{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T03:36:15Z","timestamp":1742960175710,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":40,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819981403"},{"type":"electronic","value":"9789819981410"}],"license":[{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8141-0_19","type":"book-chapter","created":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T09:02:16Z","timestamp":1700902936000},"page":"244-259","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["S-CGRU: An Efficient Model for\u00a0Pedestrian Trajectory Prediction"],"prefix":"10.1007","author":[{"given":"Zhenwei","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wushouer","family":"Slamu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaoyong","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhida","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,26]]},"reference":[{"key":"19_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":"19_CR2","doi-asserted-by":"crossref","unstructured":"Deo, N., Trivedi, M.M.: Convolutional social pooling for vehicle trajectory prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1468\u20131476 (2018)","DOI":"10.1109\/CVPRW.2018.00196"},{"issue":"8","key":"19_CR3","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1177\/0278364920917446","volume":"39","author":"A Rudenko","year":"2020","unstructured":"Rudenko, A., Palmieri, L., Herman, M., Kitani, K.M., Gavrila, D.M., Arras, K.O.: Human motion trajectory prediction: a survey. Int. J. Robot. Res. 39(8), 895\u2013935 (2020)","journal-title":"Int. J. Robot. Res."},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Yue, J., Manocha, D., Wang, H.: Human trajectory prediction via neural social physics. arXiv preprint arXiv:2207.10435 (2022)","DOI":"10.1007\/978-3-031-19830-4_22"},{"issue":"5","key":"19_CR5","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.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282 (1995)","journal-title":"Phys. Rev. E"},{"key":"19_CR6","doi-asserted-by":"crossref","unstructured":"van den Berg, J., Lin, M., Manocha, D.: Reciprocal velocity obstacles for real-time multi-agent navigation. In: 2008 IEEE International Conference on Robotics and Automation (2008)","DOI":"10.1109\/ROBOT.2008.4543489"},{"key":"19_CR7","doi-asserted-by":"crossref","unstructured":"He, F., Xia, Y., Zhao, X., Wang, H.: Informative scene decomposition for crowd analysis, comparison and simulation guidance. ACM Transaction on Graphics (TOG) 4(39) (2020) 51(5), 4282 (1995)","DOI":"10.1145\/3386569.3392407"},{"key":"19_CR8","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"},{"key":"19_CR9","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\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1349\u20131358 (2019)","DOI":"10.1109\/CVPR.2019.00144"},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"Mangalam, K., An, Y., Girase, H., Malik, J.: From goals, waypoints & paths to long term human trajectory forecasting. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15233\u201315242 (2021)","DOI":"10.1109\/ICCV48922.2021.01495"},{"key":"19_CR11","doi-asserted-by":"crossref","unstructured":"Van Toll, W., Pettr\u2019e, J.: Algorithms for microscopic crowd simulation: advancements in the 2010s. Comput. Graph. Forum 40(2), 731\u2013754 (2021)","DOI":"10.1111\/cgf.142664"},{"key":"19_CR12","doi-asserted-by":"crossref","unstructured":"Wolinski, D., J. Guy, S., Olivier, A.H., Lin, M., Manocha, D., Pettr\u2019e, J.: Parameter estimation and comparative evaluation of crowd simulations. Comput. Graph. Forum 33(2), 303\u2013312 (2014)","DOI":"10.1111\/cgf.12328"},{"key":"19_CR13","doi-asserted-by":"crossref","unstructured":"He, F., Xia, Y., Zhao, X., Wang, H.: Informative scene decomposition for crowd analysis, comparison and simulation guidance. ACM Trans. Graph. (TOG) 39(4), 50:1\u201350:13 (2020)","DOI":"10.1145\/3386569.3392407"},{"issue":"12","key":"19_CR14","doi-asserted-by":"publisher","first-page":"24126","DOI":"10.1109\/TITS.2022.3205676","volume":"23","author":"R Korbmacher","year":"2022","unstructured":"Korbmacher, R., Tordeux, A.: Review of pedestrian trajectory prediction methods: comparing deep learning and knowledge-based approaches. IEEE Trans. Intell. Transp. Syst. 23(12), 24126\u201324144 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"19_CR15","unstructured":"Bengio, Y., Pal, C.J.: Deep complex networks. In: International Conference on Learning Representations (ICLR) (2018)"},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Nitta, T.: On the critical points of the complex-valued neural network. In: Neural Information Processing (2002)","DOI":"10.1007\/3-540-44989-2_118"},{"issue":"4","key":"19_CR17","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1109\/TNNLS.2012.2183613","volume":"23","author":"A Hirose","year":"2012","unstructured":"Hirose, A., Yoshida, S.: Generalization characteristics of complex-valued feedforward neural networks in relation to signal coherence. IEEE Trans. Neural Netw. Learn. Syst. 23(4), 541\u2013551 (2012)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"19_CR18","unstructured":"Arjovsky, M., Shah, A., Bengio, Y.: Unitary evolution recurrent neural networks. arXiv preprint arXiv:1511.06464 (2015)"},{"key":"19_CR19","unstructured":"Danihelka, I., Wayne, G., Uria, B., Kalchbrenner, N., Graves, A.: Associative long short-term memory. arXiv preprint arXiv:1602.03032 (2016)"},{"key":"19_CR20","unstructured":"Wisdom, S., Powers, T., Hershey, J., Roux, J.L., Atlas, L.: Full-capacity unitary recurrent neural networks. In: Advances in Neural Information Processing Systems, pp. 4880\u20134888 (2016)"},{"key":"19_CR21","unstructured":"Reichert, D.P., Serre, T.: Neuronal synchrony in complex-valued deep networks. arXiv preprint arXiv:1312.6115 (2013)"},{"key":"19_CR22","unstructured":"Srivastava, R.K., Greff, K., Schmidhuber, J.: Training very deep net-works. In: Advances in Neural Information Processing Systems, pp. 2377\u20132385 (2015)"},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder-decoder approaches. arXiv pre-print arXiv:1409.1259 (2014)","DOI":"10.3115\/v1\/W14-4012"},{"issue":"8","key":"19_CR24","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":"19_CR25","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations (2015)"},{"key":"19_CR26","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2017)"},{"issue":"8","key":"19_CR27","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1016\/j.trb.2005.09.006","volume":"40","author":"G Antonini","year":"2006","unstructured":"Antonini, G., et al.: Discrete choice models of pedestrian walking behavior. Transport. Res. B 40(8), 667\u2013687 (2006)","journal-title":"Transport. Res. B"},{"key":"19_CR28","unstructured":"Bahdanau, D., et al.: Neural machine translation by jointly learning to align and trans-late. In: 3rd International Conference on Learning Representations (2015)"},{"key":"19_CR29","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., et al.: Crowds by example. Comput. Graphics Forum. 26, 655\u2013664 (2007)","journal-title":"Comput. Graphics Forum."},{"key":"19_CR30","doi-asserted-by":"crossref","unstructured":"Helbing, D., Moln\u00e1r, P.: Social force model for pedestrian dynamics. Phys. Rev. E, Stat. Phys. Plasmas Fluids Relat. Interdiscip. Top. 51(5), 4282 (1995)","DOI":"10.1103\/PhysRevE.51.4282"},{"key":"19_CR31","doi-asserted-by":"crossref","unstructured":"Yi, S., Li, H., Wang, X.: Understanding pedestrian behaviors from stationary crowd groups. In: Proceedings of IEEE Conference Computer Vision and Pattern Recognition (CVPR), pp. 3488\u20133496 (2015)","DOI":"10.1109\/CVPR.2015.7298971"},{"key":"19_CR32","doi-asserted-by":"crossref","unstructured":"Xue, H., Huynh, D.Q., Reynolds, M.: SS-LSTM: a hierarchical LSTM model for pedestrian trajectory prediction. In: Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1186\u20131194 (2018)","DOI":"10.1109\/WACV.2018.00135"},{"key":"19_CR33","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"19_CR34","doi-asserted-by":"crossref","unstructured":"Mohamed, A., Qian, K., Elhoseiny, M., Claudel, C.: Social-STGCNN: a social spatio-temporal graph convolutional neural network for human trajectory prediction. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.01443"},{"key":"19_CR35","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":"19_CR36","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1007\/978-3-642-15549-9_33","volume-title":"Computer Vision \u2013 ECCV 2010","author":"S Pellegrini","year":"2010","unstructured":"Pellegrini, S., Ess, A., Van Gool, L.: Improving data association by joint modeling of pedestrian trajectories and groupings. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 452\u2013465. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15549-9_33"},{"key":"19_CR37","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":"19_CR38","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.neucom.2022.03.051","volume":"491","author":"H Tang","year":"2022","unstructured":"Tang, H., Wei, P., Li, J., Zheng, N.: EvoSTGAT: evolving spatio-temporal graph attention networks for pedestrian trajectory prediction. Neurocomputing 491, 333\u2013342 (2022)","journal-title":"Neurocomputing"},{"key":"19_CR39","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\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1349\u20131358 (2019)","DOI":"10.1109\/CVPR.2019.00144"},{"key":"19_CR40","unstructured":"Danihelka, I., Wayne, G., Uria, B., Kalchbrenner, N., Graves, A.: Associative long short-term memory. In: Proceedings of The 33rd International Conference on Machine Learning (2016)"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8141-0_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T15:39:30Z","timestamp":1710344370000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8141-0_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,26]]},"ISBN":["9789819981403","9789819981410"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8141-0_19","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,26]]},"assertion":[{"value":"26 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","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":"650","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":"51% - 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":"4.14","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":"2.46","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)"}}]}}