{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T02:01:49Z","timestamp":1776391309011,"version":"3.51.2"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T00:00:00Z","timestamp":1776384000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T00:00:00Z","timestamp":1776384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s10044-026-01670-7","type":"journal-article","created":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T01:35:47Z","timestamp":1776389747000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Video anomaly detection via video restoration based on start-end frames by recalling context"],"prefix":"10.1007","volume":"29","author":[{"given":"Yuhua","family":"Tian","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanxu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,17]]},"reference":[{"key":"1670_CR1","doi-asserted-by":"publisher","first-page":"938","DOI":"10.1609\/aaai.v35i2.16177","volume":"35","author":"R Cai","year":"2021","unstructured":"Cai R, Zhang H, Liu W, Gao S, Hao Z (2021) Appearance-motion memory consistency network for video anomaly detection. Proceedings of the AAAI Conference on Artificial Intelligence 35:938\u2013946","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"1670_CR2","doi-asserted-by":"crossref","unstructured":"Lv H, Chen C, Cui Z, Xu C, Li Y, Yang J (2021) Learning normal dynamics in videos with meta prototype network. Proc IEEE Conf Comput Vis Pattern Recognit 15425\u201315434","DOI":"10.1109\/CVPR46437.2021.01517"},{"key":"1670_CR3","doi-asserted-by":"crossref","unstructured":"Park H, Noh J, Ham B (2020) Learning memory-guided normality for anomaly detection. Proc IEEE Conf Comput Vis Pattern Recognit 14372\u201314381","DOI":"10.1109\/CVPR42600.2020.01438"},{"key":"1670_CR4","doi-asserted-by":"crossref","unstructured":"Yang Z, Wu P, Liu J, Liu X (2022) Dynamic local aggregation network with adaptive clusterer for anomaly detection. Proceedings of the European Conference on Computer Vision 404\u2013421","DOI":"10.1007\/978-3-031-19772-7_24"},{"key":"1670_CR5","doi-asserted-by":"crossref","unstructured":"Ye M, Peng X, Gan W, Wu W, Qiao Y (2019) Anopcn: Video anomaly detection via deep predictive coding network, in: Proceedings of the 27th ACM International Conference on Multimedia, 1805\u20131813","DOI":"10.1145\/3343031.3350899"},{"key":"1670_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111533","volume":"289","author":"P Tan","year":"2024","unstructured":"Tan P, Wong WK (2024) Unsupervised anomaly detection and localization with one model for all categories. Knowl-Based Syst 289:111533","journal-title":"Knowl-Based Syst"},{"key":"1670_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108336","volume":"122","author":"Y Zhong","year":"2022","unstructured":"Zhong Y, Chen X, Jiang J, Ren F (2022) A cascade reconstruction model with generalization ability evaluation for anomaly detection in videos. Pattern Recogn 122:108336","journal-title":"Pattern Recogn"},{"key":"1670_CR8","doi-asserted-by":"crossref","unstructured":"Yang Z et\u00a0al. (2023) Video event restoration based on keyframes for video anomaly detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition,","DOI":"10.1109\/CVPR52729.2023.01402"},{"key":"1670_CR9","doi-asserted-by":"crossref","unstructured":"Hasan M, Choi J, Neumann J, Roy-Chowdhury AK, Davis LS (2016) Learning temporal regularity in video sequences. Proc IEEE Conf Comput Vis Pattern Recognit 733\u2013742","DOI":"10.1109\/CVPR.2016.86"},{"key":"1670_CR10","doi-asserted-by":"crossref","unstructured":"Liu W, Luo W, Lian D, Gao S (2018) Future frame prediction for anomaly detection - a new baseline. Proc IEEE Conf Comput Vis Pattern Recognit 6536\u20136545","DOI":"10.1109\/CVPR.2018.00684"},{"key":"1670_CR11","doi-asserted-by":"crossref","unstructured":"Ladune T, Philippe P (2022) Aivc: Artificial intelligence based video codec, arXiv preprint arXiv:2202.04365","DOI":"10.1109\/ICIP46576.2022.9897240"},{"key":"1670_CR12","doi-asserted-by":"crossref","unstructured":"Liu G, Zhao J (2010) Key frame extraction from mpeg video stream. Third International Symposium on Information Processing 423\u2013427","DOI":"10.1109\/ISIP.2010.63"},{"issue":"3","key":"1670_CR13","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1109\/TPAMI.2007.70825","volume":"30","author":"A Adam","year":"2008","unstructured":"Adam A, Rivlin E, Shimshoni I, Reinitz D (2008) Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Trans Pattern Anal Mach Intell 30(3):555\u2013560","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1670_CR14","unstructured":"Zhang H, Kamath G, Kulkarni J, Wu ZS (2020) Privately learning markov random fields, in: Proceedings of the 37th International Conference on Machine Learning, PMLR 119,"},{"key":"1670_CR15","doi-asserted-by":"crossref","unstructured":"Cong Y, Yuan J, Liu J (2011) Sparse reconstruction cost for abnormal event detection. Proc IEEE Conf Comput Vis Pattern Recognit 3449\u20133456","DOI":"10.1109\/CVPR.2011.5995434"},{"issue":"6","key":"1670_CR16","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390","journal-title":"Commun ACM"},{"issue":"1","key":"1670_CR17","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1109\/TCSVT.2020.2974877","volume":"31","author":"X Nie","year":"2020","unstructured":"Nie X, Wang B, Li J, Hao F, Jian M, Yin Y (2020) Deep multiscale fusion hashing for cross-modal retrieval. IEEE Trans Circuits Syst Video Technol 31(1):401\u2013410","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"1670_CR18","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. Proc IEEE Conf Comput Vis Pattern Recognit 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"1670_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108261","volume":"122","author":"F Shao","year":"2022","unstructured":"Shao F, Liu J, Wu P, Yang Z, Wu Z (2022) Exploiting foreground and background separation for prohibited item detection in overlapping x-ray images. Pattern Recogn 122:108261","journal-title":"Pattern Recogn"},{"key":"1670_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2025.129576","volume":"627","author":"M Xing","year":"2025","unstructured":"Xing M, Feng Z, Su Y, Zhang Y, Oh C, Gribova V et al (2025) Spatio-temporal graph-based self-labeling for video anomaly detection. Neurocomputing 627:129576","journal-title":"Neurocomputing"},{"key":"1670_CR21","doi-asserted-by":"crossref","unstructured":"Ma H, Sun Z, Su Y, Wang H, Li S, Yu Z et al (2022) Cross-modal two-stream target focused network for video anomaly detection. International Conference in Communications Signal Processing and Systems 69\u201378","DOI":"10.1007\/978-981-99-2653-4_9"},{"key":"1670_CR22","doi-asserted-by":"crossref","unstructured":"Liu Z, Nie Y, Long C, Zhang Q, Li G (2021) A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction. Proceedings of the IEEE International Conference on Computer Vision (ICCV)","DOI":"10.1109\/ICCV48922.2021.01333"},{"key":"1670_CR23","doi-asserted-by":"crossref","unstructured":"Nguyen T-N, Meunier J (2019) Anomaly detection in video sequence with appearance-motion correspondence, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV),","DOI":"10.1109\/ICCV.2019.00136"},{"key":"1670_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124846","volume":"256","author":"A Hussain","year":"2024","unstructured":"Hussain A, Ullah W, Khan N, Khan ZA, Kim MJ, Baik SW (2024) Tds-net: Transformer enhanced dual-stream network for video anomaly detection. Expert Syst Appl 256:124846","journal-title":"Expert Syst Appl"},{"key":"1670_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.111936","volume":"160","author":"A Hussain","year":"2025","unstructured":"Hussain A, Khan N, Khan ZA, Yar H, Baik SW (2025) Edge-assisted framework for instant anomaly detection and cloud-based anomaly recognition in smart surveillance. Eng Appl Artif Intell 160:111936","journal-title":"Eng Appl Artif Intell"},{"key":"1670_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2025.112064","volume":"170","author":"A Hussain","year":"2026","unstructured":"Hussain A, Ullah W, Khan N, Khan ZA, Yar H, Baik SW (2026) Class-incremental learning network for real-time anomaly recognition in surveillance environments. Pattern Recogn 170:112064","journal-title":"Pattern Recogn"},{"key":"1670_CR27","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1016\/j.neunet.2023.11.049","volume":"170","author":"W Zhang","year":"2024","unstructured":"Zhang W, Zhao W, Li J, Zhuang P, Sun H, Xu Y, Li C (2024) Cvanet: Cascaded visual attention network for single image super-resolution. Neural Netw 170:622\u2013634","journal-title":"Neural Netw"},{"key":"1670_CR28","doi-asserted-by":"crossref","unstructured":"Haris M, Shakhnarovich G, Ukita N (2020) Space-time-aware multi-resolution video enhancement. Proc IEEE Conf Comput Vis Pattern Recognit 2859\u20132868","DOI":"10.1109\/CVPR42600.2020.00293"},{"key":"1670_CR29","doi-asserted-by":"crossref","unstructured":"Kim TH, Lee KM, Scholkopf B, Hirsch M (2017) Online video deblurring via dynamic temporal blending network. Proceedings of the IEEE International Conference on Computer Vision 4038\u20134047","DOI":"10.1109\/ICCV.2017.435"},{"key":"1670_CR30","doi-asserted-by":"crossref","unstructured":"Sheth DY, Mohan S, Vincent JL, Manzorro R, Crozier PA, Khapra MM, Simoncelli EP, Fernandez-Granda C (2021) Unsupervised deep video denoising. Proceedings of the IEEE International Conference on Computer Vision 1759\u20131768","DOI":"10.1109\/ICCV48922.2021.00178"},{"key":"1670_CR31","doi-asserted-by":"crossref","unstructured":"Zeng Y, Lin Z, Yang J, Zhang J, Shechtman E, Lu H (2020) High-resolution image inpainting with iterative confidence feedback and guided upsampling, arXiv:2005.11742","DOI":"10.1007\/978-3-030-58529-7_1"},{"key":"1670_CR32","doi-asserted-by":"crossref","unstructured":"Liao L, Xiao J, Wang Z, Lin C-W, Satoh S (2020) Guidance and evaluation: Semantic-aware image inpainting for mixed scenes, in: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXVII, Springer International Publishing, 683\u2013700","DOI":"10.1007\/978-3-030-58583-9_41"},{"key":"1670_CR33","doi-asserted-by":"crossref","unstructured":"Zhu L, Yang Y (2020) Inflated episodic memory with region self-attention for long-tailed visual recognition. Conference on Computer Vision and Pattern Recognition (CVPR) 4344\u20134353","DOI":"10.1109\/CVPR42600.2020.00440"},{"key":"1670_CR34","doi-asserted-by":"crossref","unstructured":"Lee S, Sung J, Yu Y, Kim G (2018) A memory network approach for story-based temporal summarization of 360 videos. Conference on Computer Vision and Pattern Recognition (CVPR) 1410\u20131419","DOI":"10.1109\/CVPR.2018.00153"},{"key":"1670_CR35","doi-asserted-by":"crossref","unstructured":"Zhu M, Pan P, Chen W, Yang Y (2019) Dm-gan: Dynamic memory generative adversarial networks for text-to-image synthesis. Conference on Computer Vision and Pattern Recognition (CVPR) 5802\u20135810","DOI":"10.1109\/CVPR.2019.00595"},{"key":"1670_CR36","doi-asserted-by":"crossref","unstructured":"Gong D, Liu L, Le V, Saha B, Mansour MR, Venkatesh S, van\u00a0den Hengel A (2019) Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection, arXiv:1904.02639","DOI":"10.1109\/ICCV.2019.00179"},{"key":"1670_CR37","doi-asserted-by":"crossref","unstructured":"Liu Z, Nie Y, Long C, Zhang Q, Li G (2021) A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction, arXiv:2108.06852","DOI":"10.1109\/ICCV48922.2021.01333"},{"key":"1670_CR38","doi-asserted-by":"crossref","unstructured":"Sun S, Gong X (2023) Hierarchical semantic contrast for scene-aware video anomaly detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition,","DOI":"10.1109\/CVPR52729.2023.02188"},{"issue":"9","key":"1670_CR39","doi-asserted-by":"publisher","first-page":"7046","DOI":"10.1109\/TII.2025.3574406","volume":"21","author":"Y Su","year":"2025","unstructured":"Su Y, Li J, An S, Xu H, Peng W (2025) Dual-detector reoptimization for federated weakly supervised video anomaly detection via adaptive dynamic recursive mapping. IEEE Trans Industr Inf 21(9):7046\u20137056","journal-title":"IEEE Trans Industr Inf"},{"key":"1670_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110898","volume":"157","author":"Y Su","year":"2025","unstructured":"Su Y, Tan Y, An S, Xing M, Feng Z (2025) Semantic-driven dual consistency learning for weakly supervised video anomaly detection. Pattern Recogn 157:110898","journal-title":"Pattern Recogn"},{"key":"1670_CR41","unstructured":"Redmon J, Farhadi A (2018) Yolov3: An incremental improvement, arXiv preprint arXiv:1804.02767"},{"issue":"11","key":"1670_CR42","doi-asserted-by":"publisher","first-page":"2599","DOI":"10.1109\/TPAMI.2018.2865304","volume":"41","author":"W-S Lai","year":"2018","unstructured":"Lai W-S, Huang J-B, Ahuja N, Yang M (2018) Fast and accurate image super-resolution with deep laplacian pyramid networks. IEEE Trans Pattern Anal Mach Intell 41(11):2599\u20132613","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1670_CR43","doi-asserted-by":"crossref","unstructured":"Mahadevan V, Li W, Bhalodia V, Vasconcelos N (2010) Anomaly detection in crowded scenes. IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1975\u20131981","DOI":"10.1109\/CVPR.2010.5539872"},{"key":"1670_CR44","doi-asserted-by":"crossref","unstructured":"Lu C, Shi J, Jia J (2013) Abnormal event detection at 150 fps in matlab. Proceedings of the IEEE International Conference on Computer Vision 2720\u20132727","DOI":"10.1109\/ICCV.2013.338"},{"key":"1670_CR45","doi-asserted-by":"crossref","unstructured":"Luo W, Liu W, Gao S (2017) A revisit of sparse coding based anomaly detection in stacked rnn framework. Proceedings of the IEEE International Conference on Computer Vision 341\u2013349","DOI":"10.1109\/ICCV.2017.45"},{"issue":"7","key":"1670_CR46","first-page":"2609","volume":"31","author":"P Wu","year":"2019","unstructured":"Wu P, Liu J, Shen F (2019) A deep one-class neural network for anomalous event detection in complex scenes. IEEE Transactions on Neural Networks and Learning Systems 31(7):2609\u20132622","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"1670_CR47","doi-asserted-by":"crossref","unstructured":"Morais R, Le V, Tran T-T, Saha B, Mansour M, Venkatesh S (2019) Learning regularity in skeleton trajectories for anomaly detection in videos. Proc IEEE Conf Comput Vis Pattern Recognit 11996\u201312004","DOI":"10.1109\/CVPR.2019.01227"},{"key":"1670_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108213","volume":"122","author":"Y Chang","year":"2022","unstructured":"Chang Y, Tu Z, Xie W, Luo B, Zhang S, Sui H, Yuan J (2022) Video anomaly detection with spatio-temporal dissociation. Pattern Recogn 122:108213","journal-title":"Pattern Recogn"},{"key":"1670_CR49","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.patrec.2019.11.024","volume":"129","author":"Y Tang","year":"2020","unstructured":"Tang Y, Zhao L, Zhang S, Gong C, Li G, Yang J (2020) Integrating prediction and reconstruction for anomaly detection. Pattern Recogn Lett 129:123\u2013130","journal-title":"Pattern Recogn Lett"},{"key":"1670_CR50","doi-asserted-by":"crossref","unstructured":"Luo W, Liu W, Gao S (2017) Remembering history with convolutional lstm for anomaly detection. Proceedings of the IEEE International Conference on Multimedia and Expo 439\u2013444","DOI":"10.1109\/ICME.2017.8019325"},{"key":"1670_CR51","doi-asserted-by":"crossref","unstructured":"Chang Y, Tu Z, Xie W, Yuan J (2020) Clustering driven deep autoencoder for video anomaly detection. Proceedings of the European Conference on Computer Vision 329\u2013345","DOI":"10.1007\/978-3-030-58555-6_20"},{"issue":"6","key":"1670_CR52","doi-asserted-by":"publisher","first-page":"2301","DOI":"10.1109\/TNNLS.2021.3083152","volume":"33","author":"X Wang","year":"2021","unstructured":"Wang X, Che Z, Jiang B, Xiao N, Yang K, Tang J et al (2021) Robust unsupervised video anomaly detection by multipath frame prediction. IEEE Transactions on Neural Networks and Learning Systems 33(6):2301\u20132312","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-026-01670-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10044-026-01670-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-026-01670-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T01:36:05Z","timestamp":1776389765000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10044-026-01670-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,17]]},"references-count":52,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["1670"],"URL":"https:\/\/doi.org\/10.1007\/s10044-026-01670-7","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"value":"1433-7541","type":"print"},{"value":"1433-755X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,17]]},"assertion":[{"value":"15 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"86"}}