{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T11:35:46Z","timestamp":1779276946454,"version":"3.51.4"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T00:00:00Z","timestamp":1749427200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T00:00:00Z","timestamp":1749427200000},"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":["SIViP"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s11760-025-04299-x","type":"journal-article","created":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T12:43:17Z","timestamp":1749472997000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Global Grouped Coordinate Attention for Transformer in Pedestrian Trajectory Prediction"],"prefix":"10.1007","volume":"19","author":[{"given":"Jiashuai","family":"Xiao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zilong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,9]]},"reference":[{"key":"4299_CR1","unstructured":"Dosovitskiy, A.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"4299_CR2","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: European Conference on Computer Vision, pp. 213\u2013229 (2020). Springer","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"4299_CR3","doi-asserted-by":"crossref","unstructured":"Pellegrini, S., Ess, A., Schindler, K., Van\u00a0Gool, 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 (2009). IEEE","DOI":"10.1109\/ICCV.2009.5459260"},{"key":"4299_CR4","doi-asserted-by":"crossref","unstructured":"Lerner, A., Chrysanthou, Y., Lischinski, D.: Crowds by example. In: Computer Graphics Forum, vol. 26, pp. 655\u2013664 (2007). Wiley Online Library","DOI":"10.1111\/j.1467-8659.2007.01089.x"},{"key":"4299_CR5","doi-asserted-by":"crossref","unstructured":"Robicquet, A., Sadeghian, A., Alahi, A., Savarese, S.: Learning social etiquette: Human trajectory understanding in crowded scenes. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VIII 14, pp. 549\u2013565 (2016). Springer","DOI":"10.1007\/978-3-319-46484-8_33"},{"issue":"5","key":"4299_CR6","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"},{"issue":"5","key":"4299_CR7","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1109\/MSP.2012.2203621","volume":"29","author":"R Faragher","year":"2012","unstructured":"Faragher, R.: Understanding the basis of the kalman filter via a simple and intuitive derivation [lecture notes]. IEEE Signal Process. Mag. 29(5), 128\u2013132 (2012)","journal-title":"IEEE Signal Process. Mag."},{"key":"4299_CR8","doi-asserted-by":"crossref","unstructured":"Xu, P., Hayet, J.-B., Karamouzas, I.: Socialvae: Human trajectory prediction using timewise latents. In: European Conference on Computer Vision, pp. 511\u2013528 (2022). Springer","DOI":"10.1007\/978-3-031-19772-7_30"},{"key":"4299_CR9","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":"4299_CR10","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":"4299_CR11","doi-asserted-by":"crossref","unstructured":"Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)","DOI":"10.24963\/ijcai.2018\/505"},{"key":"4299_CR12","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1609\/aaai.v33i01.3301922","volume":"33","author":"S Guo","year":"2019","unstructured":"Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 33, 922\u2013929 (2019)","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"4299_CR13","doi-asserted-by":"crossref","unstructured":"Shi, L., Wang, L., Zhou, S., Hua, G.: Trajectory unified transformer for pedestrian trajectory prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9675\u20139684 (2023)","DOI":"10.1109\/ICCV51070.2023.00887"},{"key":"4299_CR14","unstructured":"Vaswani, A.: Attention is all you need. Advances in Neural Information Processing Systems (2017)"},{"key":"4299_CR15","doi-asserted-by":"crossref","unstructured":"Ioannou, Y., Robertson, D., Cipolla, R., Criminisi, A.: Deep roots: Improving cnn efficiency with hierarchical filter groups. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1231\u20131240 (2017)","DOI":"10.1109\/CVPR.2017.633"},{"key":"4299_CR16","unstructured":"Ioffe, S.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)"},{"key":"4299_CR17","unstructured":"Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 315\u2013323 (2011). JMLR Workshop and Conference Proceedings"},{"key":"4299_CR18","doi-asserted-by":"crossref","unstructured":"Wang, M., Zhu, X., Yu, C., Li, W., Ma, Y., Jin, R., Ren, X., Ren, D., Wang, M., Yang, W.: Ganet: Goal area network for motion forecasting. In: 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 1609\u20131615 (2023). IEEE","DOI":"10.1109\/ICRA48891.2023.10160468"},{"key":"4299_CR19","doi-asserted-by":"crossref","unstructured":"Xu, C., Mao, W., Zhang, W., Chen, S.: Remember intentions: Retrospective-memory-based trajectory prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 6488\u20136497 (2022)","DOI":"10.1109\/CVPR52688.2022.00639"},{"key":"4299_CR20","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, 1349\u20131358 (2019)","DOI":"10.1109\/CVPR.2019.00144"},{"key":"4299_CR21","doi-asserted-by":"crossref","unstructured":"Yu, C., Ma, X., Ren, J., Zhao, H., Yi, S.: 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, 507\u2013523 (2020). Springer","DOI":"10.1007\/978-3-030-58610-2_30"},{"key":"4299_CR22","doi-asserted-by":"publisher","first-page":"2235","DOI":"10.1609\/aaai.v36i2.20121","volume":"36","author":"L Shi","year":"2022","unstructured":"Shi, L., Wang, L., Long, C., Zhou, S., Zheng, F., Zheng, N., Hua, G.: Social interpretable tree for pedestrian trajectory prediction. Proceedings of the AAAI Conference on Artificial Intelligence 36, 2235\u20132243 (2022)","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"4299_CR23","doi-asserted-by":"crossref","unstructured":"Shi, L., Wang, L., Long, C., Zhou, S., Zhou, M., Niu, Z., Hua, G.: Sgcn: Sparse graph convolution network for pedestrian trajectory prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 8994\u20139003 (2021)","DOI":"10.1109\/CVPR46437.2021.00888"},{"key":"4299_CR24","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1609\/aaai.v36i1.19933","volume":"36","author":"J Duan","year":"2022","unstructured":"Duan, J., Wang, L., Long, C., Zhou, S., Zheng, F., Shi, L., Hua, G.: Complementary attention gated network for pedestrian trajectory prediction. Proceedings of the AAAI Conference on Artificial Intelligence 36, 542\u2013550 (2022)","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"4299_CR25","doi-asserted-by":"crossref","unstructured":"Mangalam, K., Girase, H., Agarwal, S., Lee, K.-H., Adeli, E., Malik, J., Gaidon, A.: 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, pp. 759\u2013776 (2020). Springer","DOI":"10.1007\/978-3-030-58536-5_45"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04299-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-04299-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04299-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T14:52:18Z","timestamp":1751554338000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-04299-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,9]]},"references-count":25,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["4299"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-04299-x","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,9]]},"assertion":[{"value":"18 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 May 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"714"}}