{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T15:27:51Z","timestamp":1785857271476,"version":"3.56.0"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031250552","type":"print"},{"value":"9783031250569","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-25056-9_42","type":"book-chapter","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T12:09:56Z","timestamp":1676376596000},"page":"663-679","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["MCIP: Multi-Stream Network for\u00a0Pedestrian Crossing Intention Prediction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9438-5460","authenticated-orcid":false,"given":"Je-Seok","family":"Ham","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1285-8384","authenticated-orcid":false,"given":"Kangmin","family":"Bae","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6616-824X","authenticated-orcid":false,"given":"Jinyoung","family":"Moon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,2,15]]},"reference":[{"key":"42_CR1","doi-asserted-by":"crossref","unstructured":"Bhattacharyya, A., Reino, D.O., Fritz, M., Schiele, B.: Euro-PVI: pedestrian vehicle interactions in dense urban centers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.00634"},{"key":"42_CR2","unstructured":"Bouhsain, S.A., Saadatnejad, S., Alahi, A.: Pedestrian intention prediction: a multi-task perspective. ArXiv preprint arXiv:2010.10270 (2020)"},{"key":"42_CR3","doi-asserted-by":"crossref","unstructured":"Braun, M., Krebs, S., Flohr, F., Gavrila, D.M.: Eurocity persons: a novel benchmark for person detection in traffic scenes. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2019)","DOI":"10.1109\/TPAMI.2019.2897684"},{"key":"42_CR4","unstructured":"Cai, Z., Vasconcelos, N.: Cascade R-CNN: high quality object detection and instance segmentation. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2019)"},{"key":"42_CR5","unstructured":"Cao, Z., Hidalgo Martinez, G., Simon, T., Wei, S., Sheikh, Y.A.: OpenPose: realtime multi-person 2D pose estimation using part affinity fields. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2019)"},{"key":"42_CR6","doi-asserted-by":"crossref","unstructured":"Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.143"},{"key":"42_CR7","doi-asserted-by":"crossref","unstructured":"Dendorfer, P., Elflein, S., Leal-Taix\u00e9, L.: 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 (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.01291"},{"key":"42_CR8","doi-asserted-by":"crossref","unstructured":"Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2011)","DOI":"10.1109\/TPAMI.2011.155"},{"key":"42_CR9","doi-asserted-by":"crossref","unstructured":"Giuliari, F., Hasan, I., Cristani, M., Galasso, F.: Transformer networks for trajectory forecasting. In: 2020 25th International Conference on Pattern Recognition (ICPR) (2021)","DOI":"10.1109\/ICPR48806.2021.9412190"},{"key":"42_CR10","doi-asserted-by":"crossref","unstructured":"Hasan, I., Liao, S., Li, J., Akram, S.U., Shao, L.: Generalizable pedestrian detection: the elephant in the room. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.01117"},{"key":"42_CR11","doi-asserted-by":"crossref","unstructured":"Khan, A.H., Munir, M., van Elst, L., Dengel, A.: F2DNet: fast focal detection network for pedestrian detection. ArXiv preprint arXiv:2203.02331 (2022)","DOI":"10.1109\/ICPR56361.2022.9956732"},{"key":"42_CR12","doi-asserted-by":"crossref","unstructured":"Kim, K., Lee, Y.K., Ahn, H., Hahn, S., Oh, S.: Pedestrian intention prediction for autonomous driving using a multiple stakeholder perspective model. In: 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (2020)","DOI":"10.1109\/IROS45743.2020.9341083"},{"key":"42_CR13","doi-asserted-by":"crossref","unstructured":"Kotseruba, I., Rasouli, A., Tsotsos, J.K.: Benchmark for evaluating pedestrian action prediction. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV) (2021)","DOI":"10.1109\/WACV48630.2021.00130"},{"key":"42_CR14","unstructured":"Lin, Z., Pei, W., Chen, F., Zhang, D., Lu, G.: Pedestrian detection by exemplar-guided contrastive learning. ArXiv preprint arXiv:2111.08974 (2021)"},{"key":"42_CR15","unstructured":"Liu, B., et al.: Spatiotemporal relationship reasoning for pedestrian intent prediction. IEEE Robot. Autom. Lett. (RA-L) PP(99), 1 (2020)"},{"key":"42_CR16","doi-asserted-by":"crossref","unstructured":"Lorenzo, J., et al.: CAPformer: pedestrian crossing action prediction using transformer. Sensors 21(17), 5694 (2021)","DOI":"10.3390\/s21175694"},{"key":"42_CR17","unstructured":"Lorenzo, J., Parra, I., Sotelo, M.: IntFormer: predicting pedestrian intention with the aid of the transformer architecture. ArXiv preprint arXiv:2105.08647 (2021)"},{"key":"42_CR18","doi-asserted-by":"crossref","unstructured":"Lorenzo, J., Parra, I., Wirth, F., Stiller, C., Llorca, D.F., Sotelo, M.A.: RNN-based pedestrian crossing prediction using activity and pose-related features. In: IEEE Intelligent Vehicles Symposium (IV) (2020)","DOI":"10.1109\/IV47402.2020.9304652"},{"key":"42_CR19","unstructured":"Lv, Z., Huang, X., Cao, W.: An improved GAN with transformers for pedestrian trajectory prediction models. Int. J. Intell. Syst. 36(12), 6989\u20137962 (2021)"},{"key":"42_CR20","doi-asserted-by":"crossref","unstructured":"Malla, S., Dariush, B., Choi, C.: Titan: future forecast using action priors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.01120"},{"key":"42_CR21","doi-asserted-by":"crossref","unstructured":"Neumann, L., Vedaldi, A.: Pedestrian and ego-vehicle trajectory prediction from monocular camera. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.01007"},{"key":"42_CR22","unstructured":"Postnikov, A., Gamayunov, A., Ferrer, G.: Transformer based trajectory prediction. ArXiv preprint arXiv:2112.04350 (2021)"},{"key":"42_CR23","unstructured":"Qingyun, F., Dapeng, H., Zhaokui, W.: Cross-modality fusion transformer for multispectral object detection. ArXiv preprint arXiv:2111.00273 (2021)"},{"key":"42_CR24","doi-asserted-by":"crossref","unstructured":"Rasouli, A., Kotseruba, I., Kunic, T., Tsotsos, J.K.: Pie: a large-scale dataset and models for pedestrian intention estimation and trajectory prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00636"},{"key":"42_CR25","doi-asserted-by":"crossref","unstructured":"Rasouli, A., Kotseruba, I., Tsotsos, J.K.: Agreeing to cross: how drivers and pedestrians communicate. In: IEEE Intelligent Vehicles Symposium (IV) (2017)","DOI":"10.1109\/IVS.2017.7995730"},{"key":"42_CR26","doi-asserted-by":"crossref","unstructured":"Rasouli, A., Kotseruba, I., Tsotsos, J.K.: Are they going to cross? a benchmark dataset and baseline for pedestrian crosswalk behavior. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW) (2017)","DOI":"10.1109\/ICCVW.2017.33"},{"key":"42_CR27","unstructured":"Rasouli, A., Kotseruba, I., Tsotsos, J.K.: Pedestrian action anticipation using contextual feature fusion in stacked RNNs. In: Proceedings of The British Machine Vision Conference (BMVC) (2019)"},{"key":"42_CR28","doi-asserted-by":"crossref","unstructured":"Razali, H., Mordan, T., Alahi, A.: Pedestrian intention prediction: a convolutional bottom-up multi-task approach. Transport. Res. Part C: Emerg. Technol. 130, 103259 (2021)","DOI":"10.1016\/j.trc.2021.103259"},{"key":"42_CR29","doi-asserted-by":"crossref","unstructured":"Shi, L., et al.: SGCN: sparse graph convolution network for pedestrian trajectory prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.00888"},{"key":"42_CR30","doi-asserted-by":"crossref","unstructured":"Simon, T., Joo, H., Matthews, I., Sheikh, Y.: Hand keypoint detection in single images using multiview bootstrapping. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.494"},{"key":"42_CR31","doi-asserted-by":"crossref","unstructured":"Sui, Z., Zhou, Y., Zhao, X., Chen, A., Ni, Y.: Joint intention and trajectory prediction based on transformer. In: 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (2021)","DOI":"10.1109\/IROS51168.2021.9636241"},{"key":"42_CR32","doi-asserted-by":"crossref","unstructured":"Wei, S.E., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.511"},{"key":"42_CR33","doi-asserted-by":"crossref","unstructured":"Yang, D., Zhang, H., Yurtsever, E., Redmill, K., Ozguner, U.: Predicting pedestrian crossing intention with feature fusion and spatio-temporal attention. IEEE Transactions on Intelligent Vehicles (T-IV) (2022)","DOI":"10.1109\/TIV.2022.3162719"},{"key":"42_CR34","doi-asserted-by":"crossref","unstructured":"Yao, H.Y., Wan, W.G., Li, X.: End-to-end pedestrian trajectory forecasting with transformer network. ISPRS Int. J. Geo-Inf. 11(1), 44 (2022)","DOI":"10.3390\/ijgi11010044"},{"key":"42_CR35","doi-asserted-by":"crossref","unstructured":"Yao, Y., Atkins, E., Johnson-Roberson, M., Vasudevan, R., Du, X.: Coupling intent and action for pedestrian crossing behavior prediction. In: Proceedings of 30th International Joint Conference on Artificial Intelligence (IJCAI) (2021)","DOI":"10.24963\/ijcai.2021\/171"},{"key":"42_CR36","doi-asserted-by":"crossref","unstructured":"Yin, Z., Liu, R., Xiong, Z., Yuan, Z.: Multimodal transformer networks for pedestrian trajectory prediction. In: Proceedings of 30th International Joint Conference on Artificial Intelligence (IJCAI) (2021)","DOI":"10.24963\/ijcai.2021\/174"},{"key":"42_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, S., Benenson, R., Schiele, B.: Citypersons: a diverse dataset for pedestrian detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.474"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25056-9_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:37:47Z","timestamp":1710268667000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25056-9_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250552","9783031250569"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25056-9_42","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"15 February 2023","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":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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":"From the workshops, 367 reviewed full papers have been selected for publication","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)"}}]}}