{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T16:18:36Z","timestamp":1783095516599,"version":"3.54.6"},"reference-count":61,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"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":["62103261\/62173228\/61873165"],"award-info":[{"award-number":["62103261\/62173228\/61873165"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Intell. Transport. Syst."],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1109\/tits.2022.3173537","type":"journal-article","created":{"date-parts":[[2022,5,13]],"date-time":"2022-05-13T19:29:50Z","timestamp":1652470190000},"page":"21050-21061","source":"Crossref","is-referenced-by-count":96,"title":["Pedestrian Graph +: A Fast Pedestrian Crossing Prediction Model Based on Graph Convolutional Networks"],"prefix":"10.1109","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4235-8946","authenticated-orcid":false,"given":"Pablo Rodrigo Gantier","family":"Cadena","sequence":"first","affiliation":[{"name":"Department of Automation, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0831-8702","authenticated-orcid":false,"given":"Yeqiang","family":"Qian","sequence":"additional","affiliation":[{"name":"University of Michigan–Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6885-6740","authenticated-orcid":false,"given":"Chunxiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Automation, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8679-9137","authenticated-orcid":false,"given":"Ming","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Automation, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01112"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref33","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"arXiv 1409 1556"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8793991"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/SITIS.2018.00109"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2016.7759351"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref36","first-page":"802","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","author":"xingjian","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref35","first-page":"2261","article-title":"Densely connected convolutional networks","author":"huang","year":"2017","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit (CVPR)"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref61","article-title":"Learning generalisable omni-scale representations for person re-identification","author":"zhou","year":"2019","journal-title":"arXiv 1910 06827"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.2352\/ISSN.2470-1173.2020.16.AVM-109"},{"key":"ref27","article-title":"Pose flow: Efficient online pose tracking","author":"xiu","year":"2018","journal-title":"arXiv 1802 00977"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2836305"},{"key":"ref2","article-title":"Predicting pedestrian crossing intention with feature fusion and spatio-temporal attention","author":"yang","year":"2021","journal-title":"arXiv 2104 05485"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00130"},{"key":"ref20","article-title":"Constructing stronger and faster baselines for skeleton-based action recognition","author":"song","year":"2021","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2017.7995730"},{"key":"ref21","article-title":"What is multimodality?","author":"parcalabescu","year":"2021","journal-title":"arxiv 2103 06304"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00103"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00636"},{"key":"ref26","article-title":"YOLOv4: Optimal speed and accuracy of object detection","author":"bochkovskiy","year":"2020","journal-title":"arXiv 2004 10934"},{"key":"ref25","article-title":"YOLOX: Exceeding YOLO series in 2021","author":"ge","year":"2021","journal-title":"arXiv 2107 08430"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2913372"},{"key":"ref51","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume":"37","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref59","article-title":"Rethinking atrous convolution for semantic image segmentation","author":"chen","year":"2017","journal-title":"arXiv 1706 05587"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1177\/0278364913491297"},{"key":"ref57","article-title":"From big to small: Multi-scale local planar guidance for monocular depth estimation","author":"lee","year":"2019","journal-title":"arXiv 1907 10326"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1117\/12.2520589"},{"key":"ref54","article-title":"Fixing weight decay regularization in Adam","author":"loshchilov","year":"2017","journal-title":"arXiv 1711 05101"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref52","article-title":"Deep learning using rectified linear units (ReLU)","author":"agarap","year":"2018","journal-title":"arXiv 1803 08375"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.510"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.288"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.502"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/IEEECONF51394.2020.9443552"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/IV47402.2020.9304591"},{"key":"ref14","article-title":"Pedestrian action anticipation using contextual feature fusion in stacked RNNs","author":"rasouli","year":"2020","journal-title":"arXiv 2005 06582"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3034236"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"ref17","article-title":"Skeleton-based action recognition with multi-stream adaptive graph convolutional networks","author":"shi","year":"2019","journal-title":"arXiv 1912 06971"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2019.8917118"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01434"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093426"},{"key":"ref3","article-title":"Crossing or not? Context-based recognition of pedestrian crossing intention in the urban environment","author":"yang","year":"2021","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2017.33"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2976305"},{"key":"ref8","first-page":"4694","article-title":"Beyond short snippets: Deep networks for video classification","author":"ng","year":"2015","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit (CVPR)"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8794278"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00441"},{"key":"ref9","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":"ref46","doi-asserted-by":"crossref","first-page":"3123","DOI":"10.1109\/TCYB.2015.2497711","article-title":"Hyperspectral anomaly detection by graph pixel selection","volume":"46","author":"yuan","year":"2015","journal-title":"IEEE Trans Cybern"},{"key":"ref45","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2016","journal-title":"arXiv 1609 02907"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01102"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01230"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00260"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01099"},{"key":"ref44","article-title":"The kinetics human action video dataset","author":"kay","year":"2017","journal-title":"arXiv 1705 06950"},{"key":"ref43","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)"}],"container-title":["IEEE Transactions on Intelligent Transportation Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6979\/9942712\/09774877.pdf?arnumber=9774877","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T22:58:07Z","timestamp":1670281087000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9774877\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11]]},"references-count":61,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tits.2022.3173537","relation":{},"ISSN":["1524-9050","1558-0016"],"issn-type":[{"value":"1524-9050","type":"print"},{"value":"1558-0016","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11]]}}}