{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T18:58:16Z","timestamp":1771613896340,"version":"3.50.1"},"reference-count":58,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":["61773085"],"award-info":[{"award-number":["61773085"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Instrum. Meas."],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/tim.2021.3054627","type":"journal-article","created":{"date-parts":[[2021,1,26]],"date-time":"2021-01-26T20:31:22Z","timestamp":1611693082000},"page":"1-13","source":"Crossref","is-referenced-by-count":25,"title":["An Automatic Detection Algorithm of Metro Passenger Boarding and Alighting Based on Deep Learning and Optical Flow"],"prefix":"10.1109","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4118-2368","authenticated-orcid":false,"given":"Quanli","family":"Liu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6038-7361","authenticated-orcid":false,"given":"Qiang","family":"Guo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7608-7438","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5632-9639","authenticated-orcid":false,"given":"Yuanqing","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5922-4971","authenticated-orcid":false,"given":"Qiang","family":"Kang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref38","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"howard","year":"2017","journal-title":"arXiv 1704 04861"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-73603-7_14"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00731"},{"key":"ref31","first-page":"745","article-title":"Graininess-aware deep feature learning for pedestrian detection","author":"lin","year":"2018","journal-title":"Proc ECCV"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.351"},{"key":"ref37","article-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size","author":"iandola","year":"2016","journal-title":"arXiv 1602 07360"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref35","article-title":"A survey of model compression and acceleration for deep neural networks","author":"cheng","year":"2017","journal-title":"arXiv 1710 09282"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966367"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2694224"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2019.02.005"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2018.12.007"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2019.2917735"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1002\/rob.21918"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2017.2648850"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.5244\/C.29.32"},{"key":"ref21","first-page":"138","article-title":"Bi-box regression for pedestrian detection and occlusion estimation","author":"zhou","year":"2018","journal-title":"Proc ECCV"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.12.042"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.384"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2017.0329"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-018-5819-6"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.319"},{"key":"ref51","first-page":"3258","article-title":"A discriminative deep model for pedestrian detection with occlusion handling","author":"ouyang","year":"2012","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref58","article-title":"Ablation studies in artificial neural networks","author":"meyes","year":"2019","journal-title":"arXiv 1901 08644"},{"key":"ref57","first-page":"1967","article-title":"Pelee: A real-time object detection system on mobile devices","author":"wang","year":"2018","journal-title":"Proc NIPS"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref55","article-title":"YOLOv3: An incremental improvement","author":"redmon","year":"2018","journal-title":"arXiv 1804 02767"},{"key":"ref54","first-page":"21","article-title":"SSD: Single shot multibox detector","author":"liu","year":"2016","journal-title":"Proc ECCV"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.155"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459207"},{"key":"ref11","first-page":"613","article-title":"Ten years of pedestrian detection, what have we learned?","author":"benenson","year":"2014","journal-title":"Proc ECCV"},{"key":"ref40","article-title":"DiCENet: Dimension-wise convolutions for efficient networks","author":"mehta","year":"2019","journal-title":"arXiv 1906 03516"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2005.1505106"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.257"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.465"},{"key":"ref15","first-page":"91","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","author":"ren","year":"2015","journal-title":"Proc NIPS"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299034"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2017.2759508"},{"key":"ref18","first-page":"443","article-title":"Is faster R-CNN doing well for pedestrian detection?","author":"zhang","year":"2016","journal-title":"Proc ECCV"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-016-0890-9"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2020.3001370"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2019.2915404"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.474"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2015.08.027"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2003.1238422"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.1997.609319"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1982.1056489"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.177"},{"key":"ref46","first-page":"674","article-title":"An iterative image registration technique with an application to stereo vision","author":"lucas","year":"1981","journal-title":"Proc IJCAI"},{"key":"ref45","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.cosrev.2018.03.001","article-title":"New trends on moving object detection in video images captured by a moving camera: A survey","volume":"28","author":"yazdi","year":"2018","journal-title":"Comput Sci Rev"},{"key":"ref48","first-page":"1","article-title":"Multi-scale context aggregation by dilated convolutions","author":"yu","year":"2016","journal-title":"Proc ICLR"},{"key":"ref47","first-page":"1","article-title":"Object detectors emerge in deep scene CNNs","author":"zhou","year":"2015","journal-title":"Proc ICLR"},{"key":"ref42","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1006\/cviu.2000.0870","article-title":"Tracking groups of people","volume":"80","author":"mckenna","year":"2000","journal-title":"Comput Vis Image Understand"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ACV.1998.732851"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-017-1487-y"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.1997.631988"}],"container-title":["IEEE Transactions on Instrumentation and Measurement"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/19\/9259274\/09335992.pdf?arnumber=9335992","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:51:31Z","timestamp":1652194291000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9335992\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":58,"URL":"https:\/\/doi.org\/10.1109\/tim.2021.3054627","relation":{},"ISSN":["0018-9456","1557-9662"],"issn-type":[{"value":"0018-9456","type":"print"},{"value":"1557-9662","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}