{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T14:46:12Z","timestamp":1770821172202,"version":"3.50.1"},"reference-count":51,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T00:00:00Z","timestamp":1567296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T00:00:00Z","timestamp":1567296000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T00:00:00Z","timestamp":1567296000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772527"],"award-info":[{"award-number":["61772527"]}],"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":["61375035"],"award-info":[{"award-number":["61375035"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Circuits Syst. Video Technol."],"published-print":{"date-parts":[[2019,9]]},"DOI":"10.1109\/tcsvt.2017.2770319","type":"journal-article","created":{"date-parts":[[2017,11,6]],"date-time":"2017-11-06T21:35:11Z","timestamp":1510004111000},"page":"2567-2579","source":"Crossref","is-referenced-by-count":80,"title":["Pixelwise Deep Sequence Learning for Moving Object Detection"],"prefix":"10.1109","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5049-8092","authenticated-orcid":false,"given":"Yingying","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9118-2780","authenticated-orcid":false,"given":"Jinqiao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingke","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4976-3095","authenticated-orcid":false,"given":"Ming","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanqing","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"Proc ICLR"},{"key":"ref38","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc NIPS"},{"key":"ref33","first-page":"1764","article-title":"Towards end-to-end speech recognition with recurrent neural networks","author":"graves","year":"2014","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref32","author":"fayyaz","year":"2016","journal-title":"STFCN spatio-temporal FCN for semantic video segmentation"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.345"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.757"},{"key":"ref37","author":"patraucean","year":"2015","journal-title":"Spatio-temporal video autoencoder with differentiable memory"},{"key":"ref36","first-page":"843","article-title":"Unsupervised Learning of Video Representations using LSTMs","author":"srivastava","year":"2015","journal-title":"Proc 32nd Int Conf Mach Learn (ICML)"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-74690-4_56"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.245"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2726546"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989027"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.03.030"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2005.213"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1979.4766907"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/34.1000236"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2015.137"},{"key":"ref21","first-page":"2017","article-title":"Spatial transformer networks","author":"jaderberg","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref24","author":"bianco","year":"2015","journal-title":"How Far Can You Get By Combining Change Detection Algorithms?"},{"key":"ref23","first-page":"1","article-title":"Learning sharable models for robust background subtraction","author":"chen","year":"2015","journal-title":"Proc ICME"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2017.11"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/IWSSIP.2016.7502717"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref51","first-page":"1","article-title":"BGSLibrary: An OpenCV C++ background subtraction library","author":"sobral","year":"2013","journal-title":"Proc 11th Workshop Vis Comput (WVC)"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2014.68"},{"key":"ref40","author":"he","year":"2015","journal-title":"Deep residual learning for image recognition"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-25903-1_12"},{"key":"ref12","first-page":"433","article-title":"C-EFIC: Color and edge based foreground background segmentation with interior classification","author":"allebosch","year":"2015","journal-title":"Proc Int Joint Conf Comput Vis Imag Comput Graph"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref15","author":"badrinarayanan","year":"2015","journal-title":"Segnet A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"ref17","first-page":"1990","article-title":"Learning to segment object candidates","volume":"28","author":"pinheiro","year":"0","journal-title":"Advances in neural information processing systems"},{"key":"ref18","first-page":"75","article-title":"Learning to refine object segments","author":"pinheiro","year":"2016","journal-title":"Proc ECCV"},{"key":"ref19","author":"guimaraes","year":"2012","journal-title":"An efficient hierarchical graph based image segmentation CoRR"},{"key":"ref4","first-page":"246","article-title":"Adaptive background mixture models for real-time tracking","volume":"2","author":"chris","year":"1999","journal-title":"Proc CVPR"},{"key":"ref3","first-page":"2104","article-title":"Background modeling using adaptive pixelwise kernel variances in a hybrid feature space","author":"learned-miller","year":"2012","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2009.4959741"},{"key":"ref5","first-page":"751","article-title":"Non-parametric model for background subtraction","author":"elgammal","year":"2000","journal-title":"Computer Vision"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5539817"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2012.6238925"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2014.2378053"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2014.126"},{"key":"ref45","first-page":"109","article-title":"Efficient inference in fully connected CRFs with Gaussian edge potentials","author":"kr\u00e4henb\u00fchl","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2004.1333992"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2016.08.005"},{"key":"ref42","first-page":"802","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","author":"shi","year":"2015","journal-title":"Proc Journal of Computer Science"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.179"},{"key":"ref43","author":"chen","year":"2015","journal-title":"Attention to scale Scale-aware semantic image segmentation"}],"container-title":["IEEE Transactions on Circuits and Systems for Video Technology"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/76\/8824151\/08097419.pdf?arnumber=8097419","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T21:07:12Z","timestamp":1657746432000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8097419\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9]]},"references-count":51,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tcsvt.2017.2770319","relation":{},"ISSN":["1051-8215","1558-2205"],"issn-type":[{"value":"1051-8215","type":"print"},{"value":"1558-2205","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9]]}}}