{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:30:57Z","timestamp":1742913057037,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031500770"},{"type":"electronic","value":"9783031500787"}],"license":[{"start":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T00:00:00Z","timestamp":1703376000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T00:00:00Z","timestamp":1703376000000},"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\/978-3-031-50078-7_9","type":"book-chapter","created":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T11:01:59Z","timestamp":1703329319000},"page":"106-119","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-Modal Information Aggregation and\u00a0Distribution Method for\u00a0Crowd Counting"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3988-1821","authenticated-orcid":false,"given":"Yin","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0731-274X","authenticated-orcid":false,"given":"Yuhao","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianyang","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,24]]},"reference":[{"key":"9_CR1","unstructured":"Lempitsky, V., Zisserman, A.: Learning to count objects in images. In: Advances in Neural Information Processing Systems, vol. 23 (2010)"},{"key":"9_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/978-3-030-88004-0_17","volume-title":"Pattern Recognition and Computer Vision","author":"X Chen","year":"2021","unstructured":"Chen, X., Yu, X., Di, H., Wang, S.: SA-InterNet: scale-aware interaction network for joint crowd counting and localization. In: Ma, H., et al. (eds.) PRCV 2021. LNCS, vol. 13019, pp. 203\u2013215. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-88004-0_17"},{"key":"9_CR3","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/978-3-031-20977-2_8","volume-title":"International Conference on Information, Communication and Computing Technology","author":"R Senthilkumar","year":"2022","unstructured":"Senthilkumar, R., Ritika, S., Manikandan, M., Shyam, B.: Crowd counting using federated learning and domain adaptation. In: Badica, C., Paprzycki, M., Kharb, L., Chahal, D. (eds.) ICICCT 2022. Communications in Computer and Information Science, pp. 97\u2013111. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20977-2_8"},{"issue":"1","key":"9_CR4","doi-asserted-by":"publisher","first-page":"5795","DOI":"10.1038\/s41598-022-09685-w","volume":"12","author":"N Ilyas","year":"2022","unstructured":"Ilyas, N., Ahmad, Z., Lee, B., Kim, K.: An effective modular approach for crowd counting in an image using convolutional neural networks. Sci. Rep. 12(1), 5795 (2022)","journal-title":"Sci. Rep."},{"issue":"6","key":"9_CR5","doi-asserted-by":"publisher","first-page":"2141","DOI":"10.1109\/TPAMI.2020.3013269","volume":"43","author":"Q Wang","year":"2020","unstructured":"Wang, Q., Gao, J., Lin, W., Li, X.: NWPU-crowd: a large-scale benchmark for crowd counting and localization. IEEE Trans. Pattern Anal. Mach. Intell. 43(6), 2141\u20132149 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"9_CR6","unstructured":"Zhang, C., Li, H., Wang, X., Yang, X.: Cross-scene crowd counting via deep convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 833\u2013841 (2015)"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y.: Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 589\u2013597 (2016)","DOI":"10.1109\/CVPR.2016.70"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Liu, L., Chen, J., Wu, H., Li, G., Li, C., Lin, L.: Cross-modal collaborative representation learning and a large-scale RGBT benchmark for crowd counting. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4823\u20134833 (2021)","DOI":"10.1109\/CVPR46437.2021.00479"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.429"},{"issue":"10","key":"9_CR10","doi-asserted-by":"publisher","first-page":"3513","DOI":"10.1109\/TCSVT.2019.2942970","volume":"30","author":"L Liu","year":"2019","unstructured":"Liu, L., Hao, L., Xiong, H., Xian, K., Cao, Z., Shen, C.: Counting objects by blockwise classification. IEEE Trans. Circ. Syst. Video Technol. 30(10), 3513\u20133527 (2019)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"issue":"7540","key":"9_CR11","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529\u2013533 (2015)","journal-title":"Nature"},{"issue":"7540","key":"9_CR12","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529\u2013533 (2015)","journal-title":"Nature"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547\u20132554 (2013)","DOI":"10.1109\/CVPR.2013.329"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Zhao, M., Zhang, J., Zhang, C., Zhang, W.: Leveraging heterogeneous auxiliary tasks to assist crowd counting. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12736\u201312745 (2019)","DOI":"10.1109\/CVPR.2019.01302"},{"issue":"1","key":"9_CR15","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1007\/s44196-021-00016-x","volume":"14","author":"SD Khan","year":"2021","unstructured":"Khan, S.D., Salih, Y., Zafar, B., Noorwali, A.: A deep-fusion network for crowd counting in high-density crowded scenes. Int. J. Comput. Intell. Syst. 14(1), 168 (2021)","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Sindagi, V.A., Patel, V.M.: Multi-level bottom-top and top-bottom feature fusion for crowd counting. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1002\u20131012 (2019)","DOI":"10.1109\/ICCV.2019.00109"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Sindagi, V.A., Yasarla, R., Patel, V.M.: Pushing the frontiers of unconstrained crowd counting: new dataset and benchmark method. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1221\u20131231 (2019)","DOI":"10.1109\/ICCV.2019.00131"},{"key":"9_CR18","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.neucom.2020.01.087","volume":"392","author":"Y Fang","year":"2020","unstructured":"Fang, Y., Gao, S., Li, J., Luo, W., He, L., Bo, H.: Multi-level feature fusion based locality-constrained spatial transformer network for video crowd counting. Neurocomputing 392, 98\u2013107 (2020)","journal-title":"Neurocomputing"},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Yan, Z., et al.: Perspective-guided convolution networks for crowd counting. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 952\u2013961 (2019)","DOI":"10.1109\/ICCV.2019.00104"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Ma, Z., Wei, X., Hong, X., Gong, Y.: Bayesian loss for crowd count estimation with point supervision. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6142\u20136151 (2019)","DOI":"10.1109\/ICCV.2019.00624"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Liu, N., Long, Y., Zou, C., Niu, Q., Pan, L., Wu, H.: Adcrowdnet: an attention-injective deformable convolutional network for crowd understanding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3225\u20133234 (2019)","DOI":"10.1109\/CVPR.2019.00334"},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Dai, J., et al.: Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 764\u2013773 (2017)","DOI":"10.1109\/ICCV.2017.89"},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Chan, A.B., Liang, Z.S.J., Vasconcelos, N.: Privacy preserving crowd monitoring: counting people without people models or tracking. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20137. IEEE (2008)","DOI":"10.1109\/CVPR.2008.4587569"},{"issue":"3","key":"9_CR25","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.1109\/TIP.2017.2740160","volume":"27","author":"S Huang","year":"2017","unstructured":"Huang, S., et al.: Body structure aware deep crowd counting. IEEE Trans. Image Process. 27(3), 1049\u20131059 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"9_CR26","unstructured":"Shi, M., Yang, Z., Xu, C., Chen, Q.: Perspective-aware CNN for crowd counting. PhD thesis, Inria Rennes-Bretagne Atlantique (2018)"},{"key":"9_CR27","doi-asserted-by":"crossref","unstructured":"Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734\u2013750 (2018)","DOI":"10.1007\/978-3-030-01228-1_45"},{"key":"9_CR28","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, X., Chen, D.: CSRNet: dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091\u20131100 (2018)","DOI":"10.1109\/CVPR.2018.00120"},{"key":"9_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/978-3-030-58586-0_15","volume-title":"Computer Vision \u2013 ECCV 2020","author":"X Liu","year":"2020","unstructured":"Liu, X., Yang, J., Ding, W., Wang, T., Wang, Z., Xiong, J.: Adaptive mixture regression network with local counting map for crowd counting. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12369, pp. 241\u2013257. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58586-0_15"},{"key":"9_CR30","unstructured":"Wang, B., Liu, H., Samaras, D., Nguyen, M.H.: Distribution matching for crowd counting. In: Advances in Neural Information Processing Systems, vol. 33, pp. 1595\u20131607 (2020)"}],"container-title":["Lecture Notes in Computer Science","Advances in Computer Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-50078-7_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T11:03:13Z","timestamp":1703329393000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-50078-7_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,24]]},"ISBN":["9783031500770","9783031500787"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-50078-7_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,24]]},"assertion":[{"value":"24 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CGI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Computer Graphics International Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","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":"28 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cgi2023","order":10,"name":"conference_id","label":"Conference ID","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"385","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":"149","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":"39% - 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":"3","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)"}}]}}