{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T07:31:52Z","timestamp":1771918312564,"version":"3.50.1"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T00:00:00Z","timestamp":1624579200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T00:00:00Z","timestamp":1624579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2019YJS043"],"award-info":[{"award-number":["2019YJS043"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,2]]},"DOI":"10.1007\/s10489-021-02562-5","type":"journal-article","created":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T20:02:52Z","timestamp":1624651372000},"page":"3018-3028","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Dynamic pedestrian trajectory forecasting with LSTM-based Delaunay triangulation"],"prefix":"10.1007","volume":"52","author":[{"given":"Qiulin","family":"Ma","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8070-5267","authenticated-orcid":false,"given":"Qi","family":"Zou","sequence":"additional","affiliation":[]},{"given":"Yaping","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Nan","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,25]]},"reference":[{"issue":"2","key":"2562_CR1","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/MITS.2019.2903525","volume":"11","author":"Z Chen","year":"2019","unstructured":"Chen Z, Zhang Y, Wu C, Ran B (2019) Understanding individualization driving states via latent dirichlet allocation model. IEEE Intell Transport Syst Magaz 11(2):41\u201353","journal-title":"IEEE Intell Transport Syst Magaz"},{"key":"2562_CR2","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1007\/s10489-009-0173-z","volume":"33","author":"S Qiao","year":"2010","unstructured":"Qiao S, Tang C, Jin H et al (2010) PutMode: prediction of uncertain trajectories in moving objects databases. Appl Intell 33:370\u2013386","journal-title":"Appl Intell"},{"key":"2562_CR3","doi-asserted-by":"crossref","unstructured":"Chen Z, Cai H, Zhang Y, Wu C, Mu M, Li Z, Sotelo MA (2019) A novel sparse representation model for pedestrian abnormal trajectory understanding. In: Expert Systems with Applications, 138","DOI":"10.1016\/j.eswa.2019.06.041"},{"key":"2562_CR4","doi-asserted-by":"crossref","unstructured":"Khodabandelou G, Kheriji W, Selem FH (2020) Link traffic speed forecasting using convolutional attention-based gated recurrent unit. Appl Intell","DOI":"10.1007\/s10489-020-02020-8"},{"key":"2562_CR5","doi-asserted-by":"crossref","unstructured":"Yamaguchi K, Berg AC, Ortiz LE, Berg TL (2011) Who are you with and Where are you going?. In: IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2011.5995468"},{"key":"2562_CR6","doi-asserted-by":"crossref","unstructured":"Alahi A, Goel K, Ramannathan V, Robicquet A, Fei-Fei L, Savarese S (2016) Social LSTM: Human trajectory prediction in crowded spaces. In: IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2016.110"},{"key":"2562_CR7","unstructured":"Varshneya D, Srinivasaraghavan G (2017) Human trajectory prediction using spatially aware deep attention models. In: 31st Conference on neural information processing systems (NIPS)"},{"key":"2562_CR8","doi-asserted-by":"crossref","unstructured":"Alahi A, Ramanathan V, Goel K, Robicquet A, Sadeghian A, Li F-F, Savarese S (2017) Learning to predict human behaviour in crowded scenes. In: Group and crowd behavior for computer vision","DOI":"10.1016\/B978-0-12-809276-7.00011-4"},{"key":"2562_CR9","doi-asserted-by":"crossref","unstructured":"Zhang P, Ouyang W, Zhang P, Xue J (2019) SR-LSTM: State refinement for LSTM towards pedestrian trajectory prediction. In: IEEE Conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2019.01236"},{"key":"2562_CR10","unstructured":"Pajouheshgar E, Lampert CH (2018) Back to square one: Probabilistic trajectory forecasting without bells and whistles. In: 32nd Conference on neural information processing systems (NIPS)"},{"issue":"5","key":"2562_CR11","doi-asserted-by":"publisher","first-page":"4282","DOI":"10.1103\/PhysRevE.51.4282","volume":"51","author":"D Helbing","year":"1995","unstructured":"Helbing D, Molnar P (1995) Social force model for pedestrian dynamics. Phys Rev E 51 (5):4282\u20134286","journal-title":"Phys Rev E"},{"key":"2562_CR12","doi-asserted-by":"crossref","unstructured":"Gupta A, Johnson J, Li F-F, Savarese S, Alahi A (2018) Social GAN: Socially acceptable trajectories with generative adversarial networks. In: IEEE Conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2018.00240"},{"key":"2562_CR13","doi-asserted-by":"crossref","unstructured":"Alahi A, Ramanathan V, Li F-F (2014) Socially-aware large-scale crowd forecasting. In: IEEE Conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2014.283"},{"key":"2562_CR14","doi-asserted-by":"crossref","unstructured":"Xu Y, Zhixin P, Gao S (2018) Encoding crowd interaction with DNN for pedestrian trajectory prediction. In: IEEE Conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2018.00553"},{"key":"2562_CR15","doi-asserted-by":"crossref","unstructured":"Vemula A, Muelling K, Oh J (2018) Social attention: Modeling attention in human crowds. In: IEEE International conference on robotics and automation (ICRA), 1\u20137","DOI":"10.1109\/ICRA.2018.8460504"},{"key":"2562_CR16","doi-asserted-by":"crossref","unstructured":"Jain A, Zamir AR, Savarese S, Saxena A (2016) Structural-RNN: Deep learning on spatio-temporal graphs. In: IEEE Conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2016.573"},{"key":"2562_CR17","doi-asserted-by":"crossref","unstructured":"Nikhil N, Tran Morris B (2018) Convolutional neural network for trajectory prediction. In: European conference on computer vision \u2013 ECCV 2018 workshops","DOI":"10.1007\/978-3-030-11015-4_16"},{"key":"2562_CR18","doi-asserted-by":"crossref","unstructured":"Bartoli F, Lisanti G, Ballan L, Del Bimbo A (2018) Context-aware trajectory prediction in crowded spaces. In: 24th International conference on pattern recognition","DOI":"10.1109\/ICPR.2018.8545447"},{"key":"2562_CR19","unstructured":"Haddad S, Wu M, Wei H, Lam SK (2019) Situation-aware pedestrian trajectory prediction with spatio-temporal attention model. In: 24th Computer vision winter workshop (CVWW)"},{"key":"2562_CR20","doi-asserted-by":"crossref","unstructured":"Sadeghian A, Kosaraju V, Sadeghian A, Hirose N, Rezatofighi H, Savarese S (2019) SoPhie: An Attentive GAN for predicting paths compliant to social and physical constraints. In: IEEE\/CVF Conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2019.00144"},{"key":"2562_CR21","unstructured":"Huynh M, Gita A (2018) Scene-LSTM: A model for human trajectory prediction. arXiv:1808.04018"},{"key":"2562_CR22","doi-asserted-by":"crossref","unstructured":"Fradi H, Luvison B, Pham QC (2016) Crowd behavior analysis using local mid-level visual descriptors. In: IEEE Transactions on circuits and systems for video technology, vol. 27","DOI":"10.1109\/TCSVT.2016.2615443"},{"key":"2562_CR23","doi-asserted-by":"crossref","unstructured":"Mikolov T, Karafiat M, Burget L, Cernock H, Khudanpur S (2010) Recurrent neural network based language model. In: Eleventh annual conference of the international speech communication association","DOI":"10.21437\/Interspeech.2010-343"},{"key":"2562_CR24","doi-asserted-by":"crossref","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. In: IEEE Transactions on circuits and systems for video technology, vol. 9","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"2562_CR25","doi-asserted-by":"crossref","unstructured":"Graves A (2013) Generating sequences with recurrent neural networks. Comput Sci","DOI":"10.1007\/978-3-642-24797-2_3"},{"key":"2562_CR26","doi-asserted-by":"crossref","unstructured":"Pellegrini S, Ess A, Schindler K, Van Gool L (2009) You\u2019ll never walk alone: Modeling social behavior for multi-targer tracking. In: IEEE 12th international conference on computer vision (ICCV)","DOI":"10.1109\/ICCV.2009.5459260"},{"issue":"3","key":"2562_CR27","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1111\/j.1467-8659.2007.01089.x","volume":"26","author":"A Lerner","year":"2007","unstructured":"Lerner A, Chrysanthou Y, Lischinski D (2007) Crowds by example. Comput Graph Forum 26(3):655\u2013664","journal-title":"Comput Graph Forum"},{"key":"2562_CR28","doi-asserted-by":"crossref","unstructured":"Liang J, Jiang L, Niebles JC, Hauptmann AG, Li F-F (2019) Peeking into the future: Predicting future person activities and locations in videos. In: IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2019.00587"},{"key":"2562_CR29","doi-asserted-by":"crossref","unstructured":"Sun J, Jiang Q, Lu C (2020) Recursive social behavior graph for trajectory prediction. In: IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR42600.2020.00074"},{"key":"2562_CR30","doi-asserted-by":"crossref","unstructured":"Fang L, Jiang Q, Shi J, Zhou B (2020) TPNet: trajectory proposal network for motion prediction. In: IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR42600.2020.00683"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02562-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02562-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02562-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,10]],"date-time":"2022-02-10T05:21:13Z","timestamp":1644470473000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02562-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,25]]},"references-count":30,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["2562"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02562-5","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,25]]},"assertion":[{"value":"24 May 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 June 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}