{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:03:07Z","timestamp":1742918587809,"version":"3.40.3"},"publisher-location":"Cham","reference-count":50,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031198328"},{"type":"electronic","value":"9783031198335"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-19833-5_11","type":"book-chapter","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T00:40:30Z","timestamp":1667522430000},"page":"177-194","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Deep Moving-Camera Background Model"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4545-6664","authenticated-orcid":false,"given":"Guy","family":"Erez","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4579-0678","authenticated-orcid":false,"given":"Ron Shapira","family":"Weber","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9816-9709","authenticated-orcid":false,"given":"Oren","family":"Freifeld","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,4]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: An unsupervised learning model for deformable medical image registration. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00964"},{"key":"11_CR2","unstructured":"Ball\u00e9, J., Laparra, V., Simoncelli, E.P.: End-to-end optimized image compression. In: ICLR (2017)"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Balzano, L., Nowak, R., Recht, B.: Online identification and tracking of subspaces from highly incomplete information. In: Allerton (2010)","DOI":"10.1109\/ALLERTON.2010.5706976"},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Berger, M., Seversky, L.M.: Subspace tracking under dynamic dimensionality for online background subtraction. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.166"},{"key":"11_CR5","doi-asserted-by":"publisher","unstructured":"Black, M.J., Rangarajan, A.: On the unification of line processes, outlier rejection, and robust statistics with applications in early vision. IJCV (1996). https:\/\/doi.org\/10.1007\/BF00131148","DOI":"10.1007\/BF00131148"},{"key":"11_CR6","doi-asserted-by":"publisher","unstructured":"Brown, M., Lowe, D.G.: Automatic panoramic image stitching using invariant features. In: IJCV (2007). https:\/\/doi.org\/10.1007\/s11263-006-0002-3","DOI":"10.1007\/s11263-006-0002-3"},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Cand\u00e8s, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? JACM (2011)","DOI":"10.1145\/1970392.1970395"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Chakraborty, R., Hauberg, S., Vemuri, B.C.: Intrinsic Grassmann averages for online linear and robust subspace learning. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.92"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Chau, G., Rodr\u00edguez, P.: Panning and jitter invariant incremental principal component pursuit for video background modeling. In: ICCV (2017)","DOI":"10.1109\/ICCVW.2017.218"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Chelly, I., Winter, V., Litvak, D., Rosen, D., Freifeld, O.: JA-POLS: a moving-camera background model via joint alignment and partially-overlapping local subspaces. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01260"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Cox, M., Sridharan, S., Lucey, S., Cohn, J.: Least squares congealing for unsupervised alignment of images. In: CVPR (2008)","DOI":"10.1109\/CVPR.2008.4587573"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Cox, M., Sridharan, S., Lucey, S., Cohn, J.: Least-squares congealing for large numbers of images. In: ICCV (2009)","DOI":"10.1109\/ICCV.2009.5459430"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Cuevas, C., Mohedano, R., Garc\u00eda, N.: Statistical moving object detection for mobile devices with camera. In: ICCE (2015)","DOI":"10.1109\/ICCE.2015.7066301"},{"key":"11_CR14","unstructured":"Dalca, A., Rakic, M., Guttag, J., Sabuncu, M.: Learning conditional deformable templates with convolutional networks. In: NeurIPS (2019)"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Freifeld, O., Hauberg, S., Batmanghelich, K., Fisher III, J.W.: Highly-expressive spaces of well-behaved transformations: keeping it simple. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.333"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Freifeld, O., Hauberg, S., Batmanghelich, K., Fisher III, J.W.: Transformations based on continuous piecewise-affine velocity fields. IEEE TPAMI (2017)","DOI":"10.1109\/TPAMI.2016.2646685"},{"key":"11_CR17","unstructured":"Geman, S., McClure, D.E.: Statistical methods for tomographic image reconstruction. In: BISI (1987)"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Gilman, K., Balzano, L.: Panoramic video separation with online Grassmannian robust subspace estimation. In: ICCV Workshops (2019)","DOI":"10.1109\/ICCVW.2019.00078"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Guo, H., Qiu, C., Vaswani, N.: Practical reprocs for separating sparse and low-dimensional signal sequences from their sum-part 1. In: ICASSP (2014)","DOI":"10.1109\/ICASSP.2014.6854385"},{"key":"11_CR20","doi-asserted-by":"publisher","unstructured":"Guyon, C., Bouwmans, T., Zahzah, E.H.: Foreground detection via robust low rank matrix decomposition including spatio-temporal constraint. In: ACCV (2012). https:\/\/doi.org\/10.1007\/978-3-642-37410-4_28","DOI":"10.1007\/978-3-642-37410-4_28"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Hauberg, S., Feragen, A., Black, M.J.: Grassmann averages for scalable robust PCA. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.481"},{"key":"11_CR22","unstructured":"He, J., Balzano, L., Szlam, A.: Incremental gradient on the Grassmannian for online foreground and background separation in subsampled video. In: CVPR (2012)"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"He, J., Zhang, D., Balzano, L., Tao, T.: Iterative Grassmannian optimization for robust image alignment. Image Vis. Comput. 32, 800\u2013813 (2014)","DOI":"10.1016\/j.imavis.2014.02.015"},{"key":"11_CR24","unstructured":"Huang, G., Mattar, M., Lee, H., Learned-Miller, E.G.: Learning to align from scratch. In: NIPS (2012)"},{"key":"11_CR25","doi-asserted-by":"crossref","unstructured":"Huang, G.B., Jain, V., Learned-Miller, E.: Unsupervised joint alignment of complex images. In: ICCV (2007)","DOI":"10.1109\/ICCV.2007.4408858"},{"key":"11_CR26","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: NeurIPS (2015)"},{"key":"11_CR27","unstructured":"Jin, Y., Tao, L., Di, H., Rao, N.I., Xu, G.: Background modeling from a free-moving camera by multi-layer homography algorithm. In: ICIP (2008)"},{"key":"11_CR28","doi-asserted-by":"crossref","unstructured":"Kaufman, I., Weber, R.S., Freifeld, O.: Cyclic diffeomorphic transformer nets for contour alignment. In: ICIP (2021)","DOI":"10.1109\/ICIP42928.2021.9506570"},{"key":"11_CR29","doi-asserted-by":"crossref","unstructured":"Kendall, A., Grimes, M., Cipolla, R.: PoseNet: a convolutional network for real-time 6-DOF camera relocalization. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.336"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: International Symposium on Mixed and Augmented Reality (2007)","DOI":"10.1109\/ISMAR.2007.4538852"},{"key":"11_CR31","doi-asserted-by":"crossref","unstructured":"Learned-Miller, E.G.: Data driven image models through continuous joint alignment. IEEE TPAMI (2006)","DOI":"10.1109\/TPAMI.2006.34"},{"key":"11_CR32","doi-asserted-by":"publisher","unstructured":"Meneghetti, G., Danelljan, M., Felsberg, M., Nordberg, K.: Image alignment for panorama stitching in sparsely structured environments. In: Scandinavian Conference on Image Analysis (2015). https:\/\/doi.org\/10.1007\/978-3-319-19665-7_36","DOI":"10.1007\/978-3-319-19665-7_36"},{"key":"11_CR33","unstructured":"Miller, E.G., Matsakis, N.E., Viola, P.A.: Learning from one example through shared densities on transforms. In: CVPR (2000)"},{"key":"11_CR34","doi-asserted-by":"crossref","unstructured":"Moore, B.E., Gao, C., Nadakuditi, R.R.: Panoramic robust PCA for foreground-background separation on noisy, free-motion camera video. IEEE Trans. Comput. Imaging 5, 195\u2013211 (2019)","DOI":"10.1109\/TCI.2019.2891389"},{"key":"11_CR35","doi-asserted-by":"crossref","unstructured":"Newcombe, R.A., Lovegrove, S.J., Davison, A.J.: DTAM: dense tracking and mapping in real-time. In: ICCV (2011)","DOI":"10.1109\/ICCV.2011.6126513"},{"key":"11_CR36","unstructured":"Pont-Tuset, J., Perazzi, F., Caelles, S., Arbel\u00e1ez, P., Sorkine-Hornung, A., Van Gool, L.: The 2017 DAVIS challenge on video object segmentation. arXiv preprint arXiv:1704.00675 (2017)"},{"key":"11_CR37","doi-asserted-by":"crossref","unstructured":"Rosen, D.M., Carlone, L., Bandeira, A.S., Leonard, J.J.: SE-Sync: a certifiably correct algorithm for synchronization over the special Euclidean group. Int. J. Robot. Res. 38, 95\u2013125 (2019)","DOI":"10.1177\/0278364918784361"},{"key":"11_CR38","unstructured":"Weber, R.S., Eyal, M., Detlefsen, N.S., Shriki, O., Freifeld, O.: Diffeomorphic temporal alignment nets. In: NeurIPS (2019)"},{"key":"11_CR39","doi-asserted-by":"crossref","unstructured":"Sheikh, Y., Javed, O., Kanade, T.: Background subtraction for freely moving cameras. In: ICCV (2009)","DOI":"10.1109\/ICCV.2009.5459334"},{"key":"11_CR40","doi-asserted-by":"crossref","unstructured":"Detlefsen, N.S., Freifeld, O., Hauberg, S.: Deep diffeomorphic transformer networks. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00463"},{"key":"11_CR41","unstructured":"Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: CVPR (1999)"},{"key":"11_CR42","doi-asserted-by":"publisher","unstructured":"Szeliski, R.: Computer vision: algorithms and applications. Springer Science & Business Media (2010). https:\/\/doi.org\/10.1007\/978-1-84882-935-0","DOI":"10.1007\/978-1-84882-935-0"},{"key":"11_CR43","doi-asserted-by":"crossref","unstructured":"Thurnhofer-Hemsi, K., L\u00f3pez-Rubio, E., Dom\u00ednguez, E., Luque-Baena, R.M., Molina-Cabello, M.A.: Panoramic background modeling for PTZ cameras with competitive learning neural networks. In: IJCNN (2017)","DOI":"10.1109\/IJCNN.2017.7965881"},{"key":"11_CR44","doi-asserted-by":"crossref","unstructured":"De la Torre, F., Black, M.J.: Robust principal component analysis for computer vision. In: ICCV (2001)","DOI":"10.1007\/3-540-47979-1_44"},{"key":"11_CR45","doi-asserted-by":"crossref","unstructured":"Wang, Y., Jodoin, P.M., Porikli, F., Konrad, J., Benezeth, Y., Ishwar, P.: CDnet 2014: an expanded change detection benchmark dataset. In: CVPR Workshop (2014)","DOI":"10.1109\/CVPRW.2014.126"},{"key":"11_CR46","doi-asserted-by":"crossref","unstructured":"Wu, C.: Towards linear-time incremental structure from motion. In: International Conference on 3D Vision (2013)","DOI":"10.1109\/3DV.2013.25"},{"key":"11_CR47","doi-asserted-by":"crossref","unstructured":"Xue, K., Liu, Y., Chen, J., Li, Q.: Panoramic background model for PTZ camera. In: International Congress on Image and Signal Processing (2010)","DOI":"10.1109\/CISP.2010.5647998"},{"key":"11_CR48","unstructured":"Yalcin, H., Hebert, M., Collins, R., Black, M.J.: A flow-based approach to vehicle detection and background mosaicking in airborne video. In: CVPR (2005)"},{"key":"11_CR49","unstructured":"Zhou, X., Yang, C., Yu, W.: Moving object detection by detecting contiguous outliers in the low-rank representation. TPAMI (2012)"},{"key":"11_CR50","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Li, X., Wright, J., Candes, E., Ma, Y.: Stable principal component pursuit. In: ISIT (2010)","DOI":"10.1109\/ISIT.2010.5513535"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19833-5_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T06:23:31Z","timestamp":1728282211000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19833-5_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198328","9783031198335"],"references-count":50,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19833-5_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"4 November 2022","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)"}}]}}