VOE: A new sparsity-based camera network placement framework
In this paper, we propose a stepwise sparsity-based framework for camera network placement. Unlike most previous methods which are developed for specific tasks, our approach is universal and can generalize well for different application scenarios. There are three steps in our approach: visibility an...
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Veröffentlicht in: | Neurocomputing (Amsterdam) 2016-07, Vol.197, p.184-194 |
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description | In this paper, we propose a stepwise sparsity-based framework for camera network placement. Unlike most previous methods which are developed for specific tasks, our approach is universal and can generalize well for different application scenarios. There are three steps in our approach: visibility analysis, optimization and evaluation (VOE), which are employed sequentially and iteratively. First, we use a cascaded visibility filter model to construct a visibility matrix, where each column describes the appearance representation of the surveillance area. Then, we formulate camera network layout as a sparse representation problem, and employ an l1-optimization algorithm to obtain a feasible solution. Our framework is general enough and applicable to various objectives in practical applications. Experiment results are presented to show the effectiveness and efficiency of the proposed framework. |
doi_str_mv | 10.1016/j.neucom.2016.02.065 |
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Experiment results are presented to show the effectiveness and efficiency of the proposed framework.</description><subject>Camera network placement</subject><subject>Cameras</subject><subject>Cascading filter model</subject><subject>Computational efficiency</subject><subject>Mathematical models</subject><subject>Networks</subject><subject>Placement</subject><subject>Sparsity</subject><subject>Stepwise framework</subject><subject>Surveillance</subject><subject>Tasks</subject><subject>Visibility</subject><issn>0925-2312</issn><issn>1872-8286</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp9kFtLAzEQhYMoWKv_wId99GXXSbq5rIhQSr1AoS_qa0iTWUjdm8nW0n_vLuuzMDDMnDMH5iPklkJGgYr7fdbgwbZ1xoYpA5aB4GdkRpVkqWJKnJMZFIynbEHZJbmKcQ9AJWXFjDx-btcPyTJp8JjEzoTo-1O6MxFdYk2NwQxKf2zDV9JVxmKNTZ-UYVDG3TW5KE0V8eavz8nH8_p99Zputi9vq-UmtUypPuXSuBwKLrncLSwYwd1Y0ghRCgPKCcZzCoWyEhylpVTKlYLJnAsQiMViTu6m3C603weMva59tFhVpsH2EDVVVICSUvLBmk9WG9oYA5a6C7424aQp6BGW3usJlh5haWB6gDWcPU1nOLzx4zHoaD02Fp0PaHvtWv9_wC-UnXMB</recordid><startdate>20160712</startdate><enddate>20160712</enddate><creator>Fu, Yi-Ge</creator><creator>Zhou, Jie</creator><general>Elsevier B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20160712</creationdate><title>VOE: A new sparsity-based camera network placement framework</title><author>Fu, Yi-Ge ; Zhou, Jie</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c288t-57ad4095757b3c0a65d65d67a66f6a08d62541098c70d11f788df62745606ee93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Camera network placement</topic><topic>Cameras</topic><topic>Cascading filter model</topic><topic>Computational efficiency</topic><topic>Mathematical models</topic><topic>Networks</topic><topic>Placement</topic><topic>Sparsity</topic><topic>Stepwise framework</topic><topic>Surveillance</topic><topic>Tasks</topic><topic>Visibility</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fu, Yi-Ge</creatorcontrib><creatorcontrib>Zhou, Jie</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science 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><jtitle>Neurocomputing (Amsterdam)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fu, Yi-Ge</au><au>Zhou, Jie</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>VOE: A new sparsity-based camera network placement framework</atitle><jtitle>Neurocomputing (Amsterdam)</jtitle><date>2016-07-12</date><risdate>2016</risdate><volume>197</volume><spage>184</spage><epage>194</epage><pages>184-194</pages><issn>0925-2312</issn><eissn>1872-8286</eissn><abstract>In this paper, we propose a stepwise sparsity-based framework for camera network placement. 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subjects | Camera network placement Cameras Cascading filter model Computational efficiency Mathematical models Networks Placement Sparsity Stepwise framework Surveillance Tasks Visibility |
title | VOE: A new sparsity-based camera network placement framework |
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