Robust Contour Tracking by Combining Region and Boundary Information
This paper presents a new object tracking model that systematically combines region and boundary features. Besides traditional region features (intensity/color and texture), we design a new boundary-based object detector for accurate and robust tracking in low-contrast and complex scenes, which usua...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on circuits and systems for video technology 2011-12, Vol.21 (12), p.1784-1794 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1794 |
|---|---|
| container_issue | 12 |
| container_start_page | 1784 |
| container_title | IEEE transactions on circuits and systems for video technology |
| container_volume | 21 |
| creator | Ling Cai Lei He Yamashita, T. Yiren Xu Yuming Zhao Xin Yang |
| description | This paper presents a new object tracking model that systematically combines region and boundary features. Besides traditional region features (intensity/color and texture), we design a new boundary-based object detector for accurate and robust tracking in low-contrast and complex scenes, which usually appear in the commonly used monochrome surveillance systems. In our model, region feature-based energy terms are characterized by probability models, and boundary feature terms include edge and frame difference. With a new weighting term, a novel energy functional is proposed to systematically combine the region and boundary-based components, and it is minimized by a level set evolution equation. For an efficient computational cost, motion information is utilized for new frame level set initialization. Compared with region feature-based models, the experimental results show that the proposed model significantly improves the performance under different circumstances, especially for objects in low-contrast and complex environments. |
| doi_str_mv | 10.1109/TCSVT.2011.2133550 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_crossref_primary_10_1109_TCSVT_2011_2133550</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5739509</ieee_id><sourcerecordid>1010895298</sourcerecordid><originalsourceid>FETCH-LOGICAL-c357t-f9870d34622eb408e84a4635ebdb7db2f52098ac12b48bed72633f1d7e6cd8343</originalsourceid><addsrcrecordid>eNpdkMtOwzAQRSMEEqXwA7CJkJDYpIxfib2E8KpUCakEtpGdOFVKYhc7WfTvcWnFgtW8zlzN3Ci6RDBDCMRdkb9_FjMMCM0wIoQxOIomiDGeYAzsOOTAUMIxYqfRmfdrAEQ5zSbR49Kq0Q9xbs1gRxcXTlZfrVnFaht6vWrNrljqVWtNLE0dP9jR1NJt47lprOvlEAbn0UkjO68vDnEafTw_Fflrsnh7mef3i6QiLBuSRvAMakJTjLWiwDWnkqaEaVWrrFa4YRgElxXCinKl6wynhDSoznRa1ZxQMo1u97obZ79H7Yeyb32lu04abUdfIkDABcOCB_T6H7oO75lwXSkQJcAIwQHCe6hy1nunm3Lj2j48F5TKna_lr6_lztfy4GtYujkoS1_JrnHSVK3_28SMQgZEBO5qz7Va678xy4hgIMgPi4J_1w</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>914305332</pqid></control><display><type>article</type><title>Robust Contour Tracking by Combining Region and Boundary Information</title><source>IEEE Xplore (Online service)</source><creator>Ling Cai ; Lei He ; Yamashita, T. ; Yiren Xu ; Yuming Zhao ; Xin Yang</creator><creatorcontrib>Ling Cai ; Lei He ; Yamashita, T. ; Yiren Xu ; Yuming Zhao ; Xin Yang</creatorcontrib><description>This paper presents a new object tracking model that systematically combines region and boundary features. Besides traditional region features (intensity/color and texture), we design a new boundary-based object detector for accurate and robust tracking in low-contrast and complex scenes, which usually appear in the commonly used monochrome surveillance systems. In our model, region feature-based energy terms are characterized by probability models, and boundary feature terms include edge and frame difference. With a new weighting term, a novel energy functional is proposed to systematically combine the region and boundary-based components, and it is minimized by a level set evolution equation. For an efficient computational cost, motion information is utilized for new frame level set initialization. Compared with region feature-based models, the experimental results show that the proposed model significantly improves the performance under different circumstances, especially for objects in low-contrast and complex environments.</description><identifier>ISSN: 1051-8215</identifier><identifier>EISSN: 1558-2205</identifier><identifier>DOI: 10.1109/TCSVT.2011.2133550</identifier><identifier>CODEN: ITCTEM</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Applied sciences ; Bayesian methods ; Bayesian model ; Boundaries ; Computational efficiency ; contour evolution ; Detection, estimation, filtering, equalization, prediction ; Detectors ; energy functional ; Exact sciences and technology ; Feature extraction ; feature fusion ; Frames ; General equipment and techniques ; Image color analysis ; Information, signal and communications theory ; Instruments, apparatus, components and techniques common to several branches of physics and astronomy ; kernel density estimation ; Level set ; Pattern recognition ; Physics ; Robustness ; Sensors (chemical, optical, electrical, movement, gas, etc.); remote sensing ; Signal and communications theory ; Signal processing ; Signal, noise ; Studies ; Surface layer ; Surveillance systems ; Telecommunications and information theory ; Texture ; Tracking</subject><ispartof>IEEE transactions on circuits and systems for video technology, 2011-12, Vol.21 (12), p.1784-1794</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Dec 2011</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c357t-f9870d34622eb408e84a4635ebdb7db2f52098ac12b48bed72633f1d7e6cd8343</citedby><cites>FETCH-LOGICAL-c357t-f9870d34622eb408e84a4635ebdb7db2f52098ac12b48bed72633f1d7e6cd8343</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5739509$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5739509$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=25407039$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Ling Cai</creatorcontrib><creatorcontrib>Lei He</creatorcontrib><creatorcontrib>Yamashita, T.</creatorcontrib><creatorcontrib>Yiren Xu</creatorcontrib><creatorcontrib>Yuming Zhao</creatorcontrib><creatorcontrib>Xin Yang</creatorcontrib><title>Robust Contour Tracking by Combining Region and Boundary Information</title><title>IEEE transactions on circuits and systems for video technology</title><addtitle>TCSVT</addtitle><description>This paper presents a new object tracking model that systematically combines region and boundary features. Besides traditional region features (intensity/color and texture), we design a new boundary-based object detector for accurate and robust tracking in low-contrast and complex scenes, which usually appear in the commonly used monochrome surveillance systems. In our model, region feature-based energy terms are characterized by probability models, and boundary feature terms include edge and frame difference. With a new weighting term, a novel energy functional is proposed to systematically combine the region and boundary-based components, and it is minimized by a level set evolution equation. For an efficient computational cost, motion information is utilized for new frame level set initialization. Compared with region feature-based models, the experimental results show that the proposed model significantly improves the performance under different circumstances, especially for objects in low-contrast and complex environments.</description><subject>Applied sciences</subject><subject>Bayesian methods</subject><subject>Bayesian model</subject><subject>Boundaries</subject><subject>Computational efficiency</subject><subject>contour evolution</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Detectors</subject><subject>energy functional</subject><subject>Exact sciences and technology</subject><subject>Feature extraction</subject><subject>feature fusion</subject><subject>Frames</subject><subject>General equipment and techniques</subject><subject>Image color analysis</subject><subject>Information, signal and communications theory</subject><subject>Instruments, apparatus, components and techniques common to several branches of physics and astronomy</subject><subject>kernel density estimation</subject><subject>Level set</subject><subject>Pattern recognition</subject><subject>Physics</subject><subject>Robustness</subject><subject>Sensors (chemical, optical, electrical, movement, gas, etc.); remote sensing</subject><subject>Signal and communications theory</subject><subject>Signal processing</subject><subject>Signal, noise</subject><subject>Studies</subject><subject>Surface layer</subject><subject>Surveillance systems</subject><subject>Telecommunications and information theory</subject><subject>Texture</subject><subject>Tracking</subject><issn>1051-8215</issn><issn>1558-2205</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkMtOwzAQRSMEEqXwA7CJkJDYpIxfib2E8KpUCakEtpGdOFVKYhc7WfTvcWnFgtW8zlzN3Ci6RDBDCMRdkb9_FjMMCM0wIoQxOIomiDGeYAzsOOTAUMIxYqfRmfdrAEQ5zSbR49Kq0Q9xbs1gRxcXTlZfrVnFaht6vWrNrljqVWtNLE0dP9jR1NJt47lprOvlEAbn0UkjO68vDnEafTw_Fflrsnh7mef3i6QiLBuSRvAMakJTjLWiwDWnkqaEaVWrrFa4YRgElxXCinKl6wynhDSoznRa1ZxQMo1u97obZ79H7Yeyb32lu04abUdfIkDABcOCB_T6H7oO75lwXSkQJcAIwQHCe6hy1nunm3Lj2j48F5TKna_lr6_lztfy4GtYujkoS1_JrnHSVK3_28SMQgZEBO5qz7Va678xy4hgIMgPi4J_1w</recordid><startdate>20111201</startdate><enddate>20111201</enddate><creator>Ling Cai</creator><creator>Lei He</creator><creator>Yamashita, T.</creator><creator>Yiren Xu</creator><creator>Yuming Zhao</creator><creator>Xin Yang</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>20111201</creationdate><title>Robust Contour Tracking by Combining Region and Boundary Information</title><author>Ling Cai ; Lei He ; Yamashita, T. ; Yiren Xu ; Yuming Zhao ; Xin Yang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c357t-f9870d34622eb408e84a4635ebdb7db2f52098ac12b48bed72633f1d7e6cd8343</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Applied sciences</topic><topic>Bayesian methods</topic><topic>Bayesian model</topic><topic>Boundaries</topic><topic>Computational efficiency</topic><topic>contour evolution</topic><topic>Detection, estimation, filtering, equalization, prediction</topic><topic>Detectors</topic><topic>energy functional</topic><topic>Exact sciences and technology</topic><topic>Feature extraction</topic><topic>feature fusion</topic><topic>Frames</topic><topic>General equipment and techniques</topic><topic>Image color analysis</topic><topic>Information, signal and communications theory</topic><topic>Instruments, apparatus, components and techniques common to several branches of physics and astronomy</topic><topic>kernel density estimation</topic><topic>Level set</topic><topic>Pattern recognition</topic><topic>Physics</topic><topic>Robustness</topic><topic>Sensors (chemical, optical, electrical, movement, gas, etc.); remote sensing</topic><topic>Signal and communications theory</topic><topic>Signal processing</topic><topic>Signal, noise</topic><topic>Studies</topic><topic>Surface layer</topic><topic>Surveillance systems</topic><topic>Telecommunications and information theory</topic><topic>Texture</topic><topic>Tracking</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ling Cai</creatorcontrib><creatorcontrib>Lei He</creatorcontrib><creatorcontrib>Yamashita, T.</creatorcontrib><creatorcontrib>Yiren Xu</creatorcontrib><creatorcontrib>Yuming Zhao</creatorcontrib><creatorcontrib>Xin Yang</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005–Present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Xplore (Online service)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications 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><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on circuits and systems for video technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ling Cai</au><au>Lei He</au><au>Yamashita, T.</au><au>Yiren Xu</au><au>Yuming Zhao</au><au>Xin Yang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Robust Contour Tracking by Combining Region and Boundary Information</atitle><jtitle>IEEE transactions on circuits and systems for video technology</jtitle><stitle>TCSVT</stitle><date>2011-12-01</date><risdate>2011</risdate><volume>21</volume><issue>12</issue><spage>1784</spage><epage>1794</epage><pages>1784-1794</pages><issn>1051-8215</issn><eissn>1558-2205</eissn><coden>ITCTEM</coden><abstract>This paper presents a new object tracking model that systematically combines region and boundary features. Besides traditional region features (intensity/color and texture), we design a new boundary-based object detector for accurate and robust tracking in low-contrast and complex scenes, which usually appear in the commonly used monochrome surveillance systems. In our model, region feature-based energy terms are characterized by probability models, and boundary feature terms include edge and frame difference. With a new weighting term, a novel energy functional is proposed to systematically combine the region and boundary-based components, and it is minimized by a level set evolution equation. For an efficient computational cost, motion information is utilized for new frame level set initialization. Compared with region feature-based models, the experimental results show that the proposed model significantly improves the performance under different circumstances, especially for objects in low-contrast and complex environments.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TCSVT.2011.2133550</doi><tpages>11</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1051-8215 |
| ispartof | IEEE transactions on circuits and systems for video technology, 2011-12, Vol.21 (12), p.1784-1794 |
| issn | 1051-8215 1558-2205 |
| language | eng |
| recordid | cdi_crossref_primary_10_1109_TCSVT_2011_2133550 |
| source | IEEE Xplore (Online service) |
| subjects | Applied sciences Bayesian methods Bayesian model Boundaries Computational efficiency contour evolution Detection, estimation, filtering, equalization, prediction Detectors energy functional Exact sciences and technology Feature extraction feature fusion Frames General equipment and techniques Image color analysis Information, signal and communications theory Instruments, apparatus, components and techniques common to several branches of physics and astronomy kernel density estimation Level set Pattern recognition Physics Robustness Sensors (chemical, optical, electrical, movement, gas, etc.) remote sensing Signal and communications theory Signal processing Signal, noise Studies Surface layer Surveillance systems Telecommunications and information theory Texture Tracking |
| title | Robust Contour Tracking by Combining Region and Boundary Information |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T06%3A16%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Robust%20Contour%20Tracking%20by%20Combining%20Region%20and%20Boundary%20Information&rft.jtitle=IEEE%20transactions%20on%20circuits%20and%20systems%20for%20video%20technology&rft.au=Ling%20Cai&rft.date=2011-12-01&rft.volume=21&rft.issue=12&rft.spage=1784&rft.epage=1794&rft.pages=1784-1794&rft.issn=1051-8215&rft.eissn=1558-2205&rft.coden=ITCTEM&rft_id=info:doi/10.1109/TCSVT.2011.2133550&rft_dat=%3Cproquest_RIE%3E1010895298%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=914305332&rft_id=info:pmid/&rft_ieee_id=5739509&rfr_iscdi=true |