{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T13:04:05Z","timestamp":1771506245864,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T00:00:00Z","timestamp":1741132800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T00:00:00Z","timestamp":1741132800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272049, 62236006, 62172045"],"award-info":[{"award-number":["62272049, 62236006, 62172045"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272049, 62236006, 62172045"],"award-info":[{"award-number":["62272049, 62236006, 62172045"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the key Projects of Beijing Union University","award":["ZKZD202301"],"award-info":[{"award-number":["ZKZD202301"]}]},{"name":"the key Projects of Beijing Union University","award":["ZKZD202301"],"award-info":[{"award-number":["ZKZD202301"]}]},{"name":"the key Projects of Beijing Union University","award":["ZKZD202301"],"award-info":[{"award-number":["ZKZD202301"]}]},{"name":"the key Projects of Beijing Union University","award":["ZKZD202301"],"award-info":[{"award-number":["ZKZD202301"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s00530-025-01735-3","type":"journal-article","created":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T07:17:31Z","timestamp":1741159051000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A video anomaly detection framework based on feature-strengthened and memory feature-ernhanced reconstruction"],"prefix":"10.1007","volume":"31","author":[{"given":"Hongfei","family":"Liu","sequence":"first","affiliation":[]},{"given":"Ning","family":"He","sequence":"additional","affiliation":[]},{"given":"Xunrui","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Runjie","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,5]]},"reference":[{"key":"1735_CR1","doi-asserted-by":"crossref","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. ACM Computing Surveys 41(3) (2009)","DOI":"10.1145\/1541880.1541882"},{"issue":"5","key":"1735_CR2","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/MSP.2010.937393","volume":"27","author":"V Saligrama","year":"2010","unstructured":"Saligrama, V., Konrad, J., Jodoin, P.: Video anomaly identification. IEEE Signal Process. Mag. 27(5), 18\u201333 (2010)","journal-title":"IEEE Signal Process. Mag."},{"key":"1735_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109335","volume":"138","author":"L Wang","year":"2023","unstructured":"Wang, L., Tian, J., Zhou, S., Shi, H., Hua, G.: Memory-augmented appearance-motion network for video anomaly detection. Pattern Recogn. 138, 109335 (2023)","journal-title":"Pattern Recogn."},{"key":"1735_CR4","doi-asserted-by":"crossref","unstructured":"Lu, Y., Kumar, M. K, Nabavi, S.S., Wang, Y.: Future frame prediction using convolutional vrnn for anomaly detection. IEEE (2019)","DOI":"10.1109\/AVSS.2019.8909850"},{"key":"1735_CR5","doi-asserted-by":"crossref","unstructured":"Gong, Y., Luo, S., Wang, C., Zheng, Y.: Feature differentiation reconstruction network for weakly-supervised video anomaly detection. IEEE Signal Processing Letters (2023)","DOI":"10.1109\/LSP.2023.3324299"},{"key":"1735_CR6","doi-asserted-by":"crossref","unstructured":"Shao, W., Xiao, R., Rajapaksha, P., Wang, M., Crespi, N., Luo, Z., Minerva, R.: Video anomaly detection with ntcn-ml: A novel tcn for multi-instance learning. Pattern Recognition, 109765 (2023)","DOI":"10.1016\/j.patcog.2023.109765"},{"key":"1735_CR7","doi-asserted-by":"crossref","unstructured":"Ye, Z., Li, Y., Cui, Z., Liu, Y., Li, L., Wang, L., Zhang, C.: Unsupervised video anomaly detection with self-attention based feature aggregating. In: 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), pp. 3551\u20133556 (2023). IEEE","DOI":"10.1109\/ITSC57777.2023.10421863"},{"issue":"7","key":"1735_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3645101","volume":"56","author":"Y Liu","year":"2024","unstructured":"Liu, Y., Yang, D., Wang, Y., Liu, J., Liu, J., Boukerche, A., Sun, P., Song, L.: Generalized video anomaly event detection: Systematic taxonomy and comparison of deep models. ACM Comput. Surv. 56(7), 1\u201338 (2024)","journal-title":"ACM Comput. Surv."},{"key":"1735_CR9","doi-asserted-by":"crossref","unstructured":"Liu, J., Liu, Y., Lin, J., Li, J., Sun, P., Hu, B., Song, L., Boukerche, A., Leung, V.: Networking systems for video anomaly detection: A tutorial and survey. arXiv preprint arXiv:2405.10347 (2024)","DOI":"10.1145\/3729222"},{"key":"1735_CR10","doi-asserted-by":"crossref","unstructured":"Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.v.d.: 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 (2019)","DOI":"10.1109\/ICCV.2019.00179"},{"key":"1735_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110986","volume":"280","author":"Y Liu","year":"2023","unstructured":"Liu, Y., Yang, D., Fang, G., Wang, Y., Wei, D., Zhao, M., Cheng, K., Liu, J., Song, L.: Stochastic video normality network for abnormal event detection in surveillance videos. Knowl. Based Syst. 280, 110986 (2023)","journal-title":"Knowl. Based Syst."},{"key":"1735_CR12","doi-asserted-by":"crossref","unstructured":"Fioresi, J., Dave, I.R., Shah, M.: Ted-spad: Temporal distinctiveness for self-supervised privacy-preservation for video anomaly detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 13598\u201313609 (2023)","DOI":"10.1109\/ICCV51070.2023.01251"},{"key":"1735_CR13","doi-asserted-by":"crossref","unstructured":"Morais, R., Le, V., Tran, T., Saha, B., Mansour, M., Venkatesh, S.: Learning regularity in skeleton trajectories for anomaly detection in videos (2019)","DOI":"10.1109\/CVPR.2019.01227"},{"key":"1735_CR14","doi-asserted-by":"crossref","unstructured":"Liu, Z., Nie, Y., Long, C., Zhang, Q., Li, G.: A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction (2021)","DOI":"10.1109\/ICCV48922.2021.01333"},{"key":"1735_CR15","doi-asserted-by":"crossref","unstructured":"Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A.K., Davis, L.S.: Learning temporal regularity in video sequences. arXiv e-prints (2016)","DOI":"10.1109\/CVPR.2016.86"},{"issue":"11","key":"1735_CR16","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., Xiao, F.: Video anomaly detection and localization via gaussian mixture fully convolutional variational autoencoder. Comput. Vis. Image Underst. 195(11), 102920 (2020)","journal-title":"Comput. Vis. Image Underst."},{"key":"1735_CR17","doi-asserted-by":"crossref","unstructured":"Bajgoti, A., Gupta, R., Balaji, P., Dwivedi, R., Siwach, M., Gupta, D.: Swinanomaly: Real-time video anomaly detection using video swin transformer and sort. IEEE Access (2023)","DOI":"10.36227\/techrxiv.171174507.79352927\/v1"},{"key":"1735_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2023.103967","volume":"97","author":"J Tang","year":"2023","unstructured":"Tang, J., Wang, Z., Hao, G., Wang, K., Zhang, Y., Wang, N., Liang, D.: Sae-ppl: Self-guided attention encoder with prior knowledge-guided pseudo labels for weakly supervised video anomaly detection. J. Vis. Commun. Image Represent. 97, 103967 (2023)","journal-title":"J. Vis. Commun. Image Represent."},{"key":"1735_CR19","doi-asserted-by":"crossref","unstructured":"Tang, Y., Zhao, L., Zhang, S., Gong, C., Li, G. and Yang, J.: Integrating prediction and reconstruction for anomaly detection. Pattern Recogn. Lett. 129, 123\u2013130 (2020)","DOI":"10.1016\/j.patrec.2019.11.024"},{"key":"1735_CR20","unstructured":"Weston, J., Chopra, S., Bordes, A.: Memory networks (2014). arXiv preprint arXiv:1410.3916"},{"key":"1735_CR21","doi-asserted-by":"crossref","unstructured":"Miller, A., Fisch, A., Dodge, J., Karimi, A.-H., Bordes, A., Weston, J.: Key-value memory networks for directly reading documents (2016). arXiv preprint arXiv:1606.03126","DOI":"10.18653\/v1\/D16-1147"},{"key":"1735_CR22","doi-asserted-by":"crossref","unstructured":"Park, H., Noh, J., Ham, B.: Learning memory-guided normality for anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14372\u201314381 (2020)","DOI":"10.1109\/CVPR42600.2020.01438"},{"key":"1735_CR23","doi-asserted-by":"crossref","unstructured":"Lv, H., Chen, C., Cui, Z., Xu, C., Li, Y., Yang, J.: 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 (2021)","DOI":"10.1109\/CVPR46437.2021.01517"},{"issue":"1","key":"1735_CR24","first-page":"18","volume":"36","author":"W Li","year":"2013","unstructured":"Li, W., Mahadevan, V., Vasconcelos, N.: Anomaly detection and localization in crowded scenes. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 18\u201332 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1735_CR25","doi-asserted-by":"crossref","unstructured":"Lu, C., Shi, J., Jia, J.: Abnormal event detection at 150 fps in matlab. In: IEEE International Conference on Computer Vision (2014)","DOI":"10.1109\/ICCV.2013.338"},{"key":"1735_CR26","doi-asserted-by":"crossref","unstructured":"Liu, W., Luo, W., Lian, D., Gao, S.: Future frame prediction for anomaly detection \u2013 a new baseline (2017)","DOI":"10.1109\/CVPR.2018.00684"},{"key":"1735_CR27","unstructured":"Kingma, D., Ba, J.: Adam: A method for stochastic optimization. Computer Science (2014)"},{"key":"1735_CR28","doi-asserted-by":"crossref","unstructured":"Mahadevan, V., Li, W.X., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: Computer Vision & Pattern Recognition (2010)","DOI":"10.1109\/CVPR.2010.5539872"},{"key":"1735_CR29","doi-asserted-by":"crossref","unstructured":"Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A.K., Davis, L.S.: Learning temporal regularity in video sequences. arXiv e-prints (2016)","DOI":"10.1109\/CVPR.2016.86"},{"key":"1735_CR30","doi-asserted-by":"crossref","unstructured":"Luo, W., Liu, W., Gao, S.: Remembering history with convolutional lstm for anomaly detection. IEEE (2017)","DOI":"10.1109\/ICME.2017.8019325"},{"key":"1735_CR31","unstructured":"Nguyen, T.N., Meunier, J.: Anomaly detection in video sequence with appearance-motion correspondence. In: International Conference on Computer Vision"},{"issue":"1","key":"1735_CR32","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/TCDS.2018.2883368","volume":"12","author":"S Yan","year":"2020","unstructured":"Yan, S., Smith, J.S., Lu, W., Zhang, B.: Abnormal event detection from videos using a two-stream recurrent variational autoencoder. IEEE Trans. Cogn. Dev. Syst. 12(1), 30\u201342 (2020)","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"1735_CR33","doi-asserted-by":"crossref","unstructured":"Zhou, J.T., Zhang, L., Fang, Z., Du, J., Peng, X., Xiao, Y.: Attention-driven loss for anomaly detection in video surveillance. IEEE Transactions on Circuits and Systems for Video Technology (2020)","DOI":"10.1109\/TCSVT.2019.2962229"},{"key":"1735_CR34","doi-asserted-by":"crossref","unstructured":"Li, D., Nie, X., Li, X., Zhang, Y., Yin, Y.: Context-related video anomaly detection via generative adversarial network. Pattern recognition letters (Apr.), 156 (2022)","DOI":"10.1016\/j.patrec.2022.03.004"},{"key":"1735_CR35","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Deng, B., Shen, C., Liu, Y., Lu, H., Hua, X.S.: [acm press the 2017 acm - mountain view, california, usa (2017.10.23-2017.10.27)] proceedings of the 2017 acm on multimedia conference - mm 17 - spatio-temporal autoencoder for video anomaly detection. In: ACM, pp. 1933\u20131941 (2017)","DOI":"10.1145\/3123266.3123451"},{"key":"1735_CR36","doi-asserted-by":"crossref","unstructured":"Ravanbakhsh, M., Nabi, M., Sangineto, E., Marcenaro, L., Regazzoni, C., Sebe, N.: Abnormal event detection in videos using generative adversarial nets. IEEE (2017)","DOI":"10.1109\/ICIP.2017.8296547"},{"key":"1735_CR37","doi-asserted-by":"crossref","unstructured":"Luo, W., Liu, W., Gao, S.: A revisit of sparse coding based anomaly detection in stacked rnn framework. In: International Conference on Computer Vision (2017)","DOI":"10.1109\/ICCV.2017.45"},{"issue":"3","key":"1735_CR38","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.1109\/TPAMI.2019.2944377","volume":"43","author":"W Luo","year":"2021","unstructured":"Luo, W., Liu, W., Lian, D., Tang, J., Duan, L., Peng, X., Gao, S.: Video anomaly detection with sparse coding inspired deep neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 43(3), 1070\u20131084 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1735_CR39","doi-asserted-by":"crossref","unstructured":"Liu, W., Luo, W., Li, Z., Zhao, P., Gao, S.: Margin learning embedded prediction for video anomaly detection with a few anomalies (2019)","DOI":"10.24963\/ijcai.2019\/419"},{"key":"1735_CR40","doi-asserted-by":"crossref","unstructured":"Y. Tang, B, L. Zhoa, S. Zhang: Integrating prediction and reconstruction for anomaly detection, Pattern Recogn. Lett. 129, 123\u2013130 (2020)","DOI":"10.1016\/j.patrec.2019.11.024"},{"key":"1735_CR41","doi-asserted-by":"crossref","unstructured":"Dong, F., Zhang, Y., Nie, X.: Dual discriminator generative adversarial network for video anomaly detection. IEEE Access (2020)","DOI":"10.1109\/ACCESS.2020.2993373"},{"key":"1735_CR42","doi-asserted-by":"crossref","unstructured":"Ye, M., Peng, X., Gan, W., Wu, W., Qiao, Y.: Anopcn: Video anomaly detection via deep predictive coding network. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 1805\u20131813 (2019)","DOI":"10.1145\/3343031.3350899"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-01735-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-025-01735-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-01735-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T19:37:07Z","timestamp":1745264227000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-025-01735-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,5]]},"references-count":42,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["1735"],"URL":"https:\/\/doi.org\/10.1007\/s00530-025-01735-3","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,5]]},"assertion":[{"value":"30 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2025","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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"There are no ethical issues with this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"137"}}