Classification of animals and people based on radio-sensor network

Zhong, Y; Zhou, Z; Jiang, T; Heimlich, M; Dutkiewicz, E; Fang, G

Classification of animals and people based on radio-sensor network

Zhong, Y Zhou, Z Jiang, T Heimlich, M Dutkiewicz, E

Fang, G

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Publication Type:: Conference Proceeding
Citation:: 2016 16th International Symposium on Communications and Information Technologies, ISCIT 2016, 2016, pp. 113 - 116
Issue Date:: 2016-11-21

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Field	Value	Language
dc.contributor.author	Zhong, Y	en_US
dc.contributor.author	Zhou, Z	en_US
dc.contributor.author	Jiang, T	en_US
dc.contributor.author	Heimlich, M	en_US
dc.contributor.author	Dutkiewicz, E https://orcid.org/0000-0002-4268-9286	en_US
dc.contributor.author	Fang, G	en_US
dc.date.issued	2016-11-21	en_US
dc.identifier.citation	2016 16th International Symposium on Communications and Information Technologies, ISCIT 2016, 2016, pp. 113 - 116	en_US
dc.identifier.isbn	9781509040995	en_US
dc.identifier.uri	http://hdl.handle.net/10453/98939
dc.description.abstract	© 2016 IEEE. Personnel detection embedded in foliage is extremely important to border patrol, perimeter protection and search-and-rescue operations. In this paper, we explore the utility of radio-sensor network (RSN) to distinguish between humans and animals. We explore the phenomenon that signals are always affected by the presence of obstacles and identify human based on the received signals by transceivers, which leads to a potential low-cost way for personnel detection without specific sensors. In our study, the impulse radio ultra-wideband (IR-UWB) technology is selected for the RF transceiver due to the fact that it is not only energy efficient, but also robust against interferences. The principle component analysis (PCA) is applied to extract the feature vector, and a support vector machine is used as the target classifier. Experiment result with an average accuracy of 97.5% based on actual data collected in a cornfield indicates that this approach has a good capability to distinguish between human and animals in a foliage environment.	en_US
dc.relation.ispartof	2016 16th International Symposium on Communications and Information Technologies, ISCIT 2016	en_US
dc.relation.isbasedon	10.1109/ISCIT.2016.7751603	en_US
dc.title	Classification of animals and people based on radio-sensor network	en_US
dc.type	Conference Proceeding
utslib.for	0806 Information Systems	en_US
utslib.for	0906 Electrical and Electronic Engineering	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. Personnel detection embedded in foliage is extremely important to border patrol, perimeter protection and search-and-rescue operations. In this paper, we explore the utility of radio-sensor network (RSN) to distinguish between humans and animals. We explore the phenomenon that signals are always affected by the presence of obstacles and identify human based on the received signals by transceivers, which leads to a potential low-cost way for personnel detection without specific sensors. In our study, the impulse radio ultra-wideband (IR-UWB) technology is selected for the RF transceiver due to the fact that it is not only energy efficient, but also robust against interferences. The principle component analysis (PCA) is applied to extract the feature vector, and a support vector machine is used as the target classifier. Experiment result with an average accuracy of 97.5% based on actual data collected in a cornfield indicates that this approach has a good capability to distinguish between human and animals in a foliage environment.

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http://hdl.handle.net/10453/98939