{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T13:54:32Z","timestamp":1777643672391,"version":"3.51.4"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":["61772102"],"award-info":[{"award-number":["61772102"]}],"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":["61806057"],"award-info":[{"award-number":["61806057"]}],"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":["62176036"],"award-info":[{"award-number":["62176036"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Liaoning Collaborative Fund","award":["2020-HYLH-17"],"award-info":[{"award-number":["2020-HYLH-17"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2023,1,1]]},"DOI":"10.1109\/tpami.2022.3147639","type":"journal-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T20:46:13Z","timestamp":1643748373000},"page":"1070-1086","source":"Crossref","is-referenced-by-count":39,"title":["SEEM: A Sequence Entropy Energy-Based Model for Pedestrian Trajectory All-Then-One Prediction"],"prefix":"10.1109","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5501-9492","authenticated-orcid":false,"given":"Dafeng","family":"Wang","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence, Dalian Maritime University, Dalian, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9296-9975","authenticated-orcid":false,"given":"Hongbo","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Dalian Maritime University, Dalian, China"}]},{"given":"Naiyao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Dalian Maritime University, Dalian, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8924-3468","authenticated-orcid":false,"given":"Yiyang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Dalian Maritime University, Dalian, China"}]},{"given":"Hua","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, Georgia Southern University, Statesboro, GA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3016-6197","authenticated-orcid":false,"given":"Sean","family":"McLoone","sequence":"additional","affiliation":[{"name":"School of Electronics, Electrical Engineering and Computer Science, Queen&#x0027;s University Belfast, Belfast, U.K."}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3069362"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2007.01089.x"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2018.09.002"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8460504"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/IROS40897.2019.8967811"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.110"},{"key":"ref37","first-page":"261","article-title":"You&#x2019;ll never walk alone: Modeling social behavior for multi-target tracking","author":"pellegrini","year":"2009","journal-title":"Proc IEEE 12th Int Conf Comput Vis"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/72.572103"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01443"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00637"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2949414"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58583-9_28"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01052"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.233"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00240"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00144"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00359"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5933"},{"key":"ref18","first-page":"1","article-title":"A tutorial on energy-based learning","volume":"1","author":"lecun","year":"2006","journal-title":"Predicting Structured Data"},{"key":"ref19","first-page":"1","article-title":"Energy-based generative adversarial network","author":"zhao","year":"2017","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206641"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1177\/0278364920917446"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.51.4282"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/IROS45743.2020.9341034"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3449359"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995468"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3038217"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3008558"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2015.2496231"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2975837"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3058599"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01240"},{"key":"ref20","first-page":"549","article-title":"Learning social etiquette: Human trajectory understanding in crowded scenes","author":"robicquet","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01236"},{"key":"ref22","first-page":"5718","article-title":"Peeking into the future: Predicting future person activities and locations in videos","author":"liang","year":"2019","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref21","first-page":"151","article-title":"Car-Net: Clairvoyant attentive recurrent network","author":"sadeghian","year":"2018","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58536-5_45"},{"key":"ref24","first-page":"2172","article-title":"InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets","author":"chen","year":"2016","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref41","first-page":"137","article-title":"Social-BiGAT: Multimodal trajectory forecasting using Bicycle-GAN and graph attention networks","author":"kosaraju","year":"2019","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.493"},{"key":"ref44","first-page":"683","article-title":"Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data","author":"salzmann","year":"2020","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00246"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01164"},{"key":"ref25","first-page":"683","article-title":"Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data","author":"salzmann","year":"2020","journal-title":"Proc 16th Eur Conf Comput Vis"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/9970415\/09699076.pdf?arnumber=9699076","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T19:14:56Z","timestamp":1672082096000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9699076\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,1]]},"references-count":45,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2022.3147639","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,1]]}}}