{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T16:11:34Z","timestamp":1771517494867,"version":"3.50.1"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585884","type":"print"},{"value":"9783030585891","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-58589-1_33","type":"book-chapter","created":{"date-parts":[[2020,11,11]],"date-time":"2020-11-11T06:18:04Z","timestamp":1605075484000},"page":"547-563","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Dynamic and Static Context-Aware LSTM for Multi-agent Motion Prediction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6093-0854","authenticated-orcid":false,"given":"Chaofan","family":"Tao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5509-7247","authenticated-orcid":false,"given":"Qinhong","family":"Jiang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0723-4016","authenticated-orcid":false,"given":"Lixin","family":"Duan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6685-7950","authenticated-orcid":false,"given":"Ping","family":"Luo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,12]]},"reference":[{"key":"33_CR1","doi-asserted-by":"crossref","unstructured":"Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S.: Social LSTM: human trajectory prediction in crowded spaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 961\u2013971 (2016)","DOI":"10.1109\/CVPR.2016.110"},{"key":"33_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-11015-4_18","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"N Bisagno","year":"2019","unstructured":"Bisagno, N., Zhang, B., Conci, N.: Group LSTM: group trajectory prediction in crowded scenarios. In: Leal-Taix\u00e9, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11131, pp. 213\u2013225. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11015-4_18"},{"key":"33_CR3","doi-asserted-by":"crossref","unstructured":"Choi, C., Dariush, B.: Looking to relations for future trajectory forecast. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 921\u2013930 (2019)","DOI":"10.1109\/ICCV.2019.00101"},{"key":"33_CR4","doi-asserted-by":"crossref","unstructured":"Fragkiadaki, K., Levine, S., Felsen, P., Malik, J.: Recurrent network models for human dynamics. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4346\u20134354 (2015)","DOI":"10.1109\/ICCV.2015.494"},{"key":"33_CR5","doi-asserted-by":"crossref","unstructured":"Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., Alahi, A.: Social GAN: socially acceptable trajectories with generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2255\u20132264 (2018)","DOI":"10.1109\/CVPR.2018.00240"},{"issue":"8","key":"33_CR6","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"33_CR7","doi-asserted-by":"crossref","unstructured":"Huang, Y., Bi, H., Li, Z., Mao, T., Wang, Z.: STGAT: modeling spatial-temporal interactions for human trajectory prediction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 6272\u20136281 (2019)","DOI":"10.1109\/ICCV.2019.00637"},{"key":"33_CR8","doi-asserted-by":"crossref","unstructured":"Ivanovic, B., Pavone, M.: The trajectron: probabilistic multi-agent trajectory modeling with dynamic spatiotemporal graphs. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2375\u20132384 (2019)","DOI":"10.1109\/ICCV.2019.00246"},{"key":"33_CR9","doi-asserted-by":"crossref","unstructured":"Karasev, V., Ayvaci, A., Heisele, B., Soatto, S.: Intent-aware long-term prediction of pedestrian motion. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 2543\u20132549. IEEE (2016)","DOI":"10.1109\/ICRA.2016.7487409"},{"key":"33_CR10","doi-asserted-by":"crossref","unstructured":"Kim, B., Kang, C.M., Kim, J., Lee, S.H., Chung, C.C., Choi, J.W.: Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 399\u2013404. IEEE (2017)","DOI":"10.1109\/ITSC.2017.8317943"},{"key":"33_CR11","unstructured":"Kosaraju, V., Sadeghian, A., Mart\u00edn-Mart\u00edn, R., Reid, I., Rezatofighi, H., Savarese, S.: Social-BIGAT: multimodal trajectory forecasting using bicycle-GAN and graph attention networks. In: Advances in Neural Information Processing Systems, pp. 137\u2013146 (2019)"},{"key":"33_CR12","doi-asserted-by":"crossref","unstructured":"Lee, N., Choi, W., Vernaza, P., Choy, C.B., Torr, P.H., Chandraker, M.: Desire: distant future prediction in dynamic scenes with interacting agents. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 336\u2013345 (2017)","DOI":"10.1109\/CVPR.2017.233"},{"key":"33_CR13","doi-asserted-by":"crossref","unstructured":"Lerner, A., Chrysanthou, Y., Lischinski, D.: Crowds by example. In: Computer Graphics Forum, vol. 26, pp. 655\u2013664. Wiley Online Library (2007)","DOI":"10.1111\/j.1467-8659.2007.01089.x"},{"key":"33_CR14","doi-asserted-by":"crossref","unstructured":"Ma, Y., Zhu, X., Zhang, S., Yang, R., Wang, W., Manocha, D.: Trafficpredict: trajectory prediction for heterogeneous traffic-agents. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 6120\u20136127 (2019)","DOI":"10.1609\/aaai.v33i01.33016120"},{"key":"33_CR15","unstructured":"Manh, H., Alaghband, G.: Scene-LSTM: a model for human trajectory prediction. arXiv preprint arXiv:1808.04018 (2018)"},{"key":"33_CR16","doi-asserted-by":"crossref","unstructured":"Park, S.H., Kim, B., Kang, C.M., Chung, C.C., Choi, J.W.: Sequence-to-sequence prediction of vehicle trajectory via LSTM encoder-decoder architecture. In: 2018 IEEE Intelligent Vehicles Symposium (IV), pp. 1672\u20131678. IEEE (2018)","DOI":"10.1109\/IVS.2018.8500658"},{"key":"33_CR17","doi-asserted-by":"crossref","unstructured":"Pellegrini, S., Ess, A., Schindler, K., Van Gool, L.: You\u2019ll never walk alone: modeling social behavior for multi-target tracking. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 261\u2013268. IEEE (2009)","DOI":"10.1109\/ICCV.2009.5459260"},{"key":"33_CR18","doi-asserted-by":"crossref","unstructured":"Petrich, D., Dang, T., Kasper, D., Breuel, G., Stiller, C.: Map-based long term motion prediction for vehicles in traffic environments. In: 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), pp. 2166\u20132172. IEEE (2013)","DOI":"10.1109\/ITSC.2013.6728549"},{"key":"33_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1007\/978-3-319-46484-8_33","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Robicquet","year":"2016","unstructured":"Robicquet, A., Sadeghian, A., Alahi, A., Savarese, S.: Learning social etiquette: human trajectory understanding in crowded scenes. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 549\u2013565. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_33"},{"key":"33_CR20","doi-asserted-by":"crossref","unstructured":"Rudenko, A., Palmieri, L., Herman, M., Kitani, K.M., Gavrila, D.M., Arras, K.O.: Human motion trajectory prediction: a survey. arXiv preprint arXiv:1905.06113 (2019)","DOI":"10.1177\/0278364920917446"},{"key":"33_CR21","doi-asserted-by":"crossref","unstructured":"Sadeghian, A., Kosaraju, V., Sadeghian, A., Hirose, N., Rezatofighi, H., Savarese, S.: Sophie: an attentive GAN for predicting paths compliant to social and physical constraints. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1349\u20131358 (2019)","DOI":"10.1109\/CVPR.2019.00144"},{"key":"33_CR22","unstructured":"Shah, R., Romijnders, R.: Applying deep learning to basketball trajectories. arXiv preprint arXiv:1608.03793 (2016)"},{"key":"33_CR23","doi-asserted-by":"crossref","unstructured":"Synnaeve, G., Dupoux, E.: A temporal coherence loss function for learning unsupervised acoustic embeddings. In: SLTU, pp. 95\u2013100 (2016)","DOI":"10.1016\/j.procs.2016.04.035"},{"key":"33_CR24","doi-asserted-by":"crossref","unstructured":"Tai, K.S., Socher, R., Manning, C.D.: Improved semantic representations from tree-structured long short-term memory networks. arXiv preprint arXiv:1503.00075 (2015)","DOI":"10.3115\/v1\/P15-1150"},{"key":"33_CR25","doi-asserted-by":"crossref","unstructured":"Vasquez, D.: Novel planning-based algorithms for human motion prediction. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 3317\u20133322. IEEE (2016)","DOI":"10.1109\/ICRA.2016.7487505"},{"key":"33_CR26","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"33_CR27","doi-asserted-by":"crossref","unstructured":"Vemula, A., Muelling, K., Oh, J.: Social attention: modeling attention in human crowds. In: 2018 IEEE international Conference on Robotics and Automation (ICRA), pp. 1\u20137. IEEE (2018)","DOI":"10.1109\/ICRA.2018.8460504"},{"key":"33_CR28","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"33_CR29","doi-asserted-by":"crossref","unstructured":"Xu, Y., Piao, Z., Gao, S.: Encoding crowd interaction with deep neural network for pedestrian trajectory prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5275\u20135284 (2018)","DOI":"10.1109\/CVPR.2018.00553"},{"key":"33_CR30","doi-asserted-by":"crossref","unstructured":"Yamaguchi, K., Berg, A.C., Ortiz, L.E., Berg, T.L.: Who are you with and where are you going? In: CVPR 2011, pp. 1345\u20131352. IEEE (2011)","DOI":"10.1109\/CVPR.2011.5995468"},{"issue":"9","key":"33_CR31","doi-asserted-by":"publisher","first-page":"4354","DOI":"10.1109\/TIP.2016.2590322","volume":"25","author":"S Yi","year":"2016","unstructured":"Yi, S., Li, H., Wang, X.: Pedestrian behavior modeling from stationary crowds with applications to intelligent surveillance. IEEE Trans. Image Process. 25(9), 4354\u20134368 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"33_CR32","unstructured":"Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-attention generative adversarial networks. arXiv preprint arXiv:1805.08318 (2018)"},{"key":"33_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, P., Ouyang, W., Zhang, P., Xue, J., Zheng, N.: SR-LSTM: state refinement for LSTM towards pedestrian trajectory prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12085\u201312094 (2019)","DOI":"10.1109\/CVPR.2019.01236"},{"key":"33_CR34","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"33_CR35","doi-asserted-by":"crossref","unstructured":"Zhao, T., et al.: Multi-agent tensor fusion for contextual trajectory prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12126\u201312134 (2019)","DOI":"10.1109\/CVPR.2019.01240"},{"key":"33_CR36","doi-asserted-by":"crossref","unstructured":"Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ade20k dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)","DOI":"10.1109\/CVPR.2017.544"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58589-1_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:23:38Z","timestamp":1731284618000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58589-1_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585884","9783030585891"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58589-1_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"12 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1360","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}