{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T20:18:30Z","timestamp":1740169110343,"version":"3.37.3"},"reference-count":57,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Shenzhen Science & Technology Fundamental Research Program","award":["JCYJ20160330095814461"],"award-info":[{"award-number":["JCYJ20160330095814461"]}]},{"name":"National Engineering Laboratory for Video Technology - Shenzhen Division"},{"name":"Shenzhen Key Laboratory for IMVR","award":["ZDSYS201703031405467"],"award-info":[{"award-number":["ZDSYS201703031405467"]}]},{"name":"Shenzhen Municipal Development and Reform Commission (Disciplinary Development Program for Data Science and Intelligent Computing). Special acknowledgments are given to Aoto-PKUSZ Joint Laboratory"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2019]]},"DOI":"10.1109\/access.2019.2929675","type":"journal-article","created":{"date-parts":[[2019,7,18]],"date-time":"2019-07-18T15:51:41Z","timestamp":1563465101000},"page":"168495-168506","source":"Crossref","is-referenced-by-count":2,"title":["SMCA-CNN: Learning a Semantic Mask and Cross-Scale Adaptive Feature for Robust Crowd Counting"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1789-1383","authenticated-orcid":false,"given":"Guoshuai","family":"Wang","sequence":"first","affiliation":[]},{"given":"Yue","family":"Zou","sequence":"additional","affiliation":[]},{"given":"Zirui","family":"Li","sequence":"additional","affiliation":[]},{"given":"Dongming","family":"Yang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01216-8_34"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00057"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000013087.49260.fb"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2016.7477682"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126526"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2016.7533041"},{"key":"ref37","first-page":"802","article-title":"Convolutional LSTM Network: A machine learning approach for precipitation nowcasting","author":"xingjian","year":"2015","journal-title":"Proc 28th Int Conf Neural Inf Process Syst"},{"key":"ref36","first-page":"568","article-title":"Two-stream convolutional networks for action recognition in videos","author":"simonyan","year":"2014","journal-title":"Proc 27th Int Conf Neural Inf Process Syst"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref34","first-page":"91","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","author":"ren","year":"2015","journal-title":"Proc 28th Int Conf Neural Inf Process Syst"},{"key":"ref28","first-page":"1324","article-title":"Learning To count objects in images","author":"lempitsky","year":"2010","journal-title":"Proc 23rd Int Conf Neural Inf Process Syst"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.475"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.372"},{"key":"ref2","first-page":"615","article-title":"Towards perspective-free object counting with deep learning","author":"o\u00f1oro-rubio","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298684"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2018.03.004"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2396051"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.177"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/DICTA.2009.22"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206621"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.5244\/C.19.63"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459191"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00564"},{"key":"ref57","article-title":"Revisiting perspective information for efficient crowd counting","author":"shi","year":"2018","journal-title":"arXiv 1807 01989"},{"key":"ref56","first-page":"1655","article-title":"Deep spatio-temporal residual networks for citywide crowd flows prediction","author":"zhang","year":"2017","journal-title":"Proc 31st AAAI Conf Artif Intell"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"ref52","first-page":"1217","article-title":"Recurrent attentive zooming for joint crowd counting and precise localization","author":"liu","year":"2019","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01216-8_33"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_17"},{"key":"ref40","article-title":"Attention to head locations for crowd counting","author":"zhang","year":"2018","journal-title":"arXiv 1806 10287"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00550"},{"key":"ref13","first-page":"694","article-title":"Perceptual losses for real-time style transfer and super-resolution","author":"johnson","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref14","first-page":"1","article-title":"Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection","author":"li","year":"2008","journal-title":"Proc 19th Int Conf Pattern Recognit"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2008.4587569"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2006.92"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.5244\/C.26.21"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.329"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.429"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.70"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/2964284.2967300"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.206"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00120"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995698"},{"key":"ref7","first-page":"90","article-title":"Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors","author":"wu","year":"2005","journal-title":"Proc 10th IEEE Int Conf Comput Vis"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.79"},{"key":"ref46","first-page":"75","article-title":"Learning to refine object segments","volume":"9905","author":"pinheiro","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref45","first-page":"21","article-title":"SSD: Single shot multibox detector","author":"liu","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref48","article-title":"Multi-scale context aggregation by dilated convolutions","author":"yu","year":"2015","journal-title":"arXiv 1511 07122"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/AVSS.2017.8078491"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01228-1_45"},{"key":"ref44","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"arXiv 1409 1556"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00381"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8600701\/08765698.pdf?arnumber=8765698","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T11:32:16Z","timestamp":1641987136000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8765698\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":57,"URL":"https:\/\/doi.org\/10.1109\/access.2019.2929675","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2019]]}}}