{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T06:42:51Z","timestamp":1774939371439,"version":"3.50.1"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Major Project of Science and Technology of Henan Province","award":["No.201400210300"],"award-info":[{"award-number":["No.201400210300"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62176087"],"award-info":[{"award-number":["No.62176087"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Promotion Projects in Henan Province","award":["No. 232102210080"],"award-info":[{"award-number":["No. 232102210080"]}]},{"name":"Henan Provincial University Key Scientific Research Project","award":["N0.24A520004"],"award-info":[{"award-number":["N0.24A520004"]}]},{"name":"Project Funded by China Postdoctoral ScienceFoundation","award":["2023M741007"],"award-info":[{"award-number":["2023M741007"]}]},{"name":"Technology Plan Project of the State Administration for Market Regulation","award":["2023MK082"],"award-info":[{"award-number":["2023MK082"]}]},{"name":"Major Project of Science and Technology of Henan Province","award":["No. 242102210079"],"award-info":[{"award-number":["No. 242102210079"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s10489-025-07065-1","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T08:25:35Z","timestamp":1771230335000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["STVAD: A Spatio-temporal Coupled Based Transformer for Unsupervised Video Anomaly Detection"],"prefix":"10.1007","volume":"56","author":[{"given":"Huiyu","family":"Mu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luhui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjian","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonggan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lanxue","family":"Dang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianyu","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,16]]},"reference":[{"key":"7065_CR1","doi-asserted-by":"publisher","first-page":"12170","DOI":"10.1109\/CVPR42600.2020.01219","volume-title":"2020 IEEE\/CVF Conference on computer vision and pattern recognition (CVPR)","author":"G Pang","year":"2020","unstructured":"Pang G, Yan C, Shen C, van den Hengel A, Bai X (2020) Self-Trained Deep Ordinal Regression for End-to-End Video Anomaly Detection. 2020 IEEE\/CVF Conference on computer vision and pattern recognition (CVPR). Seattle, WA, USA, pp 12170\u201312179"},{"key":"7065_CR2","doi-asserted-by":"crossref","unstructured":"Gaus YFA, Bhowmik N, Isaac-Medina BKS et al (2023) Region-based appearance and flow characteristics for anomaly detection in infrared surveillance imagery. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition pp 2995\u20133005","DOI":"10.1109\/CVPRW59228.2023.00301"},{"key":"7065_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2021.103249","volume":"210","author":"B Li","year":"2021","unstructured":"Li B, Leroux S, Simoens P (2021) Decoupled appearance and motion learning for efficient anomaly detection in surveillance video. Comput Vision Image Understanding 210:103249","journal-title":"Comput Vision Image Understanding"},{"issue":"17","key":"7065_CR4","doi-asserted-by":"publisher","first-page":"25875","DOI":"10.1007\/s11042-021-10921-0","volume":"80","author":"Z Aziz","year":"2021","unstructured":"Aziz Z, Bhatti N, Mahmood H et al (2021) Video anomaly detection and localization based on appearance and motion models. Multimed Tools Appl 80(17):25875\u201325895","journal-title":"Multimed Tools Appl"},{"issue":"Suppl 1","key":"7065_CR5","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1007\/s11760-024-03164-7","volume":"18","author":"K Ganagavalli","year":"2024","unstructured":"Ganagavalli K, Santhi V (2024) YOLO-based anomaly activity detection system for human behavior analysis and crime mitigation. Signal Image Video Process 18(Suppl 1):417\u2013427","journal-title":"Signal Image Video Process"},{"issue":"1","key":"7065_CR6","doi-asserted-by":"publisher","first-page":"110","DOI":"10.3390\/app7010110","volume":"7","author":"AB Sargano","year":"2017","unstructured":"Sargano AB, Angelov P, Habib Z (2017) A comprehensive review on handcrafted and learning-based action representation approaches for human activity recognition. Appl Sci 7(1):110","journal-title":"Appl Sci"},{"key":"7065_CR7","doi-asserted-by":"crossref","unstructured":"Ojha S, Sakhare S (2015) Image processing techniques for object tracking in video surveillance-A survey. Paper presented at: International Conference on Pervasive Computing (ICPC). IEEE pp 1\u20136","DOI":"10.1109\/PERVASIVE.2015.7087180"},{"key":"7065_CR8","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1007\/s10462-012-9341-3","volume":"42","author":"D Gowsikhaa","year":"2014","unstructured":"Gowsikhaa D, Abirami S, Baskaran R (2014) Automated human behavior analysis from surveillance videos: a survey. Artif Intell Rev 42:747\u2013765","journal-title":"Artif Intell Rev"},{"issue":"2","key":"7065_CR9","doi-asserted-by":"publisher","first-page":"36","DOI":"10.3390\/jimaging4020036","volume":"4","author":"BR Kiran","year":"2018","unstructured":"Kiran BR, Thomas DM, Parakkal R (2018) An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos. J Imaging 4(2):36","journal-title":"J Imaging"},{"key":"7065_CR10","doi-asserted-by":"crossref","unstructured":"Hasan M, Choi J, Neumann J, Roy-Chowdhury AK, Davis LS (2016) Learning temporal regularity in video sequences. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 733\u2013742","DOI":"10.1109\/CVPR.2016.86"},{"key":"7065_CR11","doi-asserted-by":"crossref","unstructured":"Wu S, Moore BE, Shah M (2010) Chaotic Invariants of lagrangian particle trajectories for anomaly detection in crowded scenes. In: 2010 IEEE computer society conference on computer vision and pattern recognition. IEEE, pp 2054\u20132060","DOI":"10.1109\/CVPR.2010.5539882"},{"key":"7065_CR12","doi-asserted-by":"crossref","unstructured":"Zhao B, Fei-Fei L, Xing EP (2010) Online detection of unusual events in videos via dynamic sparse coding. In: CVPR. IEEE, pp 3313\u20133320","DOI":"10.1109\/CVPR.2011.5995524"},{"key":"7065_CR13","doi-asserted-by":"crossref","unstructured":"Luo W, Liu W, Gao S (2017) A revisit of sparse coding based anomaly detection in stacked rnn framework. In: Proceedings of the IEEE international conference on computer vision, pp 341\u2013349","DOI":"10.1109\/ICCV.2017.45"},{"key":"7065_CR14","doi-asserted-by":"crossref","unstructured":"Morais R, Le V, Tran T, Saha B, Mansour M, Venkatesh S (2019) Learning regularity in skeleton trajectories for anomaly detection in videos. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11996\u201312004","DOI":"10.1109\/CVPR.2019.01227"},{"key":"7065_CR15","doi-asserted-by":"crossref","unstructured":"Vu H (2017) Deep Abnormality Detection in Video Data. In: IJCAI, pp 5217\u20135218","DOI":"10.24963\/ijcai.2017\/768"},{"key":"7065_CR16","doi-asserted-by":"crossref","unstructured":"Bergmann P, Fauser M, Sattlegger D, Steger C (2020) Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4183\u20134192","DOI":"10.1109\/CVPR42600.2020.00424"},{"key":"7065_CR17","doi-asserted-by":"crossref","unstructured":"Liu Y, Liu J, Zhao M, Yang D, Zhu X, Song L (2022) Learning appearance-motion normality for video anomaly detection. In: IEEE International conference on multimedia and expo (ICME). IEEE, pp 1\u20136","DOI":"10.1109\/ICME52920.2022.9859727"},{"key":"7065_CR18","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"},{"issue":"7","key":"7065_CR19","doi-asserted-by":"publisher","first-page":"7728","DOI":"10.1007\/s10489-022-03903-8","volume":"53","author":"H Mu","year":"2023","unstructured":"Mu H, Sun R, Chen Z, Qin J (2023) Intelligent abnormal behavior detection using double sparseness method. Appl Intell 53(7):7728\u20137740","journal-title":"Appl Intell"},{"key":"7065_CR20","doi-asserted-by":"crossref","unstructured":"Georgescu M-I, Barbalau A, Ionescu RT, Khan FS, Popescu M, Shah M (2021) Anomaly detection in video via self-supervised and multi-task learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12742\u201312752","DOI":"10.1109\/CVPR46437.2021.01255"},{"key":"7065_CR21","doi-asserted-by":"crossref","unstructured":"Chen Y, Liu Z, Zhang B et al (2023) Mgfn: Magnitude-contrastive glance-and-focus network for weakly-supervised video anomaly detection. In: Proceedings of the AAAI conference on artificial intelligence 37(1):387\u2013395","DOI":"10.1609\/aaai.v37i1.25112"},{"key":"7065_CR22","doi-asserted-by":"crossref","unstructured":"Zhou H, Yu J, Yang W (2023) Dual memory units with uncertainty regulation for weakly supervised video anomaly detection. In: Proceedings of the AAAI conference on artificial intelligence 37(3):3769\u20133777","DOI":"10.1609\/aaai.v37i3.25489"},{"issue":"8","key":"7065_CR23","doi-asserted-by":"publisher","first-page":"1082","DOI":"10.1109\/TKDE.2007.1042","volume":"20","author":"J Yin","year":"2008","unstructured":"Yin J, Yang Q, Pan JJ (2008) Sensor-based abnormal human-activity detection. IEEE Trans Know Data Eng 20(8):1082\u20131090","journal-title":"IEEE Trans Know Data Eng"},{"key":"7065_CR24","doi-asserted-by":"crossref","unstructured":"Ren H, Liu W, Olsen SI, Escalera S, Moeslund TB (2015) Unsupervised behavior-specific dictionary learning for abnormal event detection. In: British Machine Vision Conference : Machine Vision of Animals and their Behaviour, pp 28.1\u201328.13","DOI":"10.5244\/C.29.28"},{"key":"7065_CR25","doi-asserted-by":"crossref","unstructured":"Lu C, Shi J, Jia J (2013) Abnormal event detection at 150 fps in matlab. In: Proceedings of the IEEE international conference on computer vision, pp 2720\u20132727","DOI":"10.1109\/ICCV.2013.338"},{"key":"7065_CR26","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1016\/j.neucom.2014.12.064","volume":"155","author":"N Li","year":"2015","unstructured":"Li N, Wu X, Xu D, Guo H, Feng W (2015) Spatio-temporal context analysis within video volumes for anomalous-event detection and localization. Neurocomputing 155:309\u2013319","journal-title":"Neurocomputing"},{"key":"7065_CR27","doi-asserted-by":"crossref","unstructured":"Chong YS, Tay YH (2017) Abnormal event detection in videos using spatiotemporal autoencoder. In: Advances in neural networks-ISNN: 14th international symposium, ISNN. Springer, pp 189\u2013196","DOI":"10.1007\/978-3-319-59081-3_23"},{"key":"7065_CR28","doi-asserted-by":"crossref","unstructured":"Zhao Y, Deng B, Shen C, Liu Y, Lu H, Hua X (2017) Spatio-temporal autoencoder for video anomaly detection. In: Proceedings of the 25th ACM international conference on multimedia, pp 1933\u20131941","DOI":"10.1145\/3123266.3123451"},{"key":"7065_CR29","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\/CVF international conference on computer vision, pp 1273\u20131283","DOI":"10.1109\/ICCV.2019.00136"},{"key":"7065_CR30","doi-asserted-by":"crossref","unstructured":"Chang Y, Tu Z, Xie W, Yuan J (2020) Clustering driven deep autoencoder for video anomaly detection. In: Computer Vision\u2013ECCV: 16th European Conference. Springer, pp 329\u2013345","DOI":"10.1007\/978-3-030-58555-6_20"},{"key":"7065_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117030","volume":"201","author":"KV Thakare","year":"2022","unstructured":"Thakare KV, Sharma N, Dogra DP, Choi H, Kim I (2022) A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection. Expert Syst Appl 201:117030","journal-title":"Expert Syst Appl"},{"key":"7065_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108232","volume":"121","author":"Y Hao","year":"2022","unstructured":"Hao Y, Li J, Wang N, Wang X, Gao X (2022) Spatiotemporal consistency-enhanced network for video anomaly detection. Pattern Recogn 121:108232","journal-title":"Pattern Recogn"},{"issue":"6","key":"7065_CR33","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, Ye J, Wang J, Qi Q (2021) Robust unsupervised video anomaly detection by multipath frame prediction. IEEE Trans Neural Netw Learn Syst 33(6):2301\u20132312","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7065_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119079","volume":"214","author":"AM Kamoona","year":"2023","unstructured":"Kamoona AM, Gostar AK, Bab-Hadiashar A, Hoseinnezhad R (2023) Multiple instance-based video anomaly detection using deep temporal encoding\u2013decoding. Expert Syst Appl 214:119079","journal-title":"Expert Syst Appl"},{"issue":"6","key":"7065_CR35","doi-asserted-by":"publisher","first-page":"2147","DOI":"10.1109\/TCYB.2013.2242059","volume":"43","author":"M Thida","year":"2013","unstructured":"Thida M, Eng H-L, Remagnino P (2013) Laplacian eigenmap with temporal constraints for local abnormality detection in crowded scenes. IEEE Trans Cybernet 43(6):2147\u20132156","journal-title":"IEEE Trans Cybernet"},{"key":"7065_CR36","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/j.patcog.2017.01.001","volume":"65","author":"D Singh","year":"2017","unstructured":"Singh D, Mohan CK (2017) Graph formulation of video activities for abnormal activity recognition. Pattern Recognit 65:265\u2013272","journal-title":"Pattern Recognit"},{"issue":"4","key":"7065_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.102983","volume":"59","author":"H Mu","year":"2022","unstructured":"Mu H, Sun R, Wang M, Chen Z (2022) Spatio-temporal graph-based CNNs for anomaly detection in weakly-labeled videos. Inf Process Manag 59(4):102983","journal-title":"Inf Process Manag"},{"key":"7065_CR38","doi-asserted-by":"crossref","unstructured":"Ren J, Xia F, Liu Y, Lee I (2021) Deep video anomaly detection: Opportunities and challenges. In: International conference on data mining workshops (ICDMW). IEEE, pp 959\u2013966","DOI":"10.1109\/ICDMW53433.2021.00125"},{"key":"7065_CR39","doi-asserted-by":"crossref","unstructured":"Doshi K, Yilmaz Y (2023) Towards interpretable video anomaly detection. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 2655\u20132664","DOI":"10.1109\/WACV56688.2023.00268"},{"key":"7065_CR40","doi-asserted-by":"crossref","unstructured":"Acsintoae A, Florescu A, Georgescu M I et al (2022) Ubnormal: New benchmark for supervised open-set video anomaly detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition pp 20143\u201320153","DOI":"10.1109\/CVPR52688.2022.01951"},{"key":"7065_CR41","doi-asserted-by":"crossref","unstructured":"Chen W, Ma K T, Yew Z J et al (2023) TEVAD: Improved video anomaly detection with captions. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition pp 5549\u20135559","DOI":"10.1109\/CVPRW59228.2023.00587"},{"key":"7065_CR42","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv:2010.11929"},{"key":"7065_CR43","doi-asserted-by":"crossref","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: European conference on computer vision. Springer, pp 213\u2013229","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"7065_CR44","doi-asserted-by":"crossref","unstructured":"Zheng S, Lu J, Zhao H, Zhu X, Luo Z, Wang Y, Fu Y, Feng J, Xiang T, Torr P (2021) Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6881\u20136890","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"7065_CR45","doi-asserted-by":"crossref","unstructured":"Tian Y, Pang G, Chen Y, Singh R, Verjans JW, Carneiro G (2021) Weakly-supervised video anomaly detection with robust temporal feature magnitude learning. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 4975\u20134986","DOI":"10.1109\/ICCV48922.2021.00493"},{"key":"7065_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109348","volume":"252","author":"Q Li","year":"2022","unstructured":"Li Q, Yang R, Xiao F, Bhanu B, Zhang F (2022) Attention-based anomaly detection in multi-view surveillance videos. Knowl-Based Syst 252:109348","journal-title":"Knowl-Based Syst"},{"key":"7065_CR47","doi-asserted-by":"crossref","unstructured":"Park H, Noh J, Ham B (2020) Learning memory-guided normality for anomaly detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 14372\u201314381","DOI":"10.1109\/CVPR42600.2020.01438"},{"key":"7065_CR48","doi-asserted-by":"crossref","unstructured":"Arnab A, Dehghani M, Heigold G, Sun C, Lu\u010di\u0107 M, Schmid C (2021) Vivit: A video vision transformer. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6836\u20136846","DOI":"10.1109\/ICCV48922.2021.00676"},{"key":"7065_CR49","doi-asserted-by":"crossref","unstructured":"Wang X, Zhang S, Qing Z, Shao Y, Zuo Z, Gao C, Sang N (2021) Oadtr: Online action detection with transformers. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 7565\u20137575","DOI":"10.1109\/ICCV48922.2021.00747"},{"key":"7065_CR50","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.neucom.2023.02.027","volume":"532","author":"Y Wang","year":"2023","unstructured":"Wang Y, Liu T, Zhou J et al (2023) Video anomaly detection based on spatio-temporal relationships among objects. Neurocomputing 532:141\u2013151","journal-title":"Neurocomputing"},{"key":"7065_CR51","doi-asserted-by":"crossref","unstructured":"Wu P, Zhou X, Pang G et al (2024) Open-vocabulary video anomaly detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition 18297\u201318307","DOI":"10.1109\/CVPR52733.2024.01732"},{"key":"7065_CR52","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"issue":"5","key":"7065_CR53","first-page":"2498","volume":"69","author":"Y Liu","year":"2022","unstructured":"Liu Y, Liu J, Lin J, Zhao M, Song L (2022) Appearance-motion united auto-encoder framework for video anomaly detection. IEEE Trans Circuits Syst II Express Briefs 69(5):2498\u20132502","journal-title":"IEEE Trans Circuits Syst II Express Briefs"},{"key":"7065_CR54","doi-asserted-by":"crossref","unstructured":"Liu W, Luo W, Lian D, Gao S (2018) Future frame prediction for anomaly detection\u2013a new baseline. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6536\u20136545","DOI":"10.1109\/CVPR.2018.00684"},{"key":"7065_CR55","doi-asserted-by":"crossref","unstructured":"Mehran R, Oyama A, Shah M (2009) Abnormal crowd behavior detection using social force model. In: IEEE conference on computer vision and pattern recognition. IEEE, pp 935\u2013942","DOI":"10.1109\/CVPR.2009.5206641"},{"issue":"1","key":"7065_CR56","first-page":"18","volume":"36","author":"W Li","year":"2013","unstructured":"Li W, Mahadevan V, Vasconcelos N (2013) Anomaly detection and localization in crowded scenes. IEEE Trans Pattern Anal Mach Intell 36(1):18\u201332","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7065_CR57","doi-asserted-by":"crossref","unstructured":"Huang C, Wen J, Xu Y, Jiang Q, Yang J, Wang Y, Zhang D (2022) Self-supervised attentive generative adversarial networks for video anomaly detection. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2022.3159538"},{"key":"7065_CR58","doi-asserted-by":"crossref","unstructured":"Tudor Ionescu R, Smeureanu S, Alexe B, Popescu M (2017) Unmasking the abnormal events in video. In: Proceedings of the IEEE international conference on computer vision, pp 2895\u20132903","DOI":"10.1109\/ICCV.2017.315"},{"issue":"10","key":"7065_CR59","doi-asserted-by":"publisher","first-page":"2537","DOI":"10.1109\/TIFS.2019.2900907","volume":"14","author":"JT Zhou","year":"2019","unstructured":"Zhou JT, Du J, Zhu H, Peng X, Liu Y, Goh RSM (2019) Anomalynet: An anomaly detection network for video surveillance. IEEE Trans Inf Forensics Sec 14(10):2537\u20132550","journal-title":"IEEE Trans Inf Forensics Sec"},{"issue":"7","key":"7065_CR60","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 Trans Neural Netw Learn Syst 31(7):2609\u20132622","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7065_CR61","doi-asserted-by":"crossref","unstructured":"Kim J, Grauman K (2009) Observe locally, infer globally: a space-time MRF for detecting abnormal activities with incremental updates. In: IEEE conference on computer vision and pattern recognition. IEEE, pp 2921\u20132928","DOI":"10.1109\/CVPR.2009.5206569"},{"key":"7065_CR62","doi-asserted-by":"crossref","unstructured":"Mahadevan V, Li WX, Bhalodia V, Vasconcelos N (2010) Anomaly detection in crowded scenes. In: Computer vision and pattern recognition","DOI":"10.1109\/CVPR.2010.5539872"},{"key":"7065_CR63","doi-asserted-by":"crossref","unstructured":"Luo W, Liu W, Gao S (2017) Remembering history with convolutional lstm for anomaly detection. In: IEEE International conference on multimedia and expo (ICME). IEEE, pp 439\u2013444","DOI":"10.1109\/ICME.2017.8019325"},{"key":"7065_CR64","doi-asserted-by":"crossref","unstructured":"Gong D, Liu L, Le V, Saha B, Mansour M, Venkatesh S, Hengel A (2019) Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In: Proceedings of the IEEE\/CVF International conference on computer vision, pp 1705\u20131714","DOI":"10.1109\/ICCV.2019.00179"},{"issue":"12","key":"7065_CR65","doi-asserted-by":"publisher","first-page":"4639","DOI":"10.1109\/TCSVT.2019.2962229","volume":"30","author":"JT Zhou","year":"2019","unstructured":"Zhou JT, Zhang L, Fang Z, Du J, Peng X, Xiao Y (2019) Attention-driven loss for anomaly detection in video surveillance. IEEE Trans Circuits Syst Video Technol 30(12):4639\u20134647","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"8","key":"7065_CR66","doi-asserted-by":"publisher","first-page":"2138","DOI":"10.1109\/TMM.2019.2950530","volume":"22","author":"H Song","year":"2019","unstructured":"Song H, Sun C, Wu X, Chen M, Jia Y (2019) Learning normal patterns via adversarial attention-based autoencoder for abnormal event detection in videos. IEEE Trans Multimed 22(8):2138\u20132148","journal-title":"IEEE Trans Multimed"},{"key":"7065_CR67","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, pp 1805\u20131813","DOI":"10.1145\/3343031.3350899"},{"key":"7065_CR68","doi-asserted-by":"publisher","first-page":"4106","DOI":"10.1109\/TMM.2020.3037538","volume":"23","author":"Z Fang","year":"2020","unstructured":"Fang Z, Zhou JT, Xiao Y, Li Y, Yang F (2020) Multi-encoder towards effective anomaly detection in videos. IEEE Trans Multimed 23:4106\u20134116","journal-title":"IEEE Trans Multimed"},{"key":"7065_CR69","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. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 15425\u201315434","DOI":"10.1109\/CVPR46437.2021.01517"},{"key":"7065_CR70","doi-asserted-by":"crossref","unstructured":"Cai R, Zhang H, Liu W, Gao S, Hao Z (2021) Appearance-motion memory consistency network for video anomaly detection. In: Proceedings of the AAAI conference on artificial intelligence, pp 938\u2013946","DOI":"10.1609\/aaai.v35i2.16177"},{"key":"7065_CR71","doi-asserted-by":"crossref","unstructured":"Lu Y, Yu F, Reddy MKK et al (2020) Few-shot scene-adaptive anomaly detection. Computer Vision (ECCV). Springer. UK: Glasgow 12350","DOI":"10.1007\/978-3-030-58558-7_8"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-07065-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-07065-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-07065-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T05:13:50Z","timestamp":1774934030000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-07065-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":71,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["7065"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-07065-1","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"7 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 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":"93"}}