{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T08:17:28Z","timestamp":1775031448194,"version":"3.50.1"},"reference-count":27,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T00:00:00Z","timestamp":1617235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T00:00:00Z","timestamp":1617235200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T00:00:00Z","timestamp":1617235200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Aarhus University"},{"name":"Department of Electrical, and Computer Engineering","award":["28173"],"award-info":[{"award-number":["28173"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Robot. Autom. Lett."],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1109\/lra.2021.3057003","type":"journal-article","created":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T21:29:10Z","timestamp":1612387750000},"page":"1638-1645","source":"Crossref","is-referenced-by-count":23,"title":["GridNet: Image-Agnostic Conditional Anomaly Detection for Indoor Surveillance"],"prefix":"10.1109","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5244-9078","authenticated-orcid":false,"given":"Ilker","family":"Bozcan","sequence":"first","affiliation":[]},{"given":"Jonas","family":"Le Fevre","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8218-9326","authenticated-orcid":false,"given":"Huy X.","family":"Pham","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7143-8777","authenticated-orcid":false,"given":"Erdal","family":"Kayacan","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00179"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s11265-014-0913-0"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.45"},{"key":"ref13","first-page":"622","article-title":"GANomaly: Semi-supervised anomaly detection via adversarial training","author":"akcay","year":"2018","journal-title":"Proc Asian Conf Comput Vis"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00088"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2018.12.009"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5539872"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206569"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8460828"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.391"},{"key":"ref4","article-title":"Deep learning for anomaly detection: A review","author":"pang","year":"2020"},{"key":"ref27","first-page":"371","article-title":"ALET: A dataset, a baseline and a usecase for tool detection in the wild","author":"kurnaz","year":"2020","journal-title":"ECCV"},{"key":"ref3","article-title":"Auto-encoding variational bayes","author":"kingma","year":"0","journal-title":"Proc 2nd Int Conf Learn Represen"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6247917"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00320"},{"key":"ref8","first-page":"4393","article-title":"Deep one-class classification","author":"ruff","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.5244\/C.29.8"},{"key":"ref2","first-page":"1158","article-title":"UAV-ADnet: Unsupervised anomaly detection using deep neural networks for aerial surveillance","author":"bozcan","year":"0","journal-title":"Proc IEEE\/RSJ Int Conf Intell Robots Syst"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.86"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541882"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA40945.2020.9196845"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/2689746.2689747"},{"key":"ref21","article-title":"Tutorial: Deriving the standard variational autoencoder (VAE) loss function","author":"odaibo","year":"2019"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-67361-5_40"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"}],"container-title":["IEEE Robotics and Automation Letters"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7083369\/9285111\/09345954.pdf?arnumber=9345954","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:54:30Z","timestamp":1652194470000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9345954\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4]]},"references-count":27,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/lra.2021.3057003","relation":{},"ISSN":["2377-3766","2377-3774"],"issn-type":[{"value":"2377-3766","type":"electronic"},{"value":"2377-3774","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4]]}}}