{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:37:09Z","timestamp":1742913429460,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030616083"},{"type":"electronic","value":"9783030616090"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-61609-0_19","type":"book-chapter","created":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T19:02:59Z","timestamp":1603134179000},"page":"235-246","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GTFNet: Ground Truth Fitting Network for Crowd Counting"],"prefix":"10.1007","author":[{"given":"Jinghan","family":"Tan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Sang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhili","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofeng","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,14]]},"reference":[{"key":"19_CR1","doi-asserted-by":"crossref","unstructured":"Beibei, Z.: Crowd analysis: a survey. Mach. Vis. Appl. 19(5\u20136), 345\u2013357 (2008)","DOI":"10.1007\/s00138-008-0132-4"},{"key":"19_CR2","unstructured":"Teng, L.: Crowded scene analysis: a survey. IEEE Trans. Circuits Syst. Video Technol. 25(3), 367\u2013386 (2015)"},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Dalal, N.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), vol. 1, pp. 886\u2013893. IEEE (2005)","DOI":"10.1109\/CVPR.2005.177"},{"issue":"9","key":"19_CR4","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2009","unstructured":"Felzenszwalb, P.F.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627\u20131645 (2009)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"7","key":"19_CR5","doi-asserted-by":"publisher","first-page":"1198","DOI":"10.1109\/TPAMI.2007.70770","volume":"30","author":"T Zhao","year":"2008","unstructured":"Zhao, T.: Segmentation and tracking of multiple humans in crowded environments. IEEE Trans. Pattern Anal. Mach. Intell. 30(7), 1198\u20131211 (2008)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19_CR6","doi-asserted-by":"crossref","unstructured":"Rodriguez, M.: Density-aware person detection and tracking in crowds. In: 2011 International Conference on Computer Vision, pp. 2423\u20132430. IEEE (2011)","DOI":"10.1109\/ICCV.2011.6126526"},{"key":"19_CR7","doi-asserted-by":"crossref","unstructured":"Wang, M.: Automatic adaptation of a generic pedestrian detector to a specific traffic scene. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201911), vol. 7, pp. 3401\u20133408. IEEE (2011)","DOI":"10.1109\/CVPR.2011.5995698"},{"key":"19_CR8","unstructured":"Wu, B.: Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors. In: Tenth IEEE International Conference on Computer Vision (ICCV\u201905), vol. 1, pp. 90\u201397. IEEE (2005)"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Zhang, C.: Cross-scene crowd counting via deep convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 833\u2013841. IEEE (2015)","DOI":"10.1109\/CVPR.2016.70"},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"Szegedy, C.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139. IEEE (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"19_CR11","doi-asserted-by":"crossref","unstructured":"Szegedy, C.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826. IEEE (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"19_CR12","doi-asserted-by":"crossref","unstructured":"He, K.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778. IEEE (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"19_CR13","doi-asserted-by":"crossref","unstructured":"Zhang, 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. IEEE (2016)","DOI":"10.1109\/CVPR.2016.70"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Li, Y.: 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. IEEE (2018)","DOI":"10.1109\/CVPR.2018.00120"},{"key":"19_CR15","unstructured":"Simonyan, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556"},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Yu, F.: Dilated residual networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 472\u2013480. IEEE (2017)","DOI":"10.1109\/CVPR.2017.75"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Jiang, X.: Crowd counting and density estimation by trellis encoder-decoder networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6133\u20136142. IEEE (2019)","DOI":"10.1109\/CVPR.2019.00629"},{"key":"19_CR18","doi-asserted-by":"crossref","unstructured":"Idrees, H.: 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. IEEE (2013)","DOI":"10.1109\/CVPR.2013.329"},{"key":"19_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1007\/978-3-030-01216-8_33","volume-title":"Computer Vision \u2013 ECCV 2018","author":"H Idrees","year":"2018","unstructured":"Idrees, H., et al.: Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 544\u2013559. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01216-8_33"},{"key":"19_CR20","unstructured":"Yosinski, J.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, pp. 3320\u20133328. MIT Press (2014)"},{"key":"19_CR21","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G.: Pytorch: tensors and dynamic neural networks in python with strong gpu acceleration. PyTorch: tensors and dynamic neural networks in Python with strong GPU acceleration 6 (2017)"},{"key":"19_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1007\/978-3-030-01228-1_45","volume-title":"Computer Vision \u2013 ECCV 2018","author":"X Cao","year":"2018","unstructured":"Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11209, pp. 757\u2013773. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01228-1_45"},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Wang, Q.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8198\u20138207. IEEE (2019)","DOI":"10.1109\/CVPR.2019.00839"},{"key":"19_CR24","doi-asserted-by":"crossref","unstructured":"Liu, N.: Adcrowdnet: an attention-injective deformable convolutional network for crowd understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3225\u20133234. IEEE (2019)","DOI":"10.1109\/CVPR.2019.00334"},{"key":"19_CR25","unstructured":"Shi, M.: Perspective-aware CNN for crowd counting (2018)"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-61609-0_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T03:18:36Z","timestamp":1723778316000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-61609-0_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030616083","9783030616090"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-61609-0_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"14 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bratislava","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovakia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2020\/","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":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"249","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":"139","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":"56% - 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":"2.5","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":"*The conference was postponed to 2021 due to the COVID-19 pandemic.","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)"}}]}}