{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T05:44:40Z","timestamp":1775886280639,"version":"3.50.1"},"reference-count":46,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3407955","type":"journal-article","created":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T17:38:39Z","timestamp":1717177119000},"page":"123985-123994","source":"Crossref","is-referenced-by-count":6,"title":["RAOD: A Benchmark for Road Abandoned Object Detection From Video Surveillance"],"prefix":"10.1109","volume":"12","author":[{"given":"Yajun","family":"Xu","sequence":"first","affiliation":[{"name":"AI Innovation Center, China Unicom, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6697-3220","authenticated-orcid":false,"given":"Huan","family":"Hu","sequence":"additional","affiliation":[{"name":"AI Innovation Center, China Unicom, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoya","family":"Zhu","sequence":"additional","affiliation":[{"name":"AI Innovation Center, China Unicom, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yibing","family":"Nan","sequence":"additional","affiliation":[{"name":"AI Innovation Center, China Unicom, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1171-0281","authenticated-orcid":false,"given":"Kai","family":"Wang","sequence":"additional","affiliation":[{"name":"AI Innovation Center, China Unicom, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1267-0277","authenticated-orcid":false,"given":"Zhaoxiang","family":"Liu","sequence":"additional","affiliation":[{"name":"AI Innovation Center, China Unicom, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiguo","family":"Lian","sequence":"additional","affiliation":[{"name":"AI Innovation Center, China Unicom, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00141"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1177\/0278364913491297"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.534"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2016.7759186"},{"key":"ref6","article-title":"SegmentMeIfYouCan: A benchmark for anomaly segmentation","author":"Chan","year":"2021","journal-title":"arXiv:2104.14812"},{"key":"ref7","article-title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks","volume-title":"Advances in Neural Information Processing Systems","volume":"31","author":"Lee","year":"2018"},{"key":"ref8","article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Hendrycks"},{"key":"ref9","article-title":"Efficacy of pixel-level OOD detection for semantic segmentation","author":"Angus","year":"2019","journal-title":"arXiv:1911.02897"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-22796-8_28"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00167"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01664"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00224"},{"key":"ref14","article-title":"Detecting road obstacles by erasing them","author":"Lis","year":"2020","journal-title":"arXiv:2012.13633"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1965\/1\/012141"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.37965\/jait.2020.0027"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.79"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICPCA.2011.6106489"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01231-1_25"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00294"},{"key":"ref21","article-title":"Scaling out-of-distribution detection for real-world settings","volume-title":"Proc. ICML","author":"Hendrycks"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00764-5_51"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2017.8019550"},{"key":"ref25","article-title":"Abandoned object detection algorithm based on improved of YOLOv2 network","author":"Zhang","year":"2018","journal-title":"J. Zhejiang Sci-Tech Univ., Natural Sci. Ed."},{"key":"ref26","article-title":"YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles","author":"Benjumea","year":"2021","journal-title":"arXiv:2112.11798"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.3390\/s20082238"},{"key":"ref28","volume-title":"YOLOv5 by Ultralytics","author":"Jocher","year":"2020"},{"key":"ref29","volume-title":"Ultralytics YOLO","author":"Jocher","year":"2023"},{"key":"ref30","article-title":"YOLOv9: Learning what you want to learn using programmable gradient information","author":"Wang","year":"2024","journal-title":"arXiv:2402.13616"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-022-06980-6"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","article-title":"MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation","volume":"121","author":"Ibtehaz","year":"2020","journal-title":"Neural Netw."},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.3233\/xst-200650"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2010.2065803"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2015.2408263"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00013"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1992.4.3.448"},{"key":"ref41","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4612-0745-0","volume-title":"Bayesian Learning for Neural Networks","author":"Neal","year":"1996"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045502"},{"key":"ref43","article-title":"Bayesian SegNet: Model uncertainty in deep convolutional encoder\u2013decoder architectures for scene understanding","author":"Kendall","year":"2015","journal-title":"arXiv:1511.02680"},{"key":"ref44","first-page":"1","article-title":"What uncertainties do we need in Bayesian deep learning for computer vision?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Kendall"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2015.7225680"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00508"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10380310\/10542978.pdf?arnumber=10542978","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T17:59:00Z","timestamp":1726163940000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10542978\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":46,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3407955","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}