{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T13:01:51Z","timestamp":1763643711421,"version":"3.28.0"},"reference-count":26,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T00:00:00Z","timestamp":1661040000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T00:00:00Z","timestamp":1661040000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,8,21]]},"DOI":"10.1109\/icpr56361.2022.9956137","type":"proceedings-article","created":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T19:34:13Z","timestamp":1669750453000},"page":"3529-3535","source":"Crossref","is-referenced-by-count":2,"title":["Pedestrian Trajectory Prediction Using LSTM and Sparse Motion Fields"],"prefix":"10.1109","author":[{"given":"Pedro","family":"Bilro","sequence":"first","affiliation":[{"name":"Universidade de Lisboa,Institute for Systems and Robotics, Instituto Superior T&#x00E9;cnico,Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Catarina","family":"Barata","sequence":"additional","affiliation":[{"name":"Universidade de Lisboa,Institute for Systems and Robotics, Instituto Superior T&#x00E9;cnico,Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jorge S.","family":"Marques","sequence":"additional","affiliation":[{"name":"Universidade de Lisboa,Institute for Systems and Robotics, Instituto Superior T&#x00E9;cnico,Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00144"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01443"},{"key":"ref12","first-page":"683","article-title":"Trajectron++: Multi-agent generative trajectory forecasting with heterogeneous data for control","author":"salzmann","year":"2020","journal-title":"European Conference on Computer Vision (ECCV)"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/NetSys.2019.8854506"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2007.01089.x"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1177\/0278364920917446"},{"key":"ref18","article-title":"Human trajectory forecasting in crowds: A deep learning perspective","author":"kothari","year":"2021","journal-title":"IEEE Transactions on Intelligent Transportation Systems (ITS)"},{"key":"ref19","first-page":"2672","article-title":"Generative adversarial nets","volume":"27","author":"goodfellow","year":"2014","journal-title":"Advances in Neural Information Processing Systems (NIPS)"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107631"},{"key":"ref3","first-page":"5725","article-title":"Peeking into the future: Predicting future person activities and locations in videos","author":"liang","year":"2019","journal-title":"IEEE Conf Computer Vision and Pattern Recognition (CVPR)"},{"key":"ref6","first-page":"261","article-title":"You&#x2019;ll never walk alone: Modeling social behavior for multi-target tracking","author":"pellegrini","year":"2009","journal-title":"IEEE International Conference on Computer Vision (ICCV)"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.51.4282"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00240"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.110"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01252-6_10"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2974393"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.493"},{"key":"ref20","first-page":"137","article-title":"Social-BiGAT: Multimodal trajectory forecasting using bicycle-gan and graph attention networks","volume":"32","author":"kosaraju","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00135"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2018.8545447"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00635"},{"key":"ref23","volume":"382","author":"mclachlan","year":"2007","journal-title":"The EM Algorithm and Extensions"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2969925"},{"article-title":"Adam: A method for stochastic optimization","year":"2014","author":"kingma","key":"ref25"}],"event":{"name":"2022 26th International Conference on Pattern Recognition (ICPR)","start":{"date-parts":[[2022,8,21]]},"location":"Montreal, QC, Canada","end":{"date-parts":[[2022,8,25]]}},"container-title":["2022 26th International Conference on Pattern Recognition (ICPR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9956007\/9955631\/09956137.pdf?arnumber=9956137","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T20:06:02Z","timestamp":1671480362000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9956137\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,21]]},"references-count":26,"URL":"https:\/\/doi.org\/10.1109\/icpr56361.2022.9956137","relation":{},"subject":[],"published":{"date-parts":[[2022,8,21]]}}}