí J, Bovolo F, Gómez-Chova L, Bruzzone L, Camp-Valls G. Semisupervised one-class support vector machines for classification of remote sensing data. IEEE Trans Geosci Remote Sens. 2010;48(8):3188–97.CrossRefGoogle Scholar
Nguyen DT, Li W, Ogunbona PO. Human detection from images and videos: a survey. Pattern Recognit. 2016;51:148–75.CrossRefGoogle Scholar
Oh SJ, Benenson R, Fritz M, Schiele B. Faceless person recognition: privacy implications in social media. In: European conference on computer vision. Berlin: Springer; 2016. p. 19–35.CrossRefGoogle Scholar
Satpathy A, Jiang X, Eng H-L. Human detection by quadratic classification on subspace of extended histogram of gradients. IEEE Trans Image Process. 2014;23(1):287–97.MathSciNetCrossRefGoogle Scholar
Storn R, Price K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Global optim. 1997;11(4):341–59.MathSciNetCrossRefGoogle Scholar
Tang Y, Zhang Y-Q, Chawla NV, Krasser S. Svms modeling for highly imbalanced classification. IEEE Trans Syst Man Cybern Part B (Cybern). 2009;39(1):281–8.CrossRefGoogle Scholar
Zheng Z, Zheng L, Yang Y. Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. arXiv preprint arXiv:1701.07717, 3, 2017.
Zhong Y, Dutkiewicz E, Yang Y, Zhu X, Zhou Z, Jiang T. Internet of mission-critical things: human and animal classification: a device-free sensing approach. IEEE Int Things J. 2017.Google Scholar
Zhong Y, Yang Y, Zhu X, Dutkiewicz E, Shum KM, Xue Q. An on-chip bandpass filter using a broadside-coupled meander line resonator with a defected-ground structure. IEEE Electron Device Lett. 2017;38(5):626–9.CrossRefGoogle Scholar
Zhong Y, Yang Y, Zhu X, Dutkiewicz E, Zhou Z, Jiang T. Device-free sensing for personnel detection in a foliage environment. IEEE Geosci Remote Sens Lett. 2017;14(6):921–5.CrossRefGoogle Scholar
Zhong Y, Yang Y, Zhu X, Huang Y, Dutkiewicz E, Zhou Z, Jiang T. Impact of seasonal variations on foliage penetration experiment: a WSN-based device-free sensing approach. IEEE Trans Geosci Remote Sens. 2018.Google Scholar
Zhou F, De la Torre F. Spatio-temporal matching for human detection in video. In: European conference on computer vision. Berlin: Springer; 2014. p. 62–77.CrossRefGoogle Scholar