{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T21:07:27Z","timestamp":1780607247704,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":13,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,10,22]],"date-time":"2018-10-22T00:00:00Z","timestamp":1540166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61502105,61672159"],"award-info":[{"award-number":["61502105,61672159"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2018,10,22]]},"DOI":"10.1145\/3207677.3278035","type":"proceedings-article","created":{"date-parts":[[2018,10,18]],"date-time":"2018-10-18T10:19:29Z","timestamp":1539857969000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Human Pose Estimation Method Based on Flexible Model and Deep Learning"],"prefix":"10.1145","author":[{"given":"Binghan","family":"Liu","sequence":"first","affiliation":[{"name":"College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenda","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Ke","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,10,22]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Robust Human Pose Tracking for Realistic Service Robot Applications. In 2017 IEEE International Conference on Computer Vision Workshops, 1363--1372","author":"Vasileiadis M","unstructured":"Vasileiadis M, Malassiotis S, and Giakoumis D, et al. 2017. Robust Human Pose Tracking for Realistic Service Robot Applications. In 2017 IEEE International Conference on Computer Vision Workshops, 1363--1372."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995741"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/2968826.2969020"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.214"},{"key":"e_1_3_2_1_5_1","volume-title":"Learning Deep Feature Representations with Domain Guided Dropout for Person. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, 1249--1258","author":"Xiao T","year":"2016","unstructured":"Xiao T, Li H S, and Ouyang W L, et al. 2016. Learning Deep Feature Representations with Domain Guided Dropout for Person. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, 1249--1258."},{"key":"e_1_3_2_1_6_1","volume-title":"Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation. In British Machine Vision Conference, 1--11","author":"Johnson S","year":"2010","unstructured":"Johnson S, Everingham M. 2010. Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation. In British Machine Vision Conference, 1--11."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.471"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/2976456.2976598"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.299"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.5555\/2968826.2969027"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.82"},{"key":"e_1_3_2_1_12_1","volume-title":"13th European Conference on Computer Vision, 33--47","author":"Ramakrishna V","unstructured":"Ramakrishna V, Munoz D, and Hebert M, et al. 2014. Pose Machines: Articulated Pose Estimation via Inference Machines. In 13th European Conference on Computer Vision, 33--47."},{"key":"e_1_3_2_1_13_1","volume-title":"End-to-End Learning of Deformable Mixture of Parts and Deep Convolutional Neural Networks for Human Pose Estimation. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, 3073--3082","author":"Yang W","year":"2016","unstructured":"Yang W, Ouyang W L, and Li H S, et al. 2016. End-to-End Learning of Deformable Mixture of Parts and Deep Convolutional Neural Networks for Human Pose Estimation. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, 3073--3082."}],"event":{"name":"CSAE '18: The 2nd International Conference on Computer Science and Application Engineering","location":"Hohhot China","acronym":"CSAE '18"},"container-title":["Proceedings of the 2nd International Conference on Computer Science and Application Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3207677.3278035","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3207677.3278035","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T20:40:18Z","timestamp":1780605618000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3207677.3278035"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,22]]},"references-count":13,"alternative-id":["10.1145\/3207677.3278035","10.1145\/3207677"],"URL":"https:\/\/doi.org\/10.1145\/3207677.3278035","relation":{},"subject":[],"published":{"date-parts":[[2018,10,22]]},"assertion":[{"value":"2018-10-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}