Waterfront surveillance and trackability
This paper presents a method for waterfront surveillance system. Unlike traditional approaches that model dynamic water background explicitly, we choose a relaxed background model to extract multiple object hypotheses. The hypotheses are then tracked with probablistic framework. Finally, the hypothe...
Gespeichert in:
| Veröffentlicht in: | Machine vision and applications 2008-10, Vol.19 (5-6), p.291-300 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 300 |
|---|---|
| container_issue | 5-6 |
| container_start_page | 291 |
| container_title | Machine vision and applications |
| container_volume | 19 |
| creator | Li, Yi Hua, Wei Guo, Chengen Gu, Haisong Kang, Jinman Chen, Xiangrong |
| description | This paper presents a method for waterfront surveillance system. Unlike traditional approaches that model dynamic water background explicitly, we choose a relaxed background model to extract multiple object hypotheses. The hypotheses are then tracked with probablistic framework. Finally, the hypotheses are classified as positive objects or negative objects based on their
trackability
. Trackability is described by the stableness and the consistency of their trajectories and their appearances, and the properties of their accumulated templates. |
| doi_str_mv | 10.1007/s00138-008-0157-8 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_35604197</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>35604197</sourcerecordid><originalsourceid>FETCH-LOGICAL-c347t-18ffaca625dd108940c2dabb9030b01fb10208572710385721a547ab7d5f15843</originalsourceid><addsrcrecordid>eNp1kEtLxDAUhYMoOI7-AHcFQdxU782jSZYy-IIBN4rLkLaJdOy0Y9IK8-9NqSAILi7nLr5zOBxCzhGuEUDeRABkKgdIh0Lm6oAskDOaoyz0IVmATr8CTY_JSYwbAOBS8gW5erODCz703ZDFMXy5pm1tV7nMdnU2BFt92LJpm2F_So68baM7-9Eleb2_e1k95uvnh6fV7TqvGJdDjsp7W9mCirpGUJpDRWtblhoYlIC-RKCghKQSgU2KVnBpS1kLj0JxtiSXc-4u9J-ji4PZNrFyUyvXj9EwUQBHLRN48Qfc9GPoUjdDi4JrwSjVicKZqkIfY3De7EKztWFvEMy0nJmXM2k5My1nVPLQ2RMT27278Jv8v-kbcQZucw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2664953229</pqid></control><display><type>article</type><title>Waterfront surveillance and trackability</title><source>SpringerNature Journals</source><creator>Li, Yi ; Hua, Wei ; Guo, Chengen ; Gu, Haisong ; Kang, Jinman ; Chen, Xiangrong</creator><creatorcontrib>Li, Yi ; Hua, Wei ; Guo, Chengen ; Gu, Haisong ; Kang, Jinman ; Chen, Xiangrong</creatorcontrib><description>This paper presents a method for waterfront surveillance system. Unlike traditional approaches that model dynamic water background explicitly, we choose a relaxed background model to extract multiple object hypotheses. The hypotheses are then tracked with probablistic framework. Finally, the hypotheses are classified as positive objects or negative objects based on their
trackability
. Trackability is described by the stableness and the consistency of their trajectories and their appearances, and the properties of their accumulated templates.</description><identifier>ISSN: 0932-8092</identifier><identifier>EISSN: 1432-1769</identifier><identifier>DOI: 10.1007/s00138-008-0157-8</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer-Verlag</publisher><subject>Communications Engineering ; Computer Science ; Hypotheses ; Image Processing and Computer Vision ; Networks ; Pattern Recognition ; Special Issue Paper ; Surveillance ; Surveillance systems ; Vision systems ; Waterfronts</subject><ispartof>Machine vision and applications, 2008-10, Vol.19 (5-6), p.291-300</ispartof><rights>Springer-Verlag 2008</rights><rights>Springer-Verlag 2008.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c347t-18ffaca625dd108940c2dabb9030b01fb10208572710385721a547ab7d5f15843</citedby><cites>FETCH-LOGICAL-c347t-18ffaca625dd108940c2dabb9030b01fb10208572710385721a547ab7d5f15843</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00138-008-0157-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00138-008-0157-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27928,27929,41492,42561,51323</link.rule.ids></links><search><creatorcontrib>Li, Yi</creatorcontrib><creatorcontrib>Hua, Wei</creatorcontrib><creatorcontrib>Guo, Chengen</creatorcontrib><creatorcontrib>Gu, Haisong</creatorcontrib><creatorcontrib>Kang, Jinman</creatorcontrib><creatorcontrib>Chen, Xiangrong</creatorcontrib><title>Waterfront surveillance and trackability</title><title>Machine vision and applications</title><addtitle>Machine Vision and Applications</addtitle><description>This paper presents a method for waterfront surveillance system. Unlike traditional approaches that model dynamic water background explicitly, we choose a relaxed background model to extract multiple object hypotheses. The hypotheses are then tracked with probablistic framework. Finally, the hypotheses are classified as positive objects or negative objects based on their
trackability
. Trackability is described by the stableness and the consistency of their trajectories and their appearances, and the properties of their accumulated templates.</description><subject>Communications Engineering</subject><subject>Computer Science</subject><subject>Hypotheses</subject><subject>Image Processing and Computer Vision</subject><subject>Networks</subject><subject>Pattern Recognition</subject><subject>Special Issue Paper</subject><subject>Surveillance</subject><subject>Surveillance systems</subject><subject>Vision systems</subject><subject>Waterfronts</subject><issn>0932-8092</issn><issn>1432-1769</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp1kEtLxDAUhYMoOI7-AHcFQdxU782jSZYy-IIBN4rLkLaJdOy0Y9IK8-9NqSAILi7nLr5zOBxCzhGuEUDeRABkKgdIh0Lm6oAskDOaoyz0IVmATr8CTY_JSYwbAOBS8gW5erODCz703ZDFMXy5pm1tV7nMdnU2BFt92LJpm2F_So68baM7-9Eleb2_e1k95uvnh6fV7TqvGJdDjsp7W9mCirpGUJpDRWtblhoYlIC-RKCghKQSgU2KVnBpS1kLj0JxtiSXc-4u9J-ji4PZNrFyUyvXj9EwUQBHLRN48Qfc9GPoUjdDi4JrwSjVicKZqkIfY3De7EKztWFvEMy0nJmXM2k5My1nVPLQ2RMT27278Jv8v-kbcQZucw</recordid><startdate>20081001</startdate><enddate>20081001</enddate><creator>Li, Yi</creator><creator>Hua, Wei</creator><creator>Guo, Chengen</creator><creator>Gu, Haisong</creator><creator>Kang, Jinman</creator><creator>Chen, Xiangrong</creator><general>Springer-Verlag</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20081001</creationdate><title>Waterfront surveillance and trackability</title><author>Li, Yi ; Hua, Wei ; Guo, Chengen ; Gu, Haisong ; Kang, Jinman ; Chen, Xiangrong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c347t-18ffaca625dd108940c2dabb9030b01fb10208572710385721a547ab7d5f15843</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Communications Engineering</topic><topic>Computer Science</topic><topic>Hypotheses</topic><topic>Image Processing and Computer Vision</topic><topic>Networks</topic><topic>Pattern Recognition</topic><topic>Special Issue Paper</topic><topic>Surveillance</topic><topic>Surveillance systems</topic><topic>Vision systems</topic><topic>Waterfronts</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Yi</creatorcontrib><creatorcontrib>Hua, Wei</creatorcontrib><creatorcontrib>Guo, Chengen</creatorcontrib><creatorcontrib>Gu, Haisong</creatorcontrib><creatorcontrib>Kang, Jinman</creatorcontrib><creatorcontrib>Chen, Xiangrong</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</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>Machine vision and applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Yi</au><au>Hua, Wei</au><au>Guo, Chengen</au><au>Gu, Haisong</au><au>Kang, Jinman</au><au>Chen, Xiangrong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Waterfront surveillance and trackability</atitle><jtitle>Machine vision and applications</jtitle><stitle>Machine Vision and Applications</stitle><date>2008-10-01</date><risdate>2008</risdate><volume>19</volume><issue>5-6</issue><spage>291</spage><epage>300</epage><pages>291-300</pages><issn>0932-8092</issn><eissn>1432-1769</eissn><abstract>This paper presents a method for waterfront surveillance system. Unlike traditional approaches that model dynamic water background explicitly, we choose a relaxed background model to extract multiple object hypotheses. The hypotheses are then tracked with probablistic framework. Finally, the hypotheses are classified as positive objects or negative objects based on their
trackability
. Trackability is described by the stableness and the consistency of their trajectories and their appearances, and the properties of their accumulated templates.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer-Verlag</pub><doi>10.1007/s00138-008-0157-8</doi><tpages>10</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0932-8092 |
| ispartof | Machine vision and applications, 2008-10, Vol.19 (5-6), p.291-300 |
| issn | 0932-8092 1432-1769 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_35604197 |
| source | SpringerNature Journals |
| subjects | Communications Engineering Computer Science Hypotheses Image Processing and Computer Vision Networks Pattern Recognition Special Issue Paper Surveillance Surveillance systems Vision systems Waterfronts |
| title | Waterfront surveillance and trackability |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-17T12%3A57%3A58IST&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=Waterfront%20surveillance%20and%20trackability&rft.jtitle=Machine%20vision%20and%20applications&rft.au=Li,%20Yi&rft.date=2008-10-01&rft.volume=19&rft.issue=5-6&rft.spage=291&rft.epage=300&rft.pages=291-300&rft.issn=0932-8092&rft.eissn=1432-1769&rft_id=info:doi/10.1007/s00138-008-0157-8&rft_dat=%3Cproquest_cross%3E35604197%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2664953229&rft_id=info:pmid/&rfr_iscdi=true |