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Human Body 3D Posture Estimation Using Significant Points and Two Cameras
This paper proposes a three-dimensional (3D) human posture estimation system that locates 3D significant body points based on 2D body contours extracted from two cameras without using any depth sensors. The 3D significant body points that are located by this system include the head, the center of th...
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Published in: | TheScientificWorld 2014-01, Vol.2014 (2014), p.1-17 |
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description | This paper proposes a three-dimensional (3D) human posture estimation system that locates 3D significant body points based on 2D body contours extracted from two cameras without using any depth sensors. The 3D significant body points that are located by this system include the head, the center of the body, the tips of the feet, the tips of the hands, the elbows, and the knees. First, a linear support vector machine- (SVM-) based segmentation method is proposed to distinguish the human body from the background in red, green, and blue (RGB) color space. The SVM-based segmentation method uses not only normalized color differences but also included angle between pixels in the current frame and the background in order to reduce shadow influence. After segmentation, 2D significant points in each of the two extracted images are located. A significant point volume matching (SPVM) method is then proposed to reconstruct the 3D significant body point locations by using 2D posture estimation results. Experimental results show that the proposed SVM-based segmentation method shows better performance than other gray level- and RGB-based segmentation approaches. This paper also shows the effectiveness of the 3D posture estimation results in different postures. |
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The 3D significant body points that are located by this system include the head, the center of the body, the tips of the feet, the tips of the hands, the elbows, and the knees. First, a linear support vector machine- (SVM-) based segmentation method is proposed to distinguish the human body from the background in red, green, and blue (RGB) color space. The SVM-based segmentation method uses not only normalized color differences but also included angle between pixels in the current frame and the background in order to reduce shadow influence. After segmentation, 2D significant points in each of the two extracted images are located. A significant point volume matching (SPVM) method is then proposed to reconstruct the 3D significant body point locations by using 2D posture estimation results. Experimental results show that the proposed SVM-based segmentation method shows better performance than other gray level- and RGB-based segmentation approaches. This paper also shows the effectiveness of the 3D posture estimation results in different postures.</description><identifier>ISSN: 2356-6140</identifier><identifier>ISSN: 1537-744X</identifier><identifier>EISSN: 1537-744X</identifier><identifier>DOI: 10.1155/2014/670953</identifier><identifier>PMID: 24883422</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Algorithms ; Cameras ; Human body ; Humans ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; Imaging, Three-Dimensional - methods ; Kalman filters ; Personal computers ; Photography - methods ; Posture ; Sensors</subject><ispartof>TheScientificWorld, 2014-01, Vol.2014 (2014), p.1-17</ispartof><rights>Copyright © 2014 Chia-Feng Juang et al.</rights><rights>COPYRIGHT 2014 John Wiley & Sons, Inc.</rights><rights>Copyright © 2014 Chia-Feng Juang et al. Chia-Feng Juang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><rights>Copyright © 2014 Chia-Feng Juang et al. 2014</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c600t-865a22e728a7477e6caab5b4303c0ab5f22c155416ae229a18b02d3eef3fca0f3</citedby><cites>FETCH-LOGICAL-c600t-865a22e728a7477e6caab5b4303c0ab5f22c155416ae229a18b02d3eef3fca0f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1547917321/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1547917321?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24883422$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Ding, X.</contributor><contributor>Deng, Y.</contributor><creatorcontrib>Juang, C.-F.</creatorcontrib><creatorcontrib>Du, Wei-Chin</creatorcontrib><creatorcontrib>Chen, Teng-Chang</creatorcontrib><title>Human Body 3D Posture Estimation Using Significant Points and Two Cameras</title><title>TheScientificWorld</title><addtitle>ScientificWorldJournal</addtitle><description>This paper proposes a three-dimensional (3D) human posture estimation system that locates 3D significant body points based on 2D body contours extracted from two cameras without using any depth sensors. 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This paper also shows the effectiveness of the 3D posture estimation results in different postures.</description><subject>Algorithms</subject><subject>Cameras</subject><subject>Human body</subject><subject>Humans</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Kalman filters</subject><subject>Personal computers</subject><subject>Photography - methods</subject><subject>Posture</subject><subject>Sensors</subject><issn>2356-6140</issn><issn>1537-744X</issn><issn>1537-744X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNqNks9rFDEUgIModls9eZcBL1KZNj8nMxehrtUuFBRswVt4k0mmWXaSNpmx9L8369TaFQ-SQ8LLl-_lJQ-hVwQfESLEMcWEH1cSN4I9QQsimCwl59-fogVloiorwvEe2k9pjTGrJRHP0R7ldc04pQu0OpsG8MWH0N0V7GPxNaRxiqY4TaMbYHTBF5fJ-b745nrvrNPgxww5P6YCfFdc3IZiCYOJkF6gZxY2yby8nw_Q5afTi-VZef7l82p5cl7qCuOxrCsBlBpJa5BcSlNpgFa0nGGmcV5ZSnWuipMKDKUNkLrFtGPGWGY1YMsO0Gr2dgHW6jrme8Y7FcCpX4EQewVxdHpjFDc5pW4bEE3FMWsaTmoqNGENlQRslV3vZ9f11A6m08aPETY70t0d765UH36obKNSiix4ey-I4WYyaVSDS9psNuBNmJLKv0EaQWu6zfXmL3QdpujzU2WKy4ZIRskfqodcgPM25Lx6K1UnnDBJqOAsU0f_oPLozOB08Ma6HN858G4-oGNIKRr7UCPBattFattFau6iTL9-_CwP7O-2ycDhDFw538Gt-z-byYix8AgWOFfOfgKQN9Sh</recordid><startdate>20140101</startdate><enddate>20140101</enddate><creator>Juang, C.-F.</creator><creator>Du, Wei-Chin</creator><creator>Chen, Teng-Chang</creator><general>Hindawi Publishing Corporation</general><general>John Wiley & Sons, Inc</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</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>3V.</scope><scope>7QP</scope><scope>7TK</scope><scope>7TM</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20140101</creationdate><title>Human Body 3D Posture Estimation Using Significant Points and Two Cameras</title><author>Juang, C.-F. ; Du, Wei-Chin ; Chen, Teng-Chang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c600t-865a22e728a7477e6caab5b4303c0ab5f22c155416ae229a18b02d3eef3fca0f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Algorithms</topic><topic>Cameras</topic><topic>Human body</topic><topic>Humans</topic><topic>Image Enhancement - methods</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Imaging, Three-Dimensional - methods</topic><topic>Kalman filters</topic><topic>Personal computers</topic><topic>Photography - methods</topic><topic>Posture</topic><topic>Sensors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Juang, C.-F.</creatorcontrib><creatorcontrib>Du, Wei-Chin</creatorcontrib><creatorcontrib>Chen, Teng-Chang</creatorcontrib><collection>الدوريات العلمية والإحصائية - e-Marefa Academic and Statistical Periodicals</collection><collection>معرفة - المحتوى العربي الأكاديمي المتكامل - e-Marefa Academic Complete</collection><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Agriculture Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Directory of Open Access Journals</collection><jtitle>TheScientificWorld</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Juang, C.-F.</au><au>Du, Wei-Chin</au><au>Chen, Teng-Chang</au><au>Ding, X.</au><au>Deng, Y.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Human Body 3D Posture Estimation Using Significant Points and Two Cameras</atitle><jtitle>TheScientificWorld</jtitle><addtitle>ScientificWorldJournal</addtitle><date>2014-01-01</date><risdate>2014</risdate><volume>2014</volume><issue>2014</issue><spage>1</spage><epage>17</epage><pages>1-17</pages><issn>2356-6140</issn><issn>1537-744X</issn><eissn>1537-744X</eissn><abstract>This paper proposes a three-dimensional (3D) human posture estimation system that locates 3D significant body points based on 2D body contours extracted from two cameras without using any depth sensors. The 3D significant body points that are located by this system include the head, the center of the body, the tips of the feet, the tips of the hands, the elbows, and the knees. First, a linear support vector machine- (SVM-) based segmentation method is proposed to distinguish the human body from the background in red, green, and blue (RGB) color space. The SVM-based segmentation method uses not only normalized color differences but also included angle between pixels in the current frame and the background in order to reduce shadow influence. After segmentation, 2D significant points in each of the two extracted images are located. A significant point volume matching (SPVM) method is then proposed to reconstruct the 3D significant body point locations by using 2D posture estimation results. Experimental results show that the proposed SVM-based segmentation method shows better performance than other gray level- and RGB-based segmentation approaches. This paper also shows the effectiveness of the 3D posture estimation results in different postures.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><pmid>24883422</pmid><doi>10.1155/2014/670953</doi><tpages>17</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Cameras Human body Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Kalman filters Personal computers Photography - methods Posture Sensors |
title | Human Body 3D Posture Estimation Using Significant Points and Two Cameras |
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