{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T09:47:24Z","timestamp":1770457644007,"version":"3.49.0"},"reference-count":62,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61977061"],"award-info":[{"award-number":["61977061"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51934007"],"award-info":[{"award-number":["51934007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3037238","type":"journal-article","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T21:36:39Z","timestamp":1605044199000},"page":"211869-211881","source":"Crossref","is-referenced-by-count":8,"title":["Optimizing Features Quality: A Normalized Covariance Fusion Framework for Skeleton Action Recognition"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5172-9203","authenticated-orcid":false,"given":"Guan","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5159-4633","authenticated-orcid":false,"given":"Qiuyan","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-41822-8_17"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.105"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2018.2867619"},{"key":"ref32","article-title":"Sensor fusion: Gated recurrent fusion to learn driving behavior from temporal multimodal data","author":"narayanan","year":"2019","journal-title":"arXiv 1910 00628"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2017.2774007"},{"key":"ref30","first-page":"2121","article-title":"4Devise: A deep visual-semantic embedding model","volume":"2013","author":"frome","year":"0","journal-title":"Proc NIPS"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.263"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2008.924108"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2019.8803806"},{"key":"ref34","first-page":"417","article-title":"Spectral graph skeletons for 3d action recognition","author":"kerola","year":"2014","journal-title":"Proc Asian Conf Comput Vis"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/109"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00572"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00792"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313552"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1011"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.301"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2017.01.011"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.108"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/2822013.2822032"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.09.065"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.342"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2014.06.077"},{"key":"ref23","first-page":"2222","article-title":"Multimodal learning with deep Boltzmann machines","author":"srivastava","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2487860"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.299"},{"key":"ref50","article-title":"Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks","author":"zhu","year":"2016","journal-title":"arXiv 1603 07772"},{"key":"ref51","first-page":"816","article-title":"Spatio-temporal lstm with trust gates for 3d human action recognition","author":"liu","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref59","article-title":"Spatial temporal transformer network for skeleton-based action recognition","author":"plizzari","year":"2020","journal-title":"arXiv 2008 07404"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/iThings\/GreenCom\/CPSCom\/SmartData.2019.00095"},{"key":"ref57","article-title":"A Riemannian network for SPD matrix learning","author":"huang","year":"2016","journal-title":"arXiv 1608 04233"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2019.8802917"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46484-8_2"},{"key":"ref54","article-title":"How to construct deep recurrent neural networks","author":"pascanu","year":"2013","journal-title":"arXiv 1312 6026"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2015.7178838"},{"key":"ref52","article-title":"An empirical study of language CNN for image captioning","author":"gu","year":"2016","journal-title":"arXiv 1612 07086"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2010.5596746"},{"key":"ref40","first-page":"388","article-title":"Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces","author":"quang","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2013.04.007"},{"key":"ref12","first-page":"15","article-title":"Human action recognition using dynamic time warping and voting algorithm","volume":"30","author":"pham","year":"2014","journal-title":"VNU Journal of Science Comp Science & Com Eng"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.389"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.82"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.98"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2013.153"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.123"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/THMS.2014.2377111"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/4351435"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-24797-2_2"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/19.2.185"},{"key":"ref5","first-page":"1","article-title":"Human action recognition using a temporal hierarchy of covariance descriptors on 3D joint locations","author":"hussein","year":"2013","journal-title":"Proc Int Joint Conf Artif Intell"},{"key":"ref8","article-title":"Layer normalization","author":"lei ba","year":"2016","journal-title":"arXiv 1607 06450"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.460"},{"key":"ref7","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"arXiv 1502 03167"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"ref46","first-page":"1110","article-title":"Hierarchical recurrent neural network for skeleton based action recognition","author":"du","year":"2015","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit (CVPR)"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2013.6475006"},{"key":"ref48","first-page":"1010","article-title":"NTU RGB+D: A large scale dataset for 3D human activity analysis","author":"shahroudy","year":"2016","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit (CVPR)"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2552404"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.2307\/j.ctv14jx6sm"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.132"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.519"},{"key":"ref43","author":"bishop","year":"2006","journal-title":"Pattern Recognition Machine Learning"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09253537.pdf?arnumber=9253537","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:54:55Z","timestamp":1639770895000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9253537\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":62,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3037238","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}