Publication:
Human Location Classification for Outdoor Environment

Simple item page
cris.author.scopus-author-id 56995802600
cris.author.scopus-author-id 16175792400
cris.author.scopus-author-id 57192974774
cris.author.scopus-author-id 7004310832
cris.author.scopus-author-id 36560557700
cris.virtual.department Universiti Malaysia Perlis
cris.virtual.department Universiti Malaysia Perlis
cris.virtual.department Universiti Malaysia Perlis
cris.virtualsource.department 320708d5-9ee0-402f-806f-c58a5b23d87a
cris.virtualsource.department 0a657b50-c6c1-403e-ac43-c1c3eed50be2
cris.virtualsource.department db7b85b7-3dd9-408d-9d57-03e40dcbf1c9
dc.contributor.author Talib M.T.M.
dc.contributor.author Mohd Hafiz Fazalul Rahiman
dc.contributor.author Latifah Munirah Kamarudin
dc.contributor.author Nishizaki H.
dc.contributor.author Ammar Zakaria
dc.date.accessioned 2024-12-11T07:31:39Z
dc.date.available 2024-12-11T07:31:39Z
dc.date.issued 2019-12-03
dc.description.abstract Outdoor localisation can offer great capabilities in security and perimeter surveillance applications. The localisation of people become more challenges when involving with the nonlinear environment. GPS and CCTV are two localisation techniques usually use to localise human in an outdoor environment. However, they have weaknesses which result in low localisation accuracy. Therefore, the application of Device-free localisation (DFL), together with the Internet of things (IoT) is more appropriate due to their capability to detect the human body in all environmental conditions, and there is no problem losing signals as faced by GPS. This system offers excellent potential in humans localisation because humans can be detected wirelessly without any tracking device attached. In developing the DFL system, the main concern is the localisation accuracy. Although the existing DFL system gives significant result to the localisation, the accuracy is still low due to the large variation in RSSI values. Hence, a Radio Tomographic Imaging-based ANN classification (RTI-ANN) approach is proposed to increase the localisation accuracy. This Artificial Neural Network (ANN) is designed to learn the Radio Tomography imaging (RTI) input for classification purpose. Even though the RTI gives a good result to the localisation, however, it suffers from smearing effect. To eliminates this smearing area and background noise, pre-processing of the RTI image is required. Thus, extracting the valuable information technique from the RTI image has been proposed. By extracting the valuable information data from the RTI image, about 61% to 66% of the smearing noise is removed depending on the size of the RTI image. Only data directly associated with human attenuation used for training and learning of ANN. The experimental results show ANN system can localise human in the right zone for a given dataset.
dc.identifier.doi 10.1088/1757-899X/705/1/012047
dc.identifier.scopus 2-s2.0-85078295197
dc.identifier.uri https://hdl.handle.net/20.500.14170/10014
dc.relation.funding Japan Science and Technology Agency
dc.relation.grantno undefined
dc.relation.ispartof IOP Conference Series: Materials Science and Engineering
dc.relation.ispartofseries IOP Conference Series: Materials Science and Engineering
dc.relation.issn 17578981
dc.rights open access
dc.title Human Location Classification for Outdoor Environment
dc.type Conference Proceeding
dspace.entity.type Publication
oaire.citation.issue 1
oaire.citation.volume 705
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit University of Yamanashi
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.citation.number 012047
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.scopus-author-id 56995802600
person.identifier.scopus-author-id 16175792400
person.identifier.scopus-author-id 57192974774
person.identifier.scopus-author-id 7004310832
person.identifier.scopus-author-id 36560557700
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Research repository notification.pdf
Size:
4.4 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Publications 2019