{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:24:30Z","timestamp":1777652670448,"version":"3.51.4"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"5-6","license":[{"start":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:00:00Z","timestamp":1731369600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:00:00Z","timestamp":1731369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176067"],"award-info":[{"award-number":["62176067"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2023B0303020001"],"award-info":[{"award-number":["2023B0303020001"]}]},{"name":"Scientific and Technological Planning Project of Guangzhou","award":["202103000040, 2023B03J1378"],"award-info":[{"award-number":["202103000040, 2023B03J1378"]}]},{"name":"Research project of Guangdong Polytechnic Normal University","award":["22GPNUZDJS14"],"award-info":[{"award-number":["22GPNUZDJS14"]}]},{"name":"Joint Foundation of Basic and Applied Basic Research of Guangdong Province","award":["2020A1515111162"],"award-info":[{"award-number":["2020A1515111162"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s13042-024-02437-4","type":"journal-article","created":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T17:10:35Z","timestamp":1731431435000},"page":"3085-3096","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Attention-addressing and adaptive-intention-clustering based memory recall for pedestrian trajectory prediction"],"prefix":"10.1007","volume":"16","author":[{"given":"Xu","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuecai","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,12]]},"reference":[{"key":"2437_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2021.103453","volume":"134","author":"A Kalatian","year":"2022","unstructured":"Kalatian A, Farooq B (2022) A context-aware pedestrian trajectory prediction framework for automated vehicles. Transp Res Part C Emerg Technol 134:103453","journal-title":"Transp Res Part C Emerg Technol"},{"key":"2437_CR2","doi-asserted-by":"publisher","first-page":"126172","DOI":"10.1109\/ACCESS.2021.3111917","volume":"9","author":"R Akabane","year":"2021","unstructured":"Akabane R, Kato Y (2021) Pedestrian trajectory prediction based on transfer learning for human-following mobile robots. IEEE Access 9:126172\u2013126185","journal-title":"IEEE Access"},{"issue":"22","key":"2437_CR3","doi-asserted-by":"publisher","first-page":"7543","DOI":"10.3390\/s21227543","volume":"21","author":"BI Sighencea","year":"2021","unstructured":"Sighencea BI, Stanciu RI, C\u0103leanu CD (2021) A review of deep learning-based methods for pedestrian trajectory prediction. Sensors 21(22):7543","journal-title":"Sensors"},{"key":"2437_CR4","doi-asserted-by":"crossref","unstructured":"Yu C, Ma X, Ren J, Zhao H, Yi S (2020) Spatio-temporal graph transformer networks for pedestrian trajectory prediction. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XII 16, p. 507\u2013523. Springer","DOI":"10.1007\/978-3-030-58610-2_30"},{"key":"2437_CR5","doi-asserted-by":"crossref","unstructured":"Hu Y, Chen S, Zhang Y, Gu X (2020) Collaborative motion prediction via neural motion message passing. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, p. 6319\u20136328","DOI":"10.1109\/CVPR42600.2020.00635"},{"key":"2437_CR6","doi-asserted-by":"crossref","unstructured":"Yuan Y, Weng X, Ou Y, Kitani Kris\u00a0M (2021) Agentformer: Agent-aware transformers for socio-temporal multi-agent forecasting. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, p. 9813\u20139823","DOI":"10.1109\/ICCV48922.2021.00967"},{"key":"2437_CR7","doi-asserted-by":"crossref","unstructured":"Salzmann T, Ivanovic B, Chakravarty P, Pavone M (2020) Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XVIII 16, p. 683\u2013700. Springer","DOI":"10.1007\/978-3-030-58523-5_40"},{"key":"2437_CR8","first-page":"19783","volume":"33","author":"J Li","year":"2020","unstructured":"Li J, Yang F, Tomizuka M, Choi C (2020) Evolvegraph: Multi-agent trajectory prediction with dynamic relational reasoning. Adv Neural Inf Process Syst 33:19783\u201319794","journal-title":"Adv Neural Inf Process Syst"},{"key":"2437_CR9","doi-asserted-by":"publisher","first-page":"11989","DOI":"10.1109\/TNNLS.2023.3250485","volume":"35","author":"P Lv","year":"2023","unstructured":"Lv P, Wang W, Wang Y, Zhang Y, Xu M, Xu C (2023) Ssagcn: social soft attention graph convolution network for pedestrian trajectory prediction. IEEE Trans Neural Netw Learn Syst 35:11989\u201312003","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"5","key":"2437_CR10","doi-asserted-by":"publisher","first-page":"1803","DOI":"10.1109\/TITS.2018.2836305","volume":"20","author":"RQ M\u00ednguez","year":"2018","unstructured":"M\u00ednguez RQ, Alonso IP, Fern\u00e1ndez-Llorca D, Sotelo MA (2018) Pedestrian path, pose, and intention prediction through gaussian process dynamical models and pedestrian activity recognition. IEEE Trans Intell Transp Syst 20(5):1803\u20131814","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"2437_CR11","doi-asserted-by":"crossref","unstructured":"Schneider N, Gavrila Dariu\u00a0M (2013) Pedestrian path prediction with recursive bayesian filters: A comparative study. In: German conference on pattern recognition, p. 174\u2013183. Springer","DOI":"10.1007\/978-3-642-40602-7_18"},{"key":"2437_CR12","doi-asserted-by":"crossref","unstructured":"Wang B, Hu Y, Shou G, Guo Z (2016) Trajectory prediction in campus based on markov chains. In Big Data Computing and Communications: Second International Conference, BigCom 2016, Shenyang, China, July 29\u201331, 2016. Proceedings 2, p. 145\u2013154. Springer","DOI":"10.1007\/978-3-319-42553-5_13"},{"key":"2437_CR13","doi-asserted-by":"crossref","unstructured":"Kooij Julian Francisco\u00a0P, Schneider N, Flohr F, Gavrila Dariu\u00a0M (2014) Context-based pedestrian path prediction. In: Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6\u201312, 2014, Proceedings, Part VI 13, p. 618\u2013633. Springer","DOI":"10.1007\/978-3-319-10599-4_40"},{"key":"2437_CR14","unstructured":"Saadatnejad S, Gao Y, Messaoud K, Alahi A (2023) Social-transmotion: Promptable human trajectory prediction. arXiv preprint arXiv:2312.16168"},{"key":"2437_CR15","doi-asserted-by":"crossref","unstructured":"Yue J, Manocha D, Wang H (2022) Human trajectory prediction via neural social physics. In: European conference on computer vision, p. 376\u2013394. Springer","DOI":"10.1007\/978-3-031-19830-4_22"},{"key":"2437_CR16","doi-asserted-by":"crossref","unstructured":"Xu C, Li M, Ni Z, Zhang Y, Chen S (2022) Groupnet: Multiscale hypergraph neural networks for trajectory prediction with relational reasoning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, p. 6498\u20136507","DOI":"10.1109\/CVPR52688.2022.00639"},{"key":"2437_CR17","unstructured":"Yue J, Manocha D, Wang H (2023) Human trajectory forecasting with explainable behavioral uncertainty. arXiv preprint arXiv:2307.01817"},{"key":"2437_CR18","doi-asserted-by":"crossref","unstructured":"Xu C, Mao W, Zhang W, Chen S (2022) Remember intentions: Retrospective-memory-based trajectory prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, p. 6488\u20136497","DOI":"10.1109\/CVPR52688.2022.00639"},{"issue":"8","key":"2437_CR19","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 (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"2437_CR20","doi-asserted-by":"crossref","unstructured":"Dendorfer P, Elflein S, Leal-Taix\u00e9 L (2021) Mg-gan: A multi-generator model preventing out-of-distribution samples in pedestrian trajectory prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, p. 13158\u201313167","DOI":"10.1109\/ICCV48922.2021.01291"},{"key":"2437_CR21","doi-asserted-by":"crossref","unstructured":"Shi L, Wang L, Long C, Zhou S, Zhou M, Niu Z, Hua G (2021) Sgcn: Sparse graph convolution network for pedestrian trajectory prediction. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, p. 8994\u20139003","DOI":"10.1109\/CVPR46437.2021.00888"},{"key":"2437_CR22","volume-title":"Algorithms for clustering data","author":"K Jain Anil","year":"1988","unstructured":"Jain Anil K, Dubes Richard C (1988) Algorithms for clustering data. Prentice-Hall, Inc., Upper Saddle River"},{"key":"2437_CR23","unstructured":"MacQueen James et\u00a0al (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol.\u00a01, p. 281\u2013297. Oakland, CA, USA"},{"issue":"430","key":"2437_CR24","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1080\/01621459.1995.10476572","volume":"90","author":"RE Kass","year":"1995","unstructured":"Kass RE, Raftery AE (1995) Bayes factors. J Am Stat Assoc 90(430):773\u2013795","journal-title":"J Am Stat Assoc"},{"issue":"3","key":"2437_CR25","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/BF02294361","volume":"52","author":"H Bozdogan","year":"1987","unstructured":"Bozdogan H (1987) Model selection and akaike\u2019s information criterion (aic): The general theory and its analytical extensions. Psychometrika 52(3):345\u2013370","journal-title":"Psychometrika"},{"key":"2437_CR26","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1109\/TPAMI.1979.4766909","volume":"2","author":"DL Davies","year":"1979","unstructured":"Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 2:224\u2013227","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2437_CR27","doi-asserted-by":"crossref","unstructured":"Cho K, Van\u00a0Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078","DOI":"10.3115\/v1\/D14-1179"},{"key":"2437_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110022","volume":"258","author":"B Yang","year":"2022","unstructured":"Yang B, Fan F, Ni R, Li J, Kiong Loochu, Liu Xiaofeng (2022) Continual learning-based trajectory prediction with memory augmented networks. Knowl-Based Syst 258:110022","journal-title":"Knowl-Based Syst"},{"key":"2437_CR29","unstructured":"Graves A, Wayne G, Danihelka I (2014) Neural turing machines. arXiv preprint arXiv:1410.5401"},{"key":"2437_CR30","unstructured":"Santoro A, Bartunov S, Botvinick M, Wierstra D, Lillicrap T (2016) Meta-learning with memory-augmented neural networks. In: International conference on machine learning, p. 1842\u20131850. PMLR"},{"key":"2437_CR31","doi-asserted-by":"crossref","unstructured":"Goldhammer M, Doll K, Brunsmann U, Gensler A, Sick B (2014) Pedestrian\u2019s trajectory forecast in public traffic with artificial neural networks. In: 2014 22nd international conference on pattern recognition, p. 4110\u20134115. IEEE","DOI":"10.1109\/ICPR.2014.704"},{"key":"2437_CR32","unstructured":"Sukhbaatar S, Weston J, Fergus R, et\u00a0al (2015) End-to-end memory networks. In: Advances in neural information processing systems, 28"},{"key":"2437_CR33","unstructured":"Kumar A, Irsoy O, Ondruska P, Iyyer M, Bradbury J, Gulrajani I, Zhong V, Paulus R, Socher R (2016) Ask me anything: Dynamic memory networks for natural language processing. In: International conference on machine learning, p. 1378\u20131387. PMLR"},{"key":"2437_CR34","doi-asserted-by":"crossref","unstructured":"Ma C, Shen C, Dick A, Wu Q, Wang P, Van\u00a0den Hengel A, Reid I (2018) Visual question answering with memory-augmented networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, p. 6975\u20136984","DOI":"10.1109\/CVPR.2018.00729"},{"key":"2437_CR35","doi-asserted-by":"crossref","unstructured":"Rebuffi S-A, Kolesnikov A, Sperl G, Lampert Christoph\u00a0H (2017) icarl: Incremental classifier and representation learning. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, p. 2001\u20132010","DOI":"10.1109\/CVPR.2017.587"},{"issue":"4","key":"2437_CR36","doi-asserted-by":"publisher","first-page":"8410","DOI":"10.1109\/LRA.2021.3104334","volume":"6","author":"H Ma","year":"2021","unstructured":"Ma H, Sun Y, Li J, Tomizuka M, Choi Chiho (2021) Continual multi-agent interaction behavior prediction with conditional generative memory. IEEE Robot Autom Lett 6(4):8410\u20138417","journal-title":"IEEE Robot Autom Lett"},{"key":"2437_CR37","doi-asserted-by":"crossref","unstructured":"Mao W, Xu C, Zhu Q, Chen S, Wang Y (2023) Leapfrog diffusion model for stochastic trajectory prediction. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, p. 5517\u20135526","DOI":"10.1109\/CVPR52729.2023.00534"},{"key":"2437_CR38","unstructured":"Mercurius Ray\u00a0C, Ahmadi E, Shabestary Soheil Mohamad\u00a0A, Rasouli A (2024) Amend: A mixture of experts framework for long-tailed trajectory prediction. arXiv preprint arXiv:2402.08698"},{"key":"2437_CR39","doi-asserted-by":"crossref","unstructured":"Pellegrini S, Ess A, Schindler K, Van\u00a0Gool L (2009) You\u2019ll never walk alone: Modeling social behavior for multi-target tracking. In: 2009 IEEE 12th international conference on computer vision, p. 261\u2013268. IEEE","DOI":"10.1109\/ICCV.2009.5459260"},{"key":"2437_CR40","doi-asserted-by":"crossref","unstructured":"Lerner A, Chrysanthou Y, Lischinski D (2007) Crowds by example. In: Computer graphics forum, vol.\u00a026, p. 655\u2013664. Wiley Online Library","DOI":"10.1111\/j.1467-8659.2007.01089.x"},{"key":"2437_CR41","doi-asserted-by":"crossref","unstructured":"Marchetti F, Becattini F, Seidenari L, Bimbo Alberto\u00a0D (2020) Mantra: Memory augmented networks for multiple trajectory prediction. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, p. 7143\u20137152","DOI":"10.1109\/CVPR42600.2020.00717"},{"key":"2437_CR42","doi-asserted-by":"crossref","unstructured":"Mangalam K, Girase H, Agarwal S, Lee K-H, Adeli E, Malik J, Gaidon A (2020) It is not the journey but the destination: Endpoint conditioned trajectory prediction. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part II 16, p. 759\u2013776. Springer","DOI":"10.1007\/978-3-030-58536-5_45"},{"key":"2437_CR43","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-024-02258-5","author":"W Chen","year":"2024","unstructured":"Chen W, Sang H, Wang J, Zhao Z (2024) Wtgcn: wavelet transform graph convolution network for pedestrian trajectory prediction. Int J Mach Learn Cybern. https:\/\/doi.org\/10.1007\/s13042-024-02258-5","journal-title":"Int J Mach Learn Cybern"},{"key":"2437_CR44","doi-asserted-by":"crossref","unstructured":"Gupta A, Johnson J, Fei-Fei L, Savarese S, Alahi A (2018) Social gan: Socially acceptable trajectories with generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, p. 2255\u20132264","DOI":"10.1109\/CVPR.2018.00240"},{"key":"2437_CR45","doi-asserted-by":"crossref","unstructured":"Huang Y, Bi H, Li Z, Mao T, Wang Z (2019) Stgat: Modeling spatial-temporal interactions for human trajectory prediction. In: Proceedings of the IEEE\/CVF international conference on computer vision, p. 6272\u20136281","DOI":"10.1109\/ICCV.2019.00637"},{"key":"2437_CR46","doi-asserted-by":"crossref","unstructured":"Giuliari F, Hasan I, Cristani M, Galasso F (2021) Transformer networks for trajectory forecasting. In: 2020 25th international conference on pattern recognition (ICPR), p. 10335\u201310342. IEEE","DOI":"10.1109\/ICPR48806.2021.9412190"},{"issue":"12","key":"2437_CR47","doi-asserted-by":"publisher","first-page":"4193","DOI":"10.1007\/s13042-023-01889-4","volume":"14","author":"X Yang","year":"2023","unstructured":"Yang X, Fan J, Xing S (2023) Ist-ptepn: an improved pedestrian trajectory and endpoint prediction network based on spatio-temporal information. Int J Mach Learn Cybern 14(12):4193\u20134206","journal-title":"Int J Mach Learn Cybern"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02437-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02437-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02437-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T04:30:31Z","timestamp":1749270631000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02437-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,12]]},"references-count":47,"journal-issue":{"issue":"5-6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["2437"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02437-4","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,12]]},"assertion":[{"value":"6 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}