{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T09:10:31Z","timestamp":1762938631346,"version":"3.45.0"},"reference-count":38,"publisher":"Wiley","issue":"25-26","license":[{"start":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T00:00:00Z","timestamp":1759017600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2025,11,30]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Video anomaly detection (VAD), a critical task in intelligent surveillance systems, faces two key challenges: Dynamic behavioral characterization under complex scenarios and robust spatiotemporal context modeling. Existing methods face limitations, such as inadequate cross\u2010scale feature fusion, weak channel\u2010wise dependency modeling, and sensitivity to background noise. To address these issues, we propose a novel multi\u2010scale spatiotemporal feature augmentation framework. Our approach introduces three core innovations: Hierarchical feature pyramid architecture for multi\u2010granularity representation learning, capturing both local motion patterns and global scene semantics; A channel\u2010adaptive attention mechanism that dynamically models long\u2010range spatiotemporal dependencies; A spatiotemporal Gaussian difference module to enhance anomaly response through frequency\u2010domain feature reconstruction, effectively suppressing noise interference. Extensive experiments on UCSD Ped1\/2, CUHK Avenue, and ShanghaiTech benchmarks demonstrate that our method achieves state\u2010of\u2010the\u2010art performance, outperforming existing approaches in both accuracy and robustness.<\/jats:p>","DOI":"10.1002\/cpe.70315","type":"journal-article","created":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T01:33:49Z","timestamp":1759109629000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross\u2010Scale Spatiotemporal Memory\u2010Augmented Network for Unsupervised Video Anomaly Detection"],"prefix":"10.1002","volume":"37","author":[{"given":"Lihu","family":"Pan","sequence":"first","affiliation":[{"name":"Department of Computing Science and Technology Taiyuan University of Science and Technology  Taiyuan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8117-8264","authenticated-orcid":false,"given":"Bingyi","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computing Science and Technology Taiyuan University of Science and Technology  Taiyuan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shouxin","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Computing Science and Technology Taiyuan University of Science and Technology  Taiyuan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computing Science and Technology Taiyuan University of Science and Technology  Taiyuan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linliang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanxi Province Intelligent Transportation Research Institute Co Ltd.  Taiyuan China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,9,28]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00268"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2022.104467"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICME57554.2024.10688326"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108232"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.45"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3237028"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3074805"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01227"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01402"},{"key":"e_1_2_10_11_1","first-page":"2634","volume-title":"Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV)","author":"Deng H.","year":"2023"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CCWC51732.2021.9375909"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108336"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116394"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11760-020-01740-1"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2023.103860"},{"key":"e_1_2_10_17_1","first-page":"1","article-title":"Attention\u2010Based Residual Autoencoder for Video Anomaly Detection","volume":"53","author":"Le V. T.","year":"2022","journal-title":"Applied Intelligence"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.3389\/frsc.2023.1197434"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.07.058"},{"key":"e_1_2_10_20_1","doi-asserted-by":"crossref","unstructured":"N. C.Ristea F. A.Croitoru R.Tudor Ionescu M.Popescu F.Shahbaz Khan andM.Shah \u201cSelf\u2010Distilled Masked Auto\u2010Encoders are Efficient Video Anomaly Detectors \u201d arXiv e\u2010prints arXiv:2306.12041(2023) https:\/\/doi.org\/10.48550\/arXiv.2306.12041.","DOI":"10.1109\/CVPR52733.2024.01513"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2019.11.024"},{"volume-title":"Proceedings of the 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","year":"2024","author":"Zhang M.","key":"e_1_2_10_22_1"},{"volume-title":"Proceedings of the European Conference on Computer Vision","year":"2025","author":"Nie Y.","key":"e_1_2_10_23_1"},{"key":"e_1_2_10_24_1","doi-asserted-by":"crossref","unstructured":"N. C.Ristea F. A.Croitoru R. T.Ionescu M.Popescu F. S.Khan andM.Shah \u201cSelf\u2010Distilled Masked Auto\u2010Encoders are Efficient Video Anomaly Detectors \u201d(2024).","DOI":"10.1109\/CVPR52733.2024.01513"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.683"},{"key":"e_1_2_10_26_1","first-page":"1395","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Li S.","year":"2022"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206569"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5539872"},{"key":"e_1_2_10_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.315"},{"key":"e_1_2_10_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.86"},{"volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","year":"2020","author":"Pang G.","key":"e_1_2_10_31_1"},{"key":"e_1_2_10_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00179"},{"key":"e_1_2_10_33_1","first-page":"14372","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Park H.","year":"2020"},{"volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","year":"2018","author":"Liu W.","key":"e_1_2_10_34_1"},{"key":"e_1_2_10_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2024.01.004"},{"key":"e_1_2_10_36_1","first-page":"14183","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Zaheer M. Z.","year":"2020"},{"key":"e_1_2_10_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108213"},{"key":"e_1_2_10_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.127444"},{"key":"e_1_2_10_39_1","first-page":"6793","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)","author":"Al\u2010Lahham A.","year":"2024"}],"container-title":["Concurrency and Computation: Practice and Experience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cpe.70315","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T09:05:55Z","timestamp":1762938355000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/cpe.70315"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,28]]},"references-count":38,"journal-issue":{"issue":"25-26","published-print":{"date-parts":[[2025,11,30]]}},"alternative-id":["10.1002\/cpe.70315"],"URL":"https:\/\/doi.org\/10.1002\/cpe.70315","archive":["Portico"],"relation":{},"ISSN":["1532-0626","1532-0634"],"issn-type":[{"type":"print","value":"1532-0626"},{"type":"electronic","value":"1532-0634"}],"subject":[],"published":{"date-parts":[[2025,9,28]]},"assertion":[{"value":"2025-05-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-11","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70315"}}