{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T06:32:03Z","timestamp":1782369123999,"version":"3.54.5"},"reference-count":88,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T00:00:00Z","timestamp":1695340800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T00:00:00Z","timestamp":1695340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"MEiTY,India","award":["4(16)\/2019-ITEA"],"award-info":[{"award-number":["4(16)\/2019-ITEA"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s10489-023-04940-7","type":"journal-article","created":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T12:02:56Z","timestamp":1695384176000},"page":"28133-28152","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["STemGAN: spatio-temporal generative adversarial network for video anomaly detection"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7538-1710","authenticated-orcid":false,"given":"Rituraj","family":"Singh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7885-3719","authenticated-orcid":false,"given":"Krishanu","family":"Saini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7640-9897","authenticated-orcid":false,"given":"Anikeit","family":"Sethi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aruna","family":"Tiwari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sumeet","family":"Saurav","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanjay","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"issue":"1","key":"4940_CR1","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 transactions on pattern analysis and machine intelligence 36(1):18\u201332","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"4940_CR2","doi-asserted-by":"crossref","unstructured":"Ramachandra B, Jones M, Vatsavai RR (2020) A survey of single-scene video anomaly detection. IEEE transactions on pattern analysis and machine intelligence","DOI":"10.1109\/TPAMI.2020.3040591"},{"key":"4940_CR3","doi-asserted-by":"crossref","unstructured":"Xia X, Pan X, Li N, He X, Ma L, Zhang X, Ding N (2022) Gan-based anomaly detection: A review. Neurocomputing","DOI":"10.1016\/j.neucom.2021.12.093"},{"key":"4940_CR4","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, pp 2054\u20132060. IEEE","DOI":"10.1109\/CVPR.2010.5539882"},{"key":"4940_CR5","doi-asserted-by":"crossref","unstructured":"Mahadevan V, Li W, Bhalodia V, Vasconcelos N (2010) Anomaly detection in crowded scenes. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 1975\u20131981. IEEE","DOI":"10.1109\/CVPR.2010.5539872"},{"key":"4940_CR6","doi-asserted-by":"crossref","unstructured":"Saligrama V, Chen Z (2012) Video anomaly detection based on local statistical aggregates. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp 2112\u20132119. IEEE","DOI":"10.1109\/CVPR.2012.6247917"},{"key":"4940_CR7","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: 2009 Conference on Computer Vision and Pattern Recognition, pp 2921\u20132928. IEEE","DOI":"10.1109\/CVPR.2009.5206569"},{"key":"4940_CR8","doi-asserted-by":"crossref","unstructured":"Cong Y, Yuan J, Liu J (2011) Sparse reconstruction cost for abnormal event detection. In: CVPR 2011, pp 3449\u20133456. IEEE","DOI":"10.1109\/CVPR.2011.5995434"},{"key":"4940_CR9","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":"4940_CR10","doi-asserted-by":"crossref","unstructured":"Dalal N, Triggs B, Schmid C (2006) Human detection using oriented histograms of flow and appearance. In: European Conference on Computer Vision, pp 428\u2013441. Springer","DOI":"10.1007\/11744047_33"},{"issue":"4","key":"4940_CR11","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1016\/J.ENG.2016.04.018","volume":"2","author":"Y Pan","year":"2016","unstructured":"Pan Y (2016) Heading toward artificial intelligence 2.0. Engineering 2(4):409\u2013413","journal-title":"Engineering"},{"issue":"2","key":"4940_CR12","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/J.ENG.2016.02.008","volume":"2","author":"EP Xing","year":"2016","unstructured":"Xing EP, Ho Q, Xie P, Wei D (2016) Strategies and principles of distributed machine learning on big data. Engineering 2(2):179\u2013195","journal-title":"Engineering"},{"key":"4940_CR13","doi-asserted-by":"crossref","unstructured":"Sharif Razavian A, Azizpour H, Sullivan J, Carlsson S (2014) Cnn features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp 806\u2013813","DOI":"10.1109\/CVPRW.2014.131"},{"key":"4940_CR14","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"issue":"11","key":"4940_CR15","doi-asserted-by":"publisher","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","volume":"30","author":"Z-Q Zhao","year":"2019","unstructured":"Zhao Z-Q, Zheng P, Xu S-t, Wu X (2019) Object detection with deep learning: A review. IEEE transactions on neural networks and learning systems 30(11):3212\u20133232","journal-title":"IEEE transactions on neural networks and learning systems"},{"issue":"12","key":"4940_CR16","doi-asserted-by":"publisher","first-page":"5960","DOI":"10.1109\/TNNLS.2018.2816021","volume":"29","author":"Y Shen","year":"2018","unstructured":"Shen Y, Ji R, Wang C, Li X, Li X (2018) Weakly supervised object detection via object-specific pixel gradient. IEEE transactions on neural networks and learning systems 29(12):5960\u20135970","journal-title":"IEEE transactions on neural networks and learning systems"},{"key":"4940_CR17","doi-asserted-by":"crossref","unstructured":"Wan Z, He H (2017) Weakly supervised object localization with deep convolutional neural network based on spatial pyramid saliency map. In: 2017 IEEE International Conference on Image Processing (ICIP), pp 4177\u20134181. IEEE","DOI":"10.1109\/ICIP.2017.8297069"},{"issue":"1","key":"4940_CR18","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","volume":"35","author":"S Ji","year":"2012","unstructured":"Ji S, Xu W, Yang M, Yu K (2012) 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1):221\u2013231","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"4940_CR19","unstructured":"Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. Advances in neural information processing systems 27"},{"issue":"9","key":"4940_CR20","doi-asserted-by":"publisher","first-page":"3938","DOI":"10.1109\/TNNLS.2017.2740318","volume":"29","author":"X Chen","year":"2017","unstructured":"Chen X, Weng J, Lu W, Xu J, Weng J (2017) Deep manifold learning combined with convolutional neural networks for action recognition. IEEE transactions on neural networks and learning systems 29(9):3938\u20133952","journal-title":"IEEE transactions on neural networks and learning systems"},{"key":"4940_CR21","unstructured":"Mao X, Shen C, Yang Y-B (2016) Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. Advances in neural information processing systems 29"},{"key":"4940_CR22","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":"4940_CR23","doi-asserted-by":"crossref","unstructured":"Chong YS, Tay YH (2017) Abnormal event detection in videos using spatiotemporal autoencoder. In: International Symposium on Neural Networks, pp 189\u2013196. Springer","DOI":"10.1007\/978-3-319-59081-3_23"},{"issue":"3","key":"4940_CR24","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.1109\/TPAMI.2019.2944377","volume":"43","author":"W Luo","year":"2019","unstructured":"Luo W, Liu W, Lian D, Tang J, Duan L, Peng X, Gao S (2019) Video anomaly detection with sparse coding inspired deep neural networks. IEEE transactions on pattern analysis and machine intelligence 43(3):1070\u20131084","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"issue":"13","key":"4940_CR25","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1049\/el.2016.0440","volume":"52","author":"M Sabokrou","year":"2016","unstructured":"Sabokrou M, Fathy M, Hoseini M (2016) Video anomaly detection and localisation based on the sparsity and reconstruction error of auto-encoder. Electron Lett 52(13):1122\u20131124","journal-title":"Electron Lett"},{"key":"4940_CR26","doi-asserted-by":"crossref","unstructured":"Tran HT, Hogg D (2017) Anomaly detection using a convolutional winner-take-all autoencoder. In: Proceedings of the British Machine Vision Conference 2017. British Machine Vision Association","DOI":"10.5244\/C.31.139"},{"issue":"11","key":"4940_CR27","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139\u2013144. https:\/\/doi.org\/10.1145\/3422622","journal-title":"Commun ACM"},{"key":"4940_CR28","doi-asserted-by":"publisher","first-page":"1882","DOI":"10.1109\/TIP.2021.3049346","volume":"30","author":"N-T Tran","year":"2021","unstructured":"Tran N-T, Tran V-H, Nguyen N-B, Nguyen T-K, Cheung N-M (2021) On data augmentation for gan training. IEEE Trans Image Process 30:1882\u20131897","journal-title":"IEEE Trans Image Process"},{"key":"4940_CR29","doi-asserted-by":"crossref","unstructured":"Ledig C, Theis L, Husz\u00e1r F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z et al (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4681\u20134690","DOI":"10.1109\/CVPR.2017.19"},{"key":"4940_CR30","doi-asserted-by":"crossref","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\u2013 2622","DOI":"10.1109\/TNNLS.2019.2933554"},{"key":"4940_CR31","doi-asserted-by":"crossref","unstructured":"Isola P, Zhu J-Y, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1125\u20131134","DOI":"10.1109\/CVPR.2017.632"},{"key":"4940_CR32","doi-asserted-by":"crossref","unstructured":"Yu J, Lee Y, Yow KC, Jeon M, Pedrycz W (2021) Abnormal event detection and localization via adversarial event prediction. IEEE Transactions on Neural Networks and Learning Systems","DOI":"10.1109\/TNNLS.2021.3053563"},{"key":"4940_CR33","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":"4940_CR34","doi-asserted-by":"crossref","unstructured":"Bird N, Atev S, Caramelli N, Martin R, Masoud O, Papanikolopoulos N (2006) Real time, online detection of abandoned objects in public areas. In: Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006., pp 3775\u20133780. IEEE","DOI":"10.1109\/ROBOT.2006.1642279"},{"key":"4940_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2020.102920","volume":"195","author":"Y Fan","year":"2020","unstructured":"Fan Y, Wen G, Li D, Qiu S, Levine MD, Xiao F (2020) Video anomaly detection and localization via gaussian mixture fully convolutional variational autoencoder. Comp Vision Image Underst 195:102920","journal-title":"Comp Vision Image Underst"},{"key":"4940_CR36","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.neucom.2019.08.044","volume":"369","author":"N Li","year":"2019","unstructured":"Li N, Chang F (2019) Video anomaly detection and localization via multivariate gaussian fully convolution adversarial autoencoder. Neurocomputing 369:92\u2013105","journal-title":"Neurocomputing"},{"key":"4940_CR37","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1109\/TMM.2020.2984093","volume":"23","author":"N Li","year":"2020","unstructured":"Li N, Chang F, Liu C (2020) Spatial-temporal cascade autoencoder for video anomaly detection in crowded scenes. IEEE Transactions on Multimedia 23:203\u2013215","journal-title":"IEEE Transactions on Multimedia"},{"key":"4940_CR38","doi-asserted-by":"crossref","unstructured":"Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, Fei-Fei L (2014) Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1725\u20131732","DOI":"10.1109\/CVPR.2014.223"},{"key":"4940_CR39","doi-asserted-by":"crossref","unstructured":"Lin J, Gan C, Han S (2019) Tsm: Temporal shift module for efficient video understanding. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 7083\u20137093","DOI":"10.1109\/ICCV.2019.00718"},{"key":"4940_CR40","doi-asserted-by":"publisher","first-page":"172425","DOI":"10.1109\/ACCESS.2019.2954540","volume":"7","author":"Y Li","year":"2019","unstructured":"Li Y, Cai Y, Liu J, Lang S, Zhang X (2019) Spatio-temporal unity networking for video anomaly detection. IEEE Access 7:172425\u2013172432","journal-title":"IEEE Access"},{"key":"4940_CR41","doi-asserted-by":"crossref","unstructured":"Lu Y, Kumar KM, shahabeddin Nabavi S, Wang Y (2019) Future frame prediction using convolutional vrnn for anomaly detection. In: 2019 16Th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp 1\u20138. IEEE","DOI":"10.1109\/AVSS.2019.8909850"},{"issue":"10","key":"4940_CR42","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 Transactions on Information Forensics and Security 14(10):2537\u20132550","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"4940_CR43","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1016\/j.procir.2021.03.088","volume":"99","author":"B Lindemann","year":"2021","unstructured":"Lindemann B, M\u00fcller T, Vietz H, Jazdi N, Weyrich M (2021) A survey on long short-term memory networks for time series prediction. Procedia CIRP 99:650\u2013655","journal-title":"Procedia CIRP"},{"issue":"2","key":"4940_CR44","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1109\/TSC.2015.2501981","volume":"11","author":"Y Wu","year":"2015","unstructured":"Wu Y, He F, Zhang D, Li X (2015) Service-oriented feature-based data exchange for cloud-based design and manufacturing. IEEE Trans Serv Comput 11(2):341\u2013353","journal-title":"IEEE Trans Serv Comput"},{"key":"4940_CR45","unstructured":"Shi X, Chen Z, Wang H, Yeung D-Y, Wong W-K, Woo W-c (2015) Convolutional lstm network: A machine learning approach for precipitation nowcasting. Advances in neural information processing systems 28"},{"key":"4940_CR46","unstructured":"Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhudinov R, Zemel R, Bengio Y (2015) Show, attend and tell: Neural image caption generation with visual attention. In: International Conference on Machine Learning, pp 2048\u20132057. PMLR"},{"key":"4940_CR47","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":"12","key":"4940_CR48","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 transactions on circuits and systems for video technology 30(12):4639\u20134647","journal-title":"IEEE transactions on circuits and systems for video technology"},{"key":"4940_CR49","doi-asserted-by":"publisher","first-page":"3450","DOI":"10.1007\/s10489-020-01961-4","volume":"51","author":"H-B Bi","year":"2021","unstructured":"Bi H-B, Lu D, Zhu H-H, Yang L-N, Guan H-P (2021) Sta-net: spatial-temporal attention network for video salient object detection. Appl Intell 51:3450\u20133459","journal-title":"Appl Intell"},{"key":"4940_CR50","doi-asserted-by":"publisher","first-page":"3012","DOI":"10.1007\/s10489-020-02100-9","volume":"51","author":"Y Li","year":"2021","unstructured":"Li Y, Guo K, Lu Y, Liu L (2021) Cropping and attention based approach for masked face recognition. Appl Intell 51:3012\u20133025","journal-title":"Appl Intell"},{"key":"4940_CR51","doi-asserted-by":"crossref","unstructured":"Mehran R, Oyama A, Shah M (2009) Abnormal crowd behavior detection using social force model. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp 935\u2013942. IEEE","DOI":"10.1109\/CVPR.2009.5206641"},{"key":"4940_CR52","doi-asserted-by":"crossref","unstructured":"Benezeth Y, Jodoin P-M, Saligrama V, Rosenberger C (2009) Abnormal events detection based on spatio-temporal co-occurences. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp 2458\u20132465. IEEE","DOI":"10.1109\/CVPR.2009.5206686"},{"key":"4940_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2020.104078","volume":"106","author":"R Nayak","year":"2021","unstructured":"Nayak R, Pati UC, Das SK (2021) A comprehensive review on deep learning-based methods for video anomaly detection. Image Vis Comput 106:104078","journal-title":"Image Vis Comput"},{"issue":"1","key":"4940_CR54","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1109\/TII.2019.2938527","volume":"16","author":"R Nawaratne","year":"2019","unstructured":"Nawaratne R, Alahakoon D, De Silva D, Yu X (2019) Spatiotemporal anomaly detection using deep learning for real-time video surveillance. IEEE Transactions on Industrial Informatics 16(1):393\u2013402","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"4940_CR55","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.media.2019.01.010","volume":"54","author":"T Schlegl","year":"2019","unstructured":"Schlegl T, Seeb\u00f6ck P, Waldstein SM, Langs G, Schmidt-Erfurth U (2019) f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Med Image Anal 54:30\u201344","journal-title":"Med Image Anal"},{"key":"4940_CR56","doi-asserted-by":"crossref","unstructured":"Wang L, Tian J, Zhou S, Shi H, Hua G (2023) Memory-augmented appearance-motion network for video anomaly detection. Pattern Recognit 109335","DOI":"10.1016\/j.patcog.2023.109335"},{"key":"4940_CR57","doi-asserted-by":"crossref","unstructured":"Wei H, Li K, Li H, Lyu Y, Hu X (2019) Detecting video anomaly with a stacked convolutional lstm framework. In: International Conference on Computer Vision Systems, pp 330\u2013342. Springer","DOI":"10.1007\/978-3-030-34995-0_30"},{"key":"4940_CR58","doi-asserted-by":"crossref","unstructured":"Doshi K, Yilmaz Y (2022) Rethinking video anomaly detection-a continual learning approach. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp 3961\u20133970","DOI":"10.1109\/WACV51458.2022.00309"},{"key":"4940_CR59","doi-asserted-by":"crossref","unstructured":"Chang Y, Tu Z, Xie W, Yuan J (2020) Clustering driven deep autoencoder for video anomaly detection. In: European Conference on Computer Vision, pp 329\u2013345. Springer","DOI":"10.1007\/978-3-030-58555-6_20"},{"key":"4940_CR60","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 Transactions on Multimedia 23:4106\u20134116","journal-title":"IEEE Transactions on Multimedia"},{"key":"4940_CR61","doi-asserted-by":"crossref","unstructured":"Zhao Y, Deng B, Shen C, Liu Y, Lu H, Hua X-S (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":"4940_CR62","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.patrec.2022.03.004","volume":"156","author":"D Li","year":"2022","unstructured":"Li D, Nie X, Li X, Zhang Y, Yin Y (2022) Context-related video anomaly detection via generative adversarial network. Pattern Recogn Lett 156:183\u2013189","journal-title":"Pattern Recogn Lett"},{"key":"4940_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107865","volume":"114","author":"K Doshi","year":"2021","unstructured":"Doshi K, Yilmaz Y (2021) Online anomaly detection in surveillance videos with asymptotic bound on false alarm rate. Pattern Recognit 114:107865","journal-title":"Pattern Recognit"},{"key":"4940_CR64","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 Recognit 121:108232","journal-title":"Pattern Recognit"},{"issue":"1","key":"4940_CR65","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1007\/s10489-022-03488-2","volume":"53","author":"C Li","year":"2023","unstructured":"Li C, Li H, Zhang G (2023) Future frame prediction based on generative assistant discriminative network for anomaly detection. Appl Intell 53(1):542\u2013559","journal-title":"Appl Intell"},{"key":"4940_CR66","unstructured":"Mathieu M, Couprie C, LeCun Y (2015) Deep multi-scale video prediction beyond mean square error. arXiv preprint arXiv:1511.05440"},{"key":"4940_CR67","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.patcog.2019.01.006","volume":"90","author":"Z Wu","year":"2019","unstructured":"Wu Z, Shen C, Van Den Hengel A (2019) Wider or deeper: Revisiting the resnet model for visual recognition. Pattern Recognit 90:119\u2013133","journal-title":"Pattern Recognit"},{"key":"4940_CR68","unstructured":"Lin J, Gan C, Han S (2018) Temporal shift module for efficient video understanding. CoRR abs\/1811.08383 (1811)"},{"key":"4940_CR69","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"4940_CR70","doi-asserted-by":"crossref","unstructured":"Zhang Y, Li K, Li K, Wang L, Zhong B, Fu Y (2018) Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 286\u2013301","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"4940_CR71","doi-asserted-by":"crossref","unstructured":"Li C, Wand M (2016) Precomputed real-time texture synthesis with markovian generative adversarial networks. In: European Conference on Computer Vision, pp 702\u2013716. Springer","DOI":"10.1007\/978-3-319-46487-9_43"},{"key":"4940_CR72","unstructured":"Denton EL, Chintala S, Fergus R et al (2015) Deep generative image models using a laplacian pyramid of adversarial networks. Advances in neural information processing systems 28"},{"key":"4940_CR73","doi-asserted-by":"crossref","unstructured":"Lu Y, Yu F, Reddy MKK, Wang Y (2020) Few-shot scene-adaptive anomaly detection. In: European Conference on Computer Vision, pp 125\u2013141. Springer","DOI":"10.1007\/978-3-030-58558-7_8"},{"key":"4940_CR74","unstructured":"Zenati H, Foo CS, Lecouat B, Manek G, Chandrasekhar VR (2018) Efficient gan-based anomaly detection. arXiv preprint arXiv:1802.06222"},{"issue":"3","key":"4940_CR75","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 transactions on pattern analysis and machine intelligence 30(3):555\u2013560","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"4940_CR76","doi-asserted-by":"crossref","unstructured":"Zhao B, Fei-Fei L, Xing EP (2011) Online detection of unusual events in videos via dynamic sparse coding. In: CVPR 2011, pp 3313\u20133320. IEEE","DOI":"10.1109\/CVPR.2011.5995524"},{"key":"4940_CR77","doi-asserted-by":"crossref","unstructured":"Le V-T, Kim Y-G (2022) Attention-based residual autoencoder for video anomaly detection. Appl Intell 1\u201315","DOI":"10.1007\/s10489-022-03613-1"},{"key":"4940_CR78","doi-asserted-by":"crossref","unstructured":"Ravanbakhsh M, Nabi M, Sangineto E, Marcenaro L, Regazzoni C, Sebe N (2017) Abnormal event detection in videos using generative adversarial nets. In: 2017 IEEE International Conference on Image Processing (ICIP), pp 1577\u20131581. IEEE","DOI":"10.1109\/ICIP.2017.8296547"},{"key":"4940_CR79","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":"4940_CR80","doi-asserted-by":"crossref","unstructured":"Yang Y, Zhan D, Yang F, Zhou X-D, Yan Y, Wang Y (2020) Improving video anomaly detection performance with patch-level loss and segmentation map. In: 2020 IEEE 6th International Conference on Computer and Communications (ICCC), pp 1832\u20131839. IEEE","DOI":"10.1109\/ICCC51575.2020.9345287"},{"key":"4940_CR81","doi-asserted-by":"crossref","unstructured":"Abati D, Porrello A, Calderara S, Cucchiara R (2019) Latent space autoregression for novelty detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 481\u2013490","DOI":"10.1109\/CVPR.2019.00057"},{"key":"4940_CR82","doi-asserted-by":"crossref","unstructured":"Gong D, Liu L, Le V, Saha B, Mansour MR, Venkatesh S, Hengel Avd (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":"1","key":"4940_CR83","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/s11760-020-01740-1","volume":"15","author":"K Deepak","year":"2021","unstructured":"Deepak K, Chandrakala S, Mohan CK (2021) Residual spatiotemporal autoencoder for unsupervised video anomaly detection. SIViP 15(1):215\u2013222","journal-title":"SIViP"},{"key":"4940_CR84","doi-asserted-by":"crossref","unstructured":"Ravanbakhsh M, Sangineto E, Nabi M, Sebe N (2019) Training adversarial discriminators for cross-channel abnormal event\u00a0detection in crowds. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp 1896\u20131904. IEEE","DOI":"10.1109\/WACV.2019.00206"},{"key":"4940_CR85","doi-asserted-by":"crossref","unstructured":"Luo W, Liu W, Gao S (2017) Remembering history with convolutional lstm for anomaly detection. In: 2017 IEEE International Conference on Multimedia and Expo (ICME), pp 439\u2013444. IEEE","DOI":"10.1109\/ICME.2017.8019325"},{"key":"4940_CR86","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"},{"key":"4940_CR87","doi-asserted-by":"publisher","unstructured":"Ionescu RT, Smeureanu S, Popescu M, Alexe B (2019) Detecting abnormal events in video using narrowed normality clusters. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp 1951\u20131960. https:\/\/doi.org\/10.1109\/WACV.2019.00212","DOI":"10.1109\/WACV.2019.00212"},{"key":"4940_CR88","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.cviu.2016.10.010","volume":"156","author":"D Xu","year":"2017","unstructured":"Xu D, Yan Y, Ricci E, Sebe N (2017) Detecting anomalous events in videos by learning deep representations of appearance and motion. Comp Vision Image Underst 156:117\u2013127","journal-title":"Comp Vision Image Underst"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04940-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04940-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04940-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T19:18:23Z","timestamp":1730143103000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04940-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,22]]},"references-count":88,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["4940"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04940-7","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,22]]},"assertion":[{"value":"2 August 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2023","order":2,"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 there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}