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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...
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Published in: | Journal of intelligent manufacturing 2016-12, Vol.27 (6), p.1221-1235 |
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container_issue | 6 |
container_start_page | 1221 |
container_title | Journal of intelligent manufacturing |
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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 |
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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. 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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> |
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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 |
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