3D human pose estimation based on negative exponential reduction Gaussian kernel

3D human pose estimation based on negative exponential reduction Gaussian kernel

Human pose estimation has become an important research direction in the field of motion recognition. 3D human pose estimation adds depth information to 2D pose estimation, which is more widely used. In this paper, the weight of each voxel is calculated in the 3D discrete space by projecting the join...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of physics. Conference series 2022-12, Vol.2400 (1), p.12011
Hauptverfasser: Gu, Lanqing, Wang, Yu
Format: Artikel
Sprache:eng
Schlagworte:
Convolution
Human motion
Kernels
Motion perception
Physics
Pose estimation
Three dimensional motion
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 1
container_start_page 12011
container_title Journal of physics. Conference series
container_volume 2400
creator Gu, Lanqing
Wang, Yu
description Human pose estimation has become an important research direction in the field of motion recognition. 3D human pose estimation adds depth information to 2D pose estimation, which is more widely used. In this paper, the weight of each voxel is calculated in the 3D discrete space by projecting the joint point heatmap to directly estimate the 3D human pose. To improve the accuracy of 3D human pose estimation, the Gaussian kernel of heatmap with the variable size is reduced by a negative exponent in the process of training. The dilated convolution of a small convolution kernel is used to replace the large convolution kernel to solve the problem of large computation overhead when detecting key points in discrete 3D space. Experimental results show that this method is effective and can accurately estimate the 3D pose in multi view images.
doi_str_mv 10.1088/1742-6596/2400/1/012011
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2754067854</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2754067854</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2741-37eebffad83477aad60d3aafd9a52f7643b2a9064a7a3aa8fef2c25fd64240143</originalsourceid><addsrcrecordid>eNqFkG9LwzAQxoMoOKefwYLvhNr8a5O9lKlTGThQX4drk2jn1tRkFf32plYmgmDe5Lj73XN3D0LHBJ8RLGVGBKdpkU-KjHKMM5JhQjEhO2i0rexuYyn30UEIS4xZfGKEFuwiee7W0CStCyYxYVOvYVO7JikhGJ3EoDFPMfMWi--ta0yzqWGVeKO76oubQRdCHQVejG_M6hDtWVgFc_T9j9Hj1eXD9Dqd381upufztKKCk5QJY0prQUvGhQDQBdYMwOoJ5NSKgrOSwgQXHATEvLTG0ormVhc8nkk4G6OTQbf17rWLe6ul63wTRyoqco4LIfOeEgNVeReCN1a1Ph7oPxTBqrdP9cao3iTV26eIGuyLnadDZ-3aH-nbxfT-N6habSPM_oD_G_EJhCN_vw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2754067854</pqid></control><display><type>article</type><title>3D human pose estimation based on negative exponential reduction Gaussian kernel</title><source>IOP Publishing Free Content</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>IOPscience extra</source><source>Alma/SFX Local Collection</source><source>Free Full-Text Journals in Chemistry</source><creator>Gu, Lanqing ; Wang, Yu</creator><creatorcontrib>Gu, Lanqing ; Wang, Yu</creatorcontrib><description>Human pose estimation has become an important research direction in the field of motion recognition. 3D human pose estimation adds depth information to 2D pose estimation, which is more widely used. In this paper, the weight of each voxel is calculated in the 3D discrete space by projecting the joint point heatmap to directly estimate the 3D human pose. To improve the accuracy of 3D human pose estimation, the Gaussian kernel of heatmap with the variable size is reduced by a negative exponent in the process of training. The dilated convolution of a small convolution kernel is used to replace the large convolution kernel to solve the problem of large computation overhead when detecting key points in discrete 3D space. Experimental results show that this method is effective and can accurately estimate the 3D pose in multi view images.</description><identifier>ISSN: 1742-6588</identifier><identifier>EISSN: 1742-6596</identifier><identifier>DOI: 10.1088/1742-6596/2400/1/012011</identifier><language>eng</language><publisher>Bristol: IOP Publishing</publisher><subject>Convolution ; Human motion ; Kernels ; Motion perception ; Physics ; Pose estimation ; Three dimensional motion</subject><ispartof>Journal of physics. Conference series, 2022-12, Vol.2400 (1), p.12011</ispartof><rights>Published under licence by IOP Publishing Ltd</rights><rights>Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://iopscience.iop.org/article/10.1088/1742-6596/2400/1/012011/pdf$$EPDF$$P50$$Giop$$Hfree_for_read</linktopdf><link.rule.ids>314,780,784,27924,27925,38868,38890,53840,53867</link.rule.ids></links><search><creatorcontrib>Gu, Lanqing</creatorcontrib><creatorcontrib>Wang, Yu</creatorcontrib><title>3D human pose estimation based on negative exponential reduction Gaussian kernel</title><title>Journal of physics. Conference series</title><addtitle>J. Phys.: Conf. Ser</addtitle><description>Human pose estimation has become an important research direction in the field of motion recognition. 3D human pose estimation adds depth information to 2D pose estimation, which is more widely used. In this paper, the weight of each voxel is calculated in the 3D discrete space by projecting the joint point heatmap to directly estimate the 3D human pose. To improve the accuracy of 3D human pose estimation, the Gaussian kernel of heatmap with the variable size is reduced by a negative exponent in the process of training. The dilated convolution of a small convolution kernel is used to replace the large convolution kernel to solve the problem of large computation overhead when detecting key points in discrete 3D space. Experimental results show that this method is effective and can accurately estimate the 3D pose in multi view images.</description><subject>Convolution</subject><subject>Human motion</subject><subject>Kernels</subject><subject>Motion perception</subject><subject>Physics</subject><subject>Pose estimation</subject><subject>Three dimensional motion</subject><issn>1742-6588</issn><issn>1742-6596</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>O3W</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqFkG9LwzAQxoMoOKefwYLvhNr8a5O9lKlTGThQX4drk2jn1tRkFf32plYmgmDe5Lj73XN3D0LHBJ8RLGVGBKdpkU-KjHKMM5JhQjEhO2i0rexuYyn30UEIS4xZfGKEFuwiee7W0CStCyYxYVOvYVO7JikhGJ3EoDFPMfMWi--ta0yzqWGVeKO76oubQRdCHQVejG_M6hDtWVgFc_T9j9Hj1eXD9Dqd381upufztKKCk5QJY0prQUvGhQDQBdYMwOoJ5NSKgrOSwgQXHATEvLTG0ormVhc8nkk4G6OTQbf17rWLe6ul63wTRyoqco4LIfOeEgNVeReCN1a1Ph7oPxTBqrdP9cao3iTV26eIGuyLnadDZ-3aH-nbxfT-N6habSPM_oD_G_EJhCN_vw</recordid><startdate>20221201</startdate><enddate>20221201</enddate><creator>Gu, Lanqing</creator><creator>Wang, Yu</creator><general>IOP Publishing</general><scope>O3W</scope><scope>TSCCA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>H8D</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20221201</creationdate><title>3D human pose estimation based on negative exponential reduction Gaussian kernel</title><author>Gu, Lanqing ; Wang, Yu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2741-37eebffad83477aad60d3aafd9a52f7643b2a9064a7a3aa8fef2c25fd64240143</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Convolution</topic><topic>Human motion</topic><topic>Kernels</topic><topic>Motion perception</topic><topic>Physics</topic><topic>Pose estimation</topic><topic>Three dimensional motion</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gu, Lanqing</creatorcontrib><creatorcontrib>Wang, Yu</creatorcontrib><collection>IOP Publishing Free Content</collection><collection>IOPscience (Open Access)</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Aerospace Database</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</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>ProQuest Central China</collection><jtitle>Journal of physics. Conference series</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gu, Lanqing</au><au>Wang, Yu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>3D human pose estimation based on negative exponential reduction Gaussian kernel</atitle><jtitle>Journal of physics. Conference series</jtitle><addtitle>J. Phys.: Conf. Ser</addtitle><date>2022-12-01</date><risdate>2022</risdate><volume>2400</volume><issue>1</issue><spage>12011</spage><pages>12011-</pages><issn>1742-6588</issn><eissn>1742-6596</eissn><abstract>Human pose estimation has become an important research direction in the field of motion recognition. 3D human pose estimation adds depth information to 2D pose estimation, which is more widely used. In this paper, the weight of each voxel is calculated in the 3D discrete space by projecting the joint point heatmap to directly estimate the 3D human pose. To improve the accuracy of 3D human pose estimation, the Gaussian kernel of heatmap with the variable size is reduced by a negative exponent in the process of training. The dilated convolution of a small convolution kernel is used to replace the large convolution kernel to solve the problem of large computation overhead when detecting key points in discrete 3D space. Experimental results show that this method is effective and can accurately estimate the 3D pose in multi view images.</abstract><cop>Bristol</cop><pub>IOP Publishing</pub><doi>10.1088/1742-6596/2400/1/012011</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1742-6588
ispartof Journal of physics. Conference series, 2022-12, Vol.2400 (1), p.12011
issn 1742-6588
1742-6596
language eng
recordid cdi_proquest_journals_2754067854
source IOP Publishing Free Content; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; IOPscience extra; Alma/SFX Local Collection; Free Full-Text Journals in Chemistry
subjects Convolution
Human motion
Kernels
Motion perception
Physics
Pose estimation
Three dimensional motion
title 3D human pose estimation based on negative exponential reduction Gaussian kernel
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-21T06%3A55%3A08IST&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=3D%20human%20pose%20estimation%20based%20on%20negative%20exponential%20reduction%20Gaussian%20kernel&rft.jtitle=Journal%20of%20physics.%20Conference%20series&rft.au=Gu,%20Lanqing&rft.date=2022-12-01&rft.volume=2400&rft.issue=1&rft.spage=12011&rft.pages=12011-&rft.issn=1742-6588&rft.eissn=1742-6596&rft_id=info:doi/10.1088/1742-6596/2400/1/012011&rft_dat=%3Cproquest_cross%3E2754067854%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=2754067854&rft_id=info:pmid/&rfr_iscdi=true