{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:56:56Z","timestamp":1753887416388,"version":"3.41.2"},"reference-count":38,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T00:00:00Z","timestamp":1625616000000},"content-version":"vor","delay-in-days":187,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61771299"],"award-info":[{"award-number":["61771299"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Wireless Communications and Mobile Computing"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Currently, human pose estimation (HPE) methods mainly rely on the design framework of Convolutional Neural Networks (CNNs). These CNNs typically consist of high\u2010to\u2010low\u2010resolution subnetworks (encoder) to learn semantic information and low\u2010to\u2010high subnetworks (decoder) to raise the resolution for keypoint localization. Because too low\u2010resolution feature maps in encoder will inevitably lose some spatial information, which cannot be recovered in the upsampling stages, keeping high spatial resolution features is critical for human pose estimation. On the other hand, due to scale variation of human body parts, multiscale features are also very important for human pose estimation. In this paper, a novel backbone network is proposed specifically for HPE, named High Spatial Resolution and Multiscale Networks (HSR\u2010MSNet), which maintain high spatial resolution features in deeper layers of the encoder and meanwhile construct multiscale features within one single residual block via subgroup splitting and fusion of feature maps. Experiments show that our approach outperforms other state\u2010of\u2010the\u2010art methods with more accurate keypoint locations on COCO dataset.<\/jats:p>","DOI":"10.1155\/2021\/4948067","type":"journal-article","created":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T22:50:06Z","timestamp":1625698206000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Approach of Intelligent Computing for Multiperson Pose Estimation with Deep High Spatial Resolution and Multiscale Features"],"prefix":"10.1155","volume":"2021","author":[{"given":"Haiquan","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yijie","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0074-7695","authenticated-orcid":false,"given":"Yanping","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5706-2841","authenticated-orcid":false,"given":"Chunhua","family":"Qian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7974-9510","authenticated-orcid":false,"given":"Rui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,7,7]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"e_1_2_9_2_2","unstructured":"KalchbrennerN. GrefenstetteE. andBlunsomP. A convolutional neural network for modelling sentences 2014 https:\/\/arxiv.org\/abs\/1404.2188."},{"key":"e_1_2_9_3_2","doi-asserted-by":"crossref","unstructured":"KimY. Convolutional neural networks for sentence classification 2014 https:\/\/arxiv.org\/abs\/1408.5882.","DOI":"10.3115\/v1\/D14-1181"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.59"},{"key":"e_1_2_9_5_2","doi-asserted-by":"crossref","unstructured":"ToshevA.andSzegedyC. DeepPose: human pose estimation via deep neural networks 2013.","DOI":"10.1109\/CVPR.2014.214"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-020-01185-5"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_2_9_8_2","doi-asserted-by":"crossref","unstructured":"PishchulinL. AndrilukaM. GehlerP. andSchieleB. Strong appearance and expressive spatial models for human pose estimation 2013 IEEE International Conference on Computer Vision 2014 Sydney NSW Australia 3487\u20133494 https:\/\/doi.org\/10.1109\/iccv.2013.433 2-s2.0-84898795911.","DOI":"10.1109\/ICCV.2013.433"},{"key":"e_1_2_9_9_2","doi-asserted-by":"crossref","unstructured":"YangW. LiS. OuyangW. LiH. andWangX. Learning feature pyramids for human pose estimation 2017 IEEE International Conference on Computer Vision (ICCV) 2017 Venice Italy 1281\u20131290 https:\/\/doi.org\/10.1109\/iccv.2017.144 2-s2.0-85041915295.","DOI":"10.1109\/ICCV.2017.144"},{"key":"e_1_2_9_10_2","doi-asserted-by":"crossref","unstructured":"KeL. ChangM. C. QiH. andLyuS. Multi-scale structure-aware network for human pose estimation 2018 25th IEEE International Conference on Image Processing (ICIP) 2018 Athens Greece 713\u2013728 https:\/\/doi.org\/10.1109\/icip.2018.8451114 2-s2.0-85062920135.","DOI":"10.1109\/ICIP.2018.8451114"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_12"},{"key":"e_1_2_9_12_2","unstructured":"ZhangH. OuyangH. LiuS. QiX. ShenX. YangR. andJiaJ. Human pose estimation with spatial contextual information 2019 https:\/\/arxiv.org\/abs\/1901.01760."},{"key":"e_1_2_9_13_2","doi-asserted-by":"crossref","unstructured":"AndrilukaM. PishchulinL. GehlerP. andSchieleB. 2D Human pose estimation: new benchmark and state of the art analysis 2014 IEEE Conference on Computer Vision and Pattern Recognition 2014 Columbus OH USA 3686\u20133693 https:\/\/doi.org\/10.1109\/cvpr.2014.471 2-s2.0-84911448580.","DOI":"10.1109\/CVPR.2014.471"},{"key":"e_1_2_9_14_2","doi-asserted-by":"crossref","unstructured":"ChenY. WangZ. PengY. ZhangZ. YuG. andSunJ. Cascaded pyramid network for multi-person pose estimation 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2018 Salt Lake City UT USA 7103\u20137112 https:\/\/doi.org\/10.1109\/cvpr.2018.00742 2-s2.0-85061772116.","DOI":"10.1109\/CVPR.2018.00742"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01231-1_29"},{"key":"e_1_2_9_16_2","doi-asserted-by":"crossref","unstructured":"HeK. ZhangX. RenS. andSunJ. Deep residual learning for image recognition 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 Las Vegas NV USA 770\u2013778 https:\/\/doi.org\/10.1109\/cvpr.2016.90 2-s2.0-84986274465.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcbb.2020.2991173"},{"key":"e_1_2_9_18_2","doi-asserted-by":"crossref","unstructured":"SunK. XiaoB. DongL. andWangJ. Deep high-resolution representation learning for human pose estimation 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2019 Long Beach CA USA 5693\u20135703 https:\/\/doi.org\/10.1109\/cvpr.2019.00584.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"e_1_2_9_19_2","doi-asserted-by":"crossref","unstructured":"HowardA. SandlerM. andChuG. Searching for MobileNetv3 2019 IEEE\/CVF International Conference on Computer Vision (ICCV) 2019 Seoul Korea 1314\u20131324 https:\/\/doi.org\/10.1109\/iccv.2019.00140.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"e_1_2_9_20_2","unstructured":"TompsonJ. J. JainA. LeCunY. andBreglerC. Joint training of a convolutional network and a graphical model for human pose estimation 2014 https:\/\/arxiv.org\/abs\/1406.2984."},{"key":"e_1_2_9_21_2","doi-asserted-by":"crossref","unstructured":"WeiS.-E. RamakrishnaV. KanadeT. andSheikhY. Convolutional pose machines 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 Las Vegas NV USA 4724\u20134732 https:\/\/doi.org\/10.1109\/cvpr.2016.511 2-s2.0-85009920674.","DOI":"10.1109\/CVPR.2016.511"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3010248"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"e_1_2_9_24_2","doi-asserted-by":"crossref","unstructured":"LiB. LiuK. JiY. YangJ. andLiuC. Selective complementary features for multi-person pose estimation 2020 IEEE international conference on image processing (ICIP 2020 Abu Dhabi United Arab Emirates 623\u2013627 https:\/\/doi.org\/10.1109\/icip40778.2020.9191163.","DOI":"10.1109\/ICIP40778.2020.9191163"},{"key":"e_1_2_9_25_2","doi-asserted-by":"crossref","unstructured":"CaoZ. SimonT. WeiS. E. andSheikhY. Realtime multi-person 2d pose estimation using part affinity fields 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 Honolulu HI USA 7291\u20137299 https:\/\/doi.org\/10.1109\/cvpr.2017.143 2-s2.0-85042428134.","DOI":"10.1109\/CVPR.2017.143"},{"key":"e_1_2_9_26_2","doi-asserted-by":"crossref","unstructured":"HeK. GkioxariG. Doll\u00e1rP. andGirshickR. Mask R-CNN IEEE Transactions on Pattern Analysis & Machine Intelligence 2020 42 no. 2 386\u2013397 https:\/\/doi.org\/10.1109\/TPAMI.2018.2844175 2-s2.0-85048205789.","DOI":"10.1109\/TPAMI.2018.2844175"},{"key":"e_1_2_9_27_2","doi-asserted-by":"crossref","unstructured":"FangH.-S. XieS. TaiY.-W. andLuC. RMPE: regional multi-person pose estimation 2017 IEEE International Conference on Computer Vision (ICCV) 2017 Venice Italy 2334\u20132343 https:\/\/doi.org\/10.1109\/iccv.2017.256 2-s2.0-85041930089.","DOI":"10.1109\/ICCV.2017.256"},{"key":"e_1_2_9_28_2","doi-asserted-by":"crossref","unstructured":"LiuW. AnguelovD. ErhanD. SzegedyC. ReedS. FuC. Y. andBergA. C. SSD: Single shot multibox detector European conference on computer vision 2016 Amsterdam The Netherlands 21\u201337.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"e_1_2_9_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"e_1_2_9_30_2","unstructured":"LiZ. PengC. YuG. ZhangX. DengY. andSunJ. DetNet: a backbone network for object detection 2018 https:\/\/arxiv.org\/abs\/1804.06215."},{"key":"e_1_2_9_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2938758"},{"key":"e_1_2_9_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3419842"},{"key":"e_1_2_9_33_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00607-021-00907-z"},{"key":"e_1_2_9_34_2","unstructured":"RedmonJ.andFarhadiA. YOLOv3: an incremental improvement 2018 https:\/\/arxiv.org\/abs\/1804.02767."},{"key":"e_1_2_9_35_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"e_1_2_9_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2020.12.009"},{"key":"e_1_2_9_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2913372"},{"key":"e_1_2_9_38_2","doi-asserted-by":"crossref","unstructured":"PapandreouG. ZhuT. KanazawaN. ToshevA. TompsonJ. BreglerC. andMurphyK. Towards accurate multi-person pose estimation in the wild 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 Honolulu HI USA 4903\u20134911 https:\/\/doi.org\/10.1109\/cvpr.2017.395 2-s2.0-85041929929.","DOI":"10.1109\/CVPR.2017.395"}],"container-title":["Wireless Communications and Mobile Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2021\/4948067.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2021\/4948067.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/4948067","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T13:41:56Z","timestamp":1723038116000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/4948067"}},"subtitle":[],"editor":[{"given":"Xiangjie","family":"Kong","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":38,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/4948067"],"URL":"https:\/\/doi.org\/10.1155\/2021\/4948067","archive":["Portico"],"relation":{},"ISSN":["1530-8669","1530-8677"],"issn-type":[{"type":"print","value":"1530-8669"},{"type":"electronic","value":"1530-8677"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-04-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-06-22","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-07-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"4948067"}}