{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T10:55:01Z","timestamp":1782298501370,"version":"3.54.5"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T00:00:00Z","timestamp":1649289600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T00:00:00Z","timestamp":1649289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s11760-022-02148-9","type":"journal-article","created":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T18:03:51Z","timestamp":1649354631000},"page":"1885-1893","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Video anomaly detection based on 3D convolutional auto-encoder"],"prefix":"10.1007","volume":"16","author":[{"given":"Xing","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Lian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dawei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiumin","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linhua","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenmin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,4,7]]},"reference":[{"issue":"1","key":"2148_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1687-6180-2013-1","volume":"2013","author":"M Paul","year":"2013","unstructured":"Paul, M., Haque, S.M., Chakraborty, S.: Human detection in surveillance videos and its applications-a review. EURASIP J. Adv. Signal Process. 2013(1), 1\u201316 (2013)","journal-title":"EURASIP J. Adv. Signal Process."},{"issue":"3","key":"2148_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surveys (CSUR) 41(3), 1\u201358 (2009)","journal-title":"ACM Comput. Surveys (CSUR)"},{"key":"2148_CR3","doi-asserted-by":"crossref","unstructured":"Liu, W., Luo, W., Lian, D., Gao, S.: Future frame prediction for anomaly detection\u2014a new baseline, pp. 6536\u20136545 (2018)","DOI":"10.1109\/CVPR.2018.00684"},{"key":"2148_CR4","unstructured":"Chong, Y.S., Tay, Y.H.: Modeling representation of videos for anomaly detection using deep learning: a review. arXiv preprint arXiv:1505.00523 (2015)"},{"key":"2148_CR5","doi-asserted-by":"crossref","unstructured":"Sun, D., Yang, X., Liu, M. Y., Kautz, J.: Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume, pp. 8934\u20138943 (2018)","DOI":"10.1109\/CVPR.2018.00931"},{"key":"2148_CR6","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.neucom.2019.11.087","volume":"383","author":"X Hu","year":"2020","unstructured":"Hu, X., Dai, J., Huang, Y., et al.: A weakly supervised framework for abnormal behavior detection and localization in crowded scenes. Neurocomputing 383, 270\u2013281 (2020)","journal-title":"Neurocomputing"},{"issue":"4","key":"2148_CR7","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.imavis.2010.11.003","volume":"29","author":"F Tung","year":"2011","unstructured":"Tung, F., Zelek, J.S., Clausi, D.A.: Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance. Image Vis. Comput. 29(4), 230\u2013240 (2011)","journal-title":"Image Vis. Comput."},{"issue":"4","key":"2148_CR8","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1109\/TIP.2008.2012070","volume":"18","author":"F Jiang","year":"2009","unstructured":"Jiang, F., Wu, Y., Katsaggelos, A.K.: A dynamic hierarchical clustering method for trajectory-based unusual video event detection. IEEE Trans. Image Process. 18(4), 907\u2013913 (2009)","journal-title":"IEEE Trans. Image Process."},{"key":"2148_CR9","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.neucom.2012.03.040","volume":"119","author":"C Li","year":"2013","unstructured":"Li, C., Han, Z., Ye, Q., Jiao, J.: Visual abnormal behavior detection based on trajectory sparse reconstruction analysis. Neurocomputing 119, 94\u2013100 (2013)","journal-title":"Neurocomputing"},{"issue":"6","key":"2148_CR10","doi-asserted-by":"publisher","first-page":"1158","DOI":"10.1109\/TPAMI.2013.172","volume":"36","author":"R Laxhammar","year":"2013","unstructured":"Laxhammar, R., Falkman, G.: Online learning and sequential anomaly detection in trajectories. IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1158\u20131173 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2148_CR11","doi-asserted-by":"crossref","unstructured":"Bera, A., Kim, S., Manocha, D.: Realtime anomaly detection using trajectory-level crowd behavior learning. pp. 50\u201357 (2016)","DOI":"10.1109\/CVPRW.2016.163"},{"key":"2148_CR12","first-page":"935","volume-title":"Abnormal Crowd Behavior Detection Using Social Force Model","author":"R Mehran","year":"2009","unstructured":"Mehran, R., Oyama, A., Shah, M.: Abnormal Crowd Behavior Detection Using Social Force Model, pp. 935\u2013942. IEEE, Manhattan (2009)"},{"key":"2148_CR13","first-page":"3313","volume-title":"Online Detection of Unusual Events in Videos Via Dynamic Sparse Coding","author":"B Zhao","year":"2011","unstructured":"Zhao, B., Fei-Fei, L., Xing, E.P.: Online Detection of Unusual Events in Videos Via Dynamic Sparse Coding, pp. 3313\u20133320. IEEE, Manhattan (2011)"},{"issue":"7","key":"2148_CR14","doi-asserted-by":"publisher","first-page":"2153","DOI":"10.1109\/TIP.2015.2409559","volume":"24","author":"V Kaltsa","year":"2015","unstructured":"Kaltsa, V., Briassouli, A., Kompatsiaris, I., Hadjileontiadis, L.J., Strintzis, M.G.: Swarm intelligence for detecting interesting events in crowded environments. IEEE Trans. Image Process. 24(7), 2153\u20132166 (2015)","journal-title":"IEEE Trans. Image Process."},{"key":"2148_CR15","first-page":"1446","volume-title":"Anomaly Detection in Extremely Crowded Scenes Using Spatio-temporal Motion Pattern Models","author":"L Kratz","year":"2009","unstructured":"Kratz, L., Nishino, K.: Anomaly Detection in Extremely Crowded Scenes Using Spatio-temporal Motion Pattern Models, pp. 1446\u20131453. IEEE, Manhattan (2009)"},{"key":"2148_CR16","first-page":"1975","volume-title":"Anomaly Detection in Crowded Scenes","author":"V Mahadevan","year":"2010","unstructured":"Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly Detection in Crowded Scenes, pp. 1975\u20131981. IEEE, Manhattan (2010)"},{"issue":"4","key":"2148_CR17","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1109\/TIFS.2018.2868617","volume":"14","author":"X Hu","year":"2018","unstructured":"Hu, X., Huang, Y., Gao, X., Luo, L., Duan, Q.: Squirrel-cage local binary pattern and its application in video anomaly detection. IEEE Trans. Inf. Forens. Secur. 14(4), 1007\u20131022 (2018)","journal-title":"IEEE Trans. Inf. Forens. Secur."},{"key":"2148_CR18","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.neucom.2018.02.045","volume":"290","author":"H Ullah","year":"2018","unstructured":"Ullah, H., Altamimi, A.B., Uzair, M., Ullah, M.: Anomalous entities detection and localization in pedestrian flows. Neurocomputing 290, 74\u201386 (2018)","journal-title":"Neurocomputing"},{"issue":"5","key":"2148_CR19","doi-asserted-by":"publisher","first-page":"1791","DOI":"10.1016\/j.patcog.2013.11.018","volume":"47","author":"X Zhu","year":"2014","unstructured":"Zhu, X., Liu, J., Wang, J., Li, C., Lu, H.: Sparse representation for robust abnormality detection in crowded scenes. Pattern Recogn. 47(5), 1791\u20131799 (2014)","journal-title":"Pattern Recogn."},{"issue":"5","key":"2148_CR20","doi-asserted-by":"publisher","first-page":"1062","DOI":"10.1109\/TMM.2018.2818942","volume":"20","author":"K Xu","year":"2018","unstructured":"Xu, K., Jiang, X., Sun, T.: Anomaly detection based on stacked sparse coding with intraframe classification strategy. IEEE Trans. Multimedia 20(5), 1062\u20131074 (2018)","journal-title":"IEEE Trans. Multimedia"},{"key":"2148_CR21","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1097\u20131105 (2012)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"2148_CR22","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"2148_CR23","doi-asserted-by":"crossref","unstructured":"Feng, Y., Yuan, Y., Lu, X.: Deep representation for abnormal event detection in crowded scenes, pp. 591\u2013595 (2016)","DOI":"10.1145\/2964284.2967290"},{"key":"2148_CR24","doi-asserted-by":"publisher","first-page":"104706","DOI":"10.1016\/j.conengprac.2020.104706","volume":"108","author":"Y Lei","year":"2021","unstructured":"Lei, Y., Karimi, H.R., Cen, L., Chen, X., Xie, Y.: Processes soft modeling based on stacked autoencoders and wavelet extreme learning machine for aluminum plant-wide application. Control Eng. Practice 108, 104706 (2021)","journal-title":"Control Eng. Practice"},{"key":"2148_CR25","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.: Detecting anomalous events in videos by learning deep representations of appearance and motion. Comput. Vis. Image Understand. 156, 117\u2013127 (2017)","journal-title":"Comput. Vis. Image Understand."},{"key":"2148_CR26","doi-asserted-by":"crossref","unstructured":"Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A.K., Davis, L.S.: Learning temporal regularity in video sequences, pp. 733\u2013742 (2016)","DOI":"10.1109\/CVPR.2016.86"},{"key":"2148_CR27","doi-asserted-by":"publisher","DOI":"10.5244\/C.31.139","volume-title":"Anomaly Detection Using a Convolutional Winner-take-all Autoencoder","author":"HT Tran","year":"2017","unstructured":"Tran, H.T., Hogg, D.: Anomaly Detection Using a Convolutional Winner-take-all Autoencoder. British Machine Vision Association, Durham (2017)"},{"key":"2148_CR28","first-page":"189","volume-title":"Abnormal Event Detection in Videos Using Spatiotemporal Autoencoder","author":"YS Chong","year":"2017","unstructured":"Chong, Y.S., Tay, Y.H.: Abnormal Event Detection in Videos Using Spatiotemporal Autoencoder, pp. 189\u2013196. Springer, Berlin (2017)"},{"key":"2148_CR29","first-page":"439","volume-title":"Remembering History with Convolutional lstm for Anomaly Detection","author":"W Luo","year":"2017","unstructured":"Luo, W., Liu, W., Gao, S.: Remembering History with Convolutional lstm for Anomaly Detection, pp. 439\u2013444. IEEE, Manhattan (2017)"},{"key":"2148_CR30","unstructured":"Medel, J.R., Savakis, A.: Anomaly detection in video using predictive convolutional long short-term memory networks. arXiv preprintarXiv:1612.00390 (2016)"},{"key":"2148_CR31","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., et\u00a0al.: Generative adversarial networks. arXiv preprint arXiv:1406.2661 (2014)"},{"key":"2148_CR32","first-page":"146","volume-title":"Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery","author":"T Schlegl","year":"2017","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery, pp. 146\u2013157. Springer, Berlin (2017)"},{"key":"2148_CR33","first-page":"1577","volume-title":"Abnormal Event Detection in Videos Using Generative Adversarial Nets","author":"M Ravanbakhsh","year":"2017","unstructured":"Ravanbakhsh, M., Nabi, M., Sangineto, E., Marcenaro, L., Regazzoni, C., Sebe, N.: Abnormal Event Detection in Videos Using Generative Adversarial Nets, pp. 1577\u20131581. IEEE, Manhattan (2017)"},{"key":"2148_CR34","first-page":"1896","volume-title":"Training Adversarial Discriminators for Cross-channel Abnormal Event Detection in Crowds","author":"M Ravanbakhsh","year":"2019","unstructured":"Ravanbakhsh, M., Sangineto, E., Nabi, M., Sebe, N.: Training Adversarial Discriminators for Cross-channel Abnormal Event Detection in Crowds, pp. 1896\u20131904. IEEE, Manhattan (2019)"},{"key":"2148_CR35","doi-asserted-by":"crossref","unstructured":"Sabokrou, M., Khalooei, M., Fathy, M., Adeli, E.: Adversarially learned one-class classifier for novelty detection, pp. 3379\u20133388 (2018)","DOI":"10.1109\/CVPR.2018.00356"},{"key":"2148_CR36","doi-asserted-by":"crossref","unstructured":"Jamadandi, A., Kotturshettar, S., Mudenagudi, U.: PredGAN: a deep multi-scale video prediction framework for detecting anomalies in videos, pp. 1\u20138 (2018)","DOI":"10.1145\/3293353.3293354"},{"issue":"8","key":"2148_CR37","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.: Learning normal patterns via adversarial attention-based autoencoder for abnormal event detection in videos. IEEE Trans. Multimedia 22(8), 2138\u20132148 (2019)","journal-title":"IEEE Trans. Multimedia"},{"key":"2148_CR38","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks, pp. 7132\u20137141"},{"key":"2148_CR39","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.c.: Convolutional LSTM network: A machine learning approach for precipitation nowcasting. arXiv preprint arXiv:1506.04214 (2015)"},{"key":"2148_CR40","doi-asserted-by":"crossref","unstructured":"Lu, C., Shi, J., Jia, J.: Abnormal event detection at 150 fps in matlab, pp. 2720\u20132727 (2013)","DOI":"10.1109\/ICCV.2013.338"},{"key":"2148_CR41","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"2148_CR42","doi-asserted-by":"crossref","unstructured":"Xu, D., Ricci, E., Yan, Y., Song, J., Sebe, N.: Learning deep representations of appearance and motion for anomalous event detection. arXiv preprint arXiv:1510.01553 (2015)","DOI":"10.5244\/C.29.8"},{"key":"2148_CR43","doi-asserted-by":"publisher","first-page":"102920","DOI":"10.1016\/j.cviu.2020.102920","volume":"195","author":"Y Fan","year":"2020","unstructured":"Fan, Y., Wen, G., Li, D., Qiu, S., Levine, M.D., Xiao, F.: Video anomaly detection and localization via gaussian mixture fully convolutional variational autoencoder. Comput. Vis. Image Understand. 195, 102920 (2020)","journal-title":"Comput. Vis. Image Understand."},{"key":"2148_CR44","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.: Video anomaly detection and localization via multivariate gaussian fully convolution adversarial autoencoder. Neurocomputing 369, 92\u2013105 (2019)","journal-title":"Neurocomputing"},{"key":"2148_CR45","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.neunet.2021.04.003","volume":"141","author":"D Yang","year":"2021","unstructured":"Yang, D., Karimi, H.R., Sun, K.: Residual wide-kernel deep convolutional auto-encoder for intelligent rotating machinery fault diagnosis with limited samples. Neural Netw. 141, 133\u2013144 (2021)","journal-title":"Neural Netw."},{"key":"2148_CR46","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: IEEE Conference on Computer Vision and Pattern Recognition, pp. 15425\u201315434 (2021)","DOI":"10.1109\/CVPR46437.2021.01517"},{"key":"2148_CR47","doi-asserted-by":"crossref","unstructured":"Sultani, W., Chen, C., Shah, M.: Real-world anomaly detection in surveillance videos. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6479\u20136488 (2018)","DOI":"10.1109\/CVPR.2018.00678"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-022-02148-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-022-02148-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-022-02148-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,11]],"date-time":"2022-08-11T06:37:04Z","timestamp":1660199824000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-022-02148-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,7]]},"references-count":47,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["2148"],"URL":"https:\/\/doi.org\/10.1007\/s11760-022-02148-9","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,7]]},"assertion":[{"value":"5 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 November 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 April 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}