Model-Based 3D Hand Pose Estimation from Monocular Video
A novel model-based approach to 3D hand tracking from monocular video is presented. The 3D hand pose, the hand texture, and the illuminant are dynamically estimated through minimization of an objective function. Derived from an inverse problem formulation, the objective function enables explicit use...
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| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence 2011-09, Vol.33 (9), p.1793-1805 |
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| container_title | IEEE transactions on pattern analysis and machine intelligence |
| container_volume | 33 |
| creator | de La Gorce, M. Fleet, D. J. Paragios, N. |
| description | A novel model-based approach to 3D hand tracking from monocular video is presented. The 3D hand pose, the hand texture, and the illuminant are dynamically estimated through minimization of an objective function. Derived from an inverse problem formulation, the objective function enables explicit use of temporal texture continuity and shading information while handling important self-occlusions and time-varying illumination. The minimization is done efficiently using a quasi-Newton method, for which we provide a rigorous derivation of the objective function gradient. Particular attention is given to terms related to the change of visibility near self-occlusion boundaries that are neglected in existing formulations. To this end, we introduce new occlusion forces and show that using all gradient terms greatly improves the performance of the method. Qualitative and quantitative experimental results demonstrate the potential of the approach. |
| doi_str_mv | 10.1109/TPAMI.2011.33 |
| format | Article |
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Qualitative and quantitative experimental results demonstrate the potential of the approach.</description><identifier>ISSN: 0162-8828</identifier><identifier>EISSN: 1939-3539</identifier><identifier>EISSN: 2160-9292</identifier><identifier>DOI: 10.1109/TPAMI.2011.33</identifier><identifier>PMID: 21339527</identifier><identifier>CODEN: ITPIDJ</identifier><language>eng</language><publisher>Los Alamitos, CA: IEEE</publisher><subject>Algorithms ; Applied sciences ; Artificial intelligence ; Computer Science ; Computer science; control theory; systems ; Computer Vision and Pattern Recognition ; Exact sciences and technology ; generative modeling ; gradient descent ; Hand - physiology ; Hand tracking ; Humans ; Image edge detection ; Imaging, Three-Dimensional - methods ; Materials handling ; Mathematical models ; Minimization ; model based shape from shading ; Models, Theoretical ; Optimization ; Pattern recognition. Digital image processing. Computational geometry ; pose estimation ; Solid modeling ; Studies ; Surface layer ; Surface texture ; Texture ; Three dimensional ; Three dimensional displays ; Three dimensional models ; Tracking ; variational formulation ; Video Recording - methods</subject><ispartof>IEEE transactions on pattern analysis and machine intelligence, 2011-09, Vol.33 (9), p.1793-1805</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Sep 2011</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c476t-d8eb22cbc7d787eb9cc97aeef95a9f6710ee3d08dada1625be2fce9ec4f177473</citedby><cites>FETCH-LOGICAL-c476t-d8eb22cbc7d787eb9cc97aeef95a9f6710ee3d08dada1625be2fce9ec4f177473</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5719617$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,314,780,784,796,885,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5719617$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=24707721$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21339527$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-00856313$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>de La Gorce, M.</creatorcontrib><creatorcontrib>Fleet, D. J.</creatorcontrib><creatorcontrib>Paragios, N.</creatorcontrib><title>Model-Based 3D Hand Pose Estimation from Monocular Video</title><title>IEEE transactions on pattern analysis and machine intelligence</title><addtitle>TPAMI</addtitle><addtitle>IEEE Trans Pattern Anal Mach Intell</addtitle><description>A novel model-based approach to 3D hand tracking from monocular video is presented. The 3D hand pose, the hand texture, and the illuminant are dynamically estimated through minimization of an objective function. Derived from an inverse problem formulation, the objective function enables explicit use of temporal texture continuity and shading information while handling important self-occlusions and time-varying illumination. The minimization is done efficiently using a quasi-Newton method, for which we provide a rigorous derivation of the objective function gradient. Particular attention is given to terms related to the change of visibility near self-occlusion boundaries that are neglected in existing formulations. To this end, we introduce new occlusion forces and show that using all gradient terms greatly improves the performance of the method. Qualitative and quantitative experimental results demonstrate the potential of the approach.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer Science</subject><subject>Computer science; control theory; systems</subject><subject>Computer Vision and Pattern Recognition</subject><subject>Exact sciences and technology</subject><subject>generative modeling</subject><subject>gradient descent</subject><subject>Hand - physiology</subject><subject>Hand tracking</subject><subject>Humans</subject><subject>Image edge detection</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Materials handling</subject><subject>Mathematical models</subject><subject>Minimization</subject><subject>model based shape from shading</subject><subject>Models, Theoretical</subject><subject>Optimization</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>pose estimation</subject><subject>Solid modeling</subject><subject>Studies</subject><subject>Surface layer</subject><subject>Surface texture</subject><subject>Texture</subject><subject>Three dimensional</subject><subject>Three dimensional displays</subject><subject>Three dimensional models</subject><subject>Tracking</subject><subject>variational formulation</subject><subject>Video Recording - methods</subject><issn>0162-8828</issn><issn>1939-3539</issn><issn>2160-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqF0cFr2zAUBnBRVtqs7bGnwTCDwXpwpqdnW9Yx67qlkNAeul6FLD0zF8dqpXiw_35Kk2bQS0-Cpx8f7_Exdg58CsDV17vb2fJ6KjjAFPGATUChyrFE9Y5NOFQir2tRH7P3MT5wDkXJ8YgdC0BUpZATVi-9oz7_ZiK5DL9nczO47NZHyq7iuluZdeeHrA1-lS394O3Ym5Ddd478KTtsTR_pbPeesF8_ru4u5_ni5uf15WyR20JW69zV1AhhGyudrCU1ylolDVGrSqPaSgInQsdrZ5xJ25YNidaSIlu0IGUh8YRdbHN_m14_hrRS-Ku96fR8ttCbGed1WSHgH0j2y9Y-Bv80UlzrVRct9b0ZyI9RgwQosVIg3qZYqEIAPNNPr-iDH8OQjtYqMZHieEL5FtngYwzU7ncFrjdF6eei9KYojZj8x13o2KzI7fVLMwl83gETrenbYAbbxf-ukFxKsbn5w9Z1RLT_LiWoCiT-Az2zoGc</recordid><startdate>20110901</startdate><enddate>20110901</enddate><creator>de La Gorce, M.</creator><creator>Fleet, D. J.</creator><creator>Paragios, N.</creator><general>IEEE</general><general>IEEE Computer Society</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><general>Institute of Electrical and Electronics Engineers</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</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><scope>7X8</scope><scope>1XC</scope></search><sort><creationdate>20110901</creationdate><title>Model-Based 3D Hand Pose Estimation from Monocular Video</title><author>de La Gorce, M. ; Fleet, D. J. ; Paragios, N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c476t-d8eb22cbc7d787eb9cc97aeef95a9f6710ee3d08dada1625be2fce9ec4f177473</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Algorithms</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer Science</topic><topic>Computer science; control theory; systems</topic><topic>Computer Vision and Pattern Recognition</topic><topic>Exact sciences and technology</topic><topic>generative modeling</topic><topic>gradient descent</topic><topic>Hand - physiology</topic><topic>Hand tracking</topic><topic>Humans</topic><topic>Image edge detection</topic><topic>Imaging, Three-Dimensional - methods</topic><topic>Materials handling</topic><topic>Mathematical models</topic><topic>Minimization</topic><topic>model based shape from shading</topic><topic>Models, Theoretical</topic><topic>Optimization</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>pose estimation</topic><topic>Solid modeling</topic><topic>Studies</topic><topic>Surface layer</topic><topic>Surface texture</topic><topic>Texture</topic><topic>Three dimensional</topic><topic>Three dimensional displays</topic><topic>Three dimensional models</topic><topic>Tracking</topic><topic>variational formulation</topic><topic>Video Recording - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>de La Gorce, M.</creatorcontrib><creatorcontrib>Fleet, D. 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J.</au><au>Paragios, N.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Model-Based 3D Hand Pose Estimation from Monocular Video</atitle><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle><stitle>TPAMI</stitle><addtitle>IEEE Trans Pattern Anal Mach Intell</addtitle><date>2011-09-01</date><risdate>2011</risdate><volume>33</volume><issue>9</issue><spage>1793</spage><epage>1805</epage><pages>1793-1805</pages><issn>0162-8828</issn><eissn>1939-3539</eissn><eissn>2160-9292</eissn><coden>ITPIDJ</coden><abstract>A novel model-based approach to 3D hand tracking from monocular video is presented. The 3D hand pose, the hand texture, and the illuminant are dynamically estimated through minimization of an objective function. Derived from an inverse problem formulation, the objective function enables explicit use of temporal texture continuity and shading information while handling important self-occlusions and time-varying illumination. 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| ispartof | IEEE transactions on pattern analysis and machine intelligence, 2011-09, Vol.33 (9), p.1793-1805 |
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| subjects | Algorithms Applied sciences Artificial intelligence Computer Science Computer science control theory systems Computer Vision and Pattern Recognition Exact sciences and technology generative modeling gradient descent Hand - physiology Hand tracking Humans Image edge detection Imaging, Three-Dimensional - methods Materials handling Mathematical models Minimization model based shape from shading Models, Theoretical Optimization Pattern recognition. Digital image processing. Computational geometry pose estimation Solid modeling Studies Surface layer Surface texture Texture Three dimensional Three dimensional displays Three dimensional models Tracking variational formulation Video Recording - methods |
| title | Model-Based 3D Hand Pose Estimation from Monocular Video |
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