Loading…

Target coverage in camera networks for manufacturing workplaces

In this paper, we investigate the camera network placement problem for target coverage in manufacturing workplaces. The problem is formulated to find the minimum number of cameras of different types and their best configurations to maximise the coverage of the monitored workplace such that the given...

Full description

Saved in:
Bibliographic Details
Published in:Journal of intelligent manufacturing 2016-12, Vol.27 (6), p.1221-1235
Main Authors: Hanoun, Samer, Bhatti, Asim, Creighton, Doug, Nahavandi, Saeid, Crothers, Phillip, Esparza, Celeste Gloria
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c402t-94b7eddafa90710976c198902fd318486494f125504402a920312051a3f4933f3
cites cdi_FETCH-LOGICAL-c402t-94b7eddafa90710976c198902fd318486494f125504402a920312051a3f4933f3
container_end_page 1235
container_issue 6
container_start_page 1221
container_title Journal of intelligent manufacturing
container_volume 27
creator Hanoun, Samer
Bhatti, Asim
Creighton, Doug
Nahavandi, Saeid
Crothers, Phillip
Esparza, Celeste Gloria
description In this paper, we investigate the camera network placement problem for target coverage in manufacturing workplaces. The problem is formulated to find the minimum number of cameras of different types and their best configurations to maximise the coverage of the monitored workplace such that the given set of target points of interest are each k -covered with a predefined minimum spatial resolution. Since the problem is NP -complete, and even NP -hard to approximate, a novel method based on Simulated Annealing is presented to solve the optimisation problem. A new neighbourhood generation function is proposed to handle the discrete nature of the problem. The visual coverage is modelled using realistic and coherent assumptions of camera intrinsic and extrinsic parameters making it suitable for many real world camera based applications. Task-specific quality of coverage measure is proposed to assist selecting the best among the set of camera network placements with equal coverage. A 3D CAD of the monitored space is used to examine physical occlusions of target points. The results show the accuracy, efficiency and scalability of the presented solution method; which can be applied effectively in the design of practical camera networks.
doi_str_mv 10.1007/s10845-014-0946-z
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1855364125</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>4227345131</sourcerecordid><originalsourceid>FETCH-LOGICAL-c402t-94b7eddafa90710976c198902fd318486494f125504402a920312051a3f4933f3</originalsourceid><addsrcrecordid>eNp1kE1LAzEQhoMoWKs_wNuCFy_RmXzsJieR4hcUvNRziNukbN0vk13F_nqz1IMInmYYnvdleAg5R7hCgOI6IighKaCgoEVOdwdkhrJgVKGQh2QGWuZUSpTH5CTGLQBoleOM3Kxs2LghK7sPF-zGZVWblbZJe9a64bMLbzHzXcga247elsMYqnaTTfe-tqWLp-TI2zq6s585Jy_3d6vFI10-Pzwtbpe0FMAGqsVr4dZr662GAkEXeYlaaWB-zVEJlQstPDIpQSTeagYcGUi03AvNuedzcrnv7UP3Pro4mKaKpatr27pujAaVlDwXqSKhF3_QbTeGNn2XKM6QK5VPFO6pMnQxBudNH6rGhi-DYCalZq_UJKVmUmp2KcP2mdhPGlz41fxv6BuEBnd1</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1832138865</pqid></control><display><type>article</type><title>Target coverage in camera networks for manufacturing workplaces</title><source>EBSCOhost Business Source Ultimate</source><source>ABI/INFORM Global</source><source>Springer Nature</source><creator>Hanoun, Samer ; Bhatti, Asim ; Creighton, Doug ; Nahavandi, Saeid ; Crothers, Phillip ; Esparza, Celeste Gloria</creator><creatorcontrib>Hanoun, Samer ; Bhatti, Asim ; Creighton, Doug ; Nahavandi, Saeid ; Crothers, Phillip ; Esparza, Celeste Gloria</creatorcontrib><description>In this paper, we investigate the camera network placement problem for target coverage in manufacturing workplaces. The problem is formulated to find the minimum number of cameras of different types and their best configurations to maximise the coverage of the monitored workplace such that the given set of target points of interest are each k -covered with a predefined minimum spatial resolution. Since the problem is NP -complete, and even NP -hard to approximate, a novel method based on Simulated Annealing is presented to solve the optimisation problem. A new neighbourhood generation function is proposed to handle the discrete nature of the problem. The visual coverage is modelled using realistic and coherent assumptions of camera intrinsic and extrinsic parameters making it suitable for many real world camera based applications. Task-specific quality of coverage measure is proposed to assist selecting the best among the set of camera network placements with equal coverage. A 3D CAD of the monitored space is used to examine physical occlusions of target points. The results show the accuracy, efficiency and scalability of the presented solution method; which can be applied effectively in the design of practical camera networks.</description><identifier>ISSN: 0956-5515</identifier><identifier>EISSN: 1572-8145</identifier><identifier>DOI: 10.1007/s10845-014-0946-z</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Approximation ; Business and Management ; Cameras ; Control ; Cultural heritage ; Intelligent systems ; Local area networks ; Machines ; Manufacturing ; Manufacturing execution systems ; Mathematical analysis ; Mathematical models ; Mechatronics ; Monitoring ; Networks ; Optimization ; Placement ; Processes ; Production ; Robotics ; Robots ; Sensors ; Simulated annealing ; Studies ; Workplaces</subject><ispartof>Journal of intelligent manufacturing, 2016-12, Vol.27 (6), p.1221-1235</ispartof><rights>Springer Science+Business Media New York 2014</rights><rights>Springer Science+Business Media New York 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c402t-94b7eddafa90710976c198902fd318486494f125504402a920312051a3f4933f3</citedby><cites>FETCH-LOGICAL-c402t-94b7eddafa90710976c198902fd318486494f125504402a920312051a3f4933f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1832138865/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1832138865?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,11688,27924,27925,36060,36061,44363,74895</link.rule.ids></links><search><creatorcontrib>Hanoun, Samer</creatorcontrib><creatorcontrib>Bhatti, Asim</creatorcontrib><creatorcontrib>Creighton, Doug</creatorcontrib><creatorcontrib>Nahavandi, Saeid</creatorcontrib><creatorcontrib>Crothers, Phillip</creatorcontrib><creatorcontrib>Esparza, Celeste Gloria</creatorcontrib><title>Target coverage in camera networks for manufacturing workplaces</title><title>Journal of intelligent manufacturing</title><addtitle>J Intell Manuf</addtitle><description>In this paper, we investigate the camera network placement problem for target coverage in manufacturing workplaces. The problem is formulated to find the minimum number of cameras of different types and their best configurations to maximise the coverage of the monitored workplace such that the given set of target points of interest are each k -covered with a predefined minimum spatial resolution. Since the problem is NP -complete, and even NP -hard to approximate, a novel method based on Simulated Annealing is presented to solve the optimisation problem. A new neighbourhood generation function is proposed to handle the discrete nature of the problem. The visual coverage is modelled using realistic and coherent assumptions of camera intrinsic and extrinsic parameters making it suitable for many real world camera based applications. Task-specific quality of coverage measure is proposed to assist selecting the best among the set of camera network placements with equal coverage. A 3D CAD of the monitored space is used to examine physical occlusions of target points. The results show the accuracy, efficiency and scalability of the presented solution method; which can be applied effectively in the design of practical camera networks.</description><subject>Algorithms</subject><subject>Approximation</subject><subject>Business and Management</subject><subject>Cameras</subject><subject>Control</subject><subject>Cultural heritage</subject><subject>Intelligent systems</subject><subject>Local area networks</subject><subject>Machines</subject><subject>Manufacturing</subject><subject>Manufacturing execution systems</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Mechatronics</subject><subject>Monitoring</subject><subject>Networks</subject><subject>Optimization</subject><subject>Placement</subject><subject>Processes</subject><subject>Production</subject><subject>Robotics</subject><subject>Robots</subject><subject>Sensors</subject><subject>Simulated annealing</subject><subject>Studies</subject><subject>Workplaces</subject><issn>0956-5515</issn><issn>1572-8145</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>M0C</sourceid><recordid>eNp1kE1LAzEQhoMoWKs_wNuCFy_RmXzsJieR4hcUvNRziNukbN0vk13F_nqz1IMInmYYnvdleAg5R7hCgOI6IighKaCgoEVOdwdkhrJgVKGQh2QGWuZUSpTH5CTGLQBoleOM3Kxs2LghK7sPF-zGZVWblbZJe9a64bMLbzHzXcga247elsMYqnaTTfe-tqWLp-TI2zq6s585Jy_3d6vFI10-Pzwtbpe0FMAGqsVr4dZr662GAkEXeYlaaWB-zVEJlQstPDIpQSTeagYcGUi03AvNuedzcrnv7UP3Pro4mKaKpatr27pujAaVlDwXqSKhF3_QbTeGNn2XKM6QK5VPFO6pMnQxBudNH6rGhi-DYCalZq_UJKVmUmp2KcP2mdhPGlz41fxv6BuEBnd1</recordid><startdate>20161201</startdate><enddate>20161201</enddate><creator>Hanoun, Samer</creator><creator>Bhatti, Asim</creator><creator>Creighton, Doug</creator><creator>Nahavandi, Saeid</creator><creator>Crothers, Phillip</creator><creator>Esparza, Celeste Gloria</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7TB</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>88E</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>K9.</scope><scope>L.-</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M0S</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>Q9U</scope></search><sort><creationdate>20161201</creationdate><title>Target coverage in camera networks for manufacturing workplaces</title><author>Hanoun, Samer ; Bhatti, Asim ; Creighton, Doug ; Nahavandi, Saeid ; Crothers, Phillip ; Esparza, Celeste Gloria</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c402t-94b7eddafa90710976c198902fd318486494f125504402a920312051a3f4933f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Approximation</topic><topic>Business and Management</topic><topic>Cameras</topic><topic>Control</topic><topic>Cultural heritage</topic><topic>Intelligent systems</topic><topic>Local area networks</topic><topic>Machines</topic><topic>Manufacturing</topic><topic>Manufacturing execution systems</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Mechatronics</topic><topic>Monitoring</topic><topic>Networks</topic><topic>Optimization</topic><topic>Placement</topic><topic>Processes</topic><topic>Production</topic><topic>Robotics</topic><topic>Robots</topic><topic>Sensors</topic><topic>Simulated annealing</topic><topic>Studies</topic><topic>Workplaces</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hanoun, Samer</creatorcontrib><creatorcontrib>Bhatti, Asim</creatorcontrib><creatorcontrib>Creighton, Doug</creatorcontrib><creatorcontrib>Nahavandi, Saeid</creatorcontrib><creatorcontrib>Crothers, Phillip</creatorcontrib><creatorcontrib>Esparza, Celeste Gloria</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Engineering Database</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Journal of intelligent manufacturing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hanoun, Samer</au><au>Bhatti, Asim</au><au>Creighton, Doug</au><au>Nahavandi, Saeid</au><au>Crothers, Phillip</au><au>Esparza, Celeste Gloria</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Target coverage in camera networks for manufacturing workplaces</atitle><jtitle>Journal of intelligent manufacturing</jtitle><stitle>J Intell Manuf</stitle><date>2016-12-01</date><risdate>2016</risdate><volume>27</volume><issue>6</issue><spage>1221</spage><epage>1235</epage><pages>1221-1235</pages><issn>0956-5515</issn><eissn>1572-8145</eissn><abstract>In this paper, we investigate the camera network placement problem for target coverage in manufacturing workplaces. The problem is formulated to find the minimum number of cameras of different types and their best configurations to maximise the coverage of the monitored workplace such that the given set of target points of interest are each k -covered with a predefined minimum spatial resolution. Since the problem is NP -complete, and even NP -hard to approximate, a novel method based on Simulated Annealing is presented to solve the optimisation problem. A new neighbourhood generation function is proposed to handle the discrete nature of the problem. The visual coverage is modelled using realistic and coherent assumptions of camera intrinsic and extrinsic parameters making it suitable for many real world camera based applications. Task-specific quality of coverage measure is proposed to assist selecting the best among the set of camera network placements with equal coverage. A 3D CAD of the monitored space is used to examine physical occlusions of target points. The results show the accuracy, efficiency and scalability of the presented solution method; which can be applied effectively in the design of practical camera networks.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10845-014-0946-z</doi><tpages>15</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0956-5515
ispartof Journal of intelligent manufacturing, 2016-12, Vol.27 (6), p.1221-1235
issn 0956-5515
1572-8145
language eng
recordid cdi_proquest_miscellaneous_1855364125
source EBSCOhost Business Source Ultimate; ABI/INFORM Global; Springer Nature
subjects Algorithms
Approximation
Business and Management
Cameras
Control
Cultural heritage
Intelligent systems
Local area networks
Machines
Manufacturing
Manufacturing execution systems
Mathematical analysis
Mathematical models
Mechatronics
Monitoring
Networks
Optimization
Placement
Processes
Production
Robotics
Robots
Sensors
Simulated annealing
Studies
Workplaces
title Target coverage in camera networks for manufacturing workplaces
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-26T17%3A49%3A53IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Target%20coverage%20in%20camera%20networks%20for%20manufacturing%20workplaces&rft.jtitle=Journal%20of%20intelligent%20manufacturing&rft.au=Hanoun,%20Samer&rft.date=2016-12-01&rft.volume=27&rft.issue=6&rft.spage=1221&rft.epage=1235&rft.pages=1221-1235&rft.issn=0956-5515&rft.eissn=1572-8145&rft_id=info:doi/10.1007/s10845-014-0946-z&rft_dat=%3Cproquest_cross%3E4227345131%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c402t-94b7eddafa90710976c198902fd318486494f125504402a920312051a3f4933f3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1832138865&rft_id=info:pmid/&rfr_iscdi=true