{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T10:00:55Z","timestamp":1769076055992,"version":"3.49.0"},"reference-count":109,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T00:00:00Z","timestamp":1704153600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T00:00:00Z","timestamp":1704153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s00371-023-03210-4","type":"journal-article","created":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T11:02:12Z","timestamp":1704193332000},"page":"7823-7844","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["AnomalyNet: a spatiotemporal motion-aware CNN approach for detecting anomalies in real-world autonomous surveillance"],"prefix":"10.1007","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1760-6346","authenticated-orcid":false,"given":"Aqib","family":"Mumtaz","sequence":"first","affiliation":[]},{"given":"Allah Bux","family":"Sargano","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9758-9162","authenticated-orcid":false,"given":"Zulfiqar","family":"Habib","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,2]]},"reference":[{"key":"3210_CR1","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.M.: Video anomaly identification. IEEE Signal Process. Mag. 27, 18 (2010)","journal-title":"IEEE Signal Process. Mag."},{"key":"3210_CR2","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. In Proceedings-International Conference on Image Processing, ICIP, pp. 1577\u20131581 (2017)","DOI":"10.1109\/ICIP.2017.8296547"},{"key":"3210_CR3","doi-asserted-by":"crossref","unstructured":"Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A. K., Davis, L. S.: Learning temporal regularity in video sequences. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 733\u2013742 (2016)","DOI":"10.1109\/CVPR.2016.86"},{"key":"3210_CR4","doi-asserted-by":"crossref","unstructured":"Liu, W., Luo, W., Lian, D., Gao, S.: Future frame prediction for anomaly detection\u2014a new baseline. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 6536\u20136545 (2018)","DOI":"10.1109\/CVPR.2018.00684"},{"key":"3210_CR5","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":"3210_CR6","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. In British Machine Vision Conference (BMVC), pp. 1\u20133 (2015)","DOI":"10.5244\/C.29.8"},{"key":"3210_CR7","doi-asserted-by":"crossref","unstructured":"Zhu, X., Liu, J., Wang, J., Fang, Y., Lu, H.: Anomaly detection in crowded scene via appearance and dynamics joint modelling. In IEEE International Conference on Image Processing (ICIP), pp. 2705\u20132708 (2012)","DOI":"10.1109\/ICIP.2012.6467457"},{"issue":"3","key":"3210_CR8","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1109\/TCSVT.2016.2637778","volume":"27","author":"RVHM Colque","year":"2016","unstructured":"Colque, R.V.H.M., Caetano, C., De Andrade, M.T.L., Schwartz, W.R.: Histograms of optical flow orientation and magnitude and entropy to detect anomalous events in videos. IEEE Trans. Circuits Syst. Video Technol. 27(3), 673\u2013682 (2016)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"3210_CR9","doi-asserted-by":"crossref","unstructured":"Sultani, W., Chen, C., Shah, M.: Real-world anomaly detection in surveillance videos. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 6479\u20136488 (2018)","DOI":"10.1109\/CVPR.2018.00678"},{"key":"3210_CR10","doi-asserted-by":"crossref","unstructured":"Lu, C., Shi, J., Jia, J.: Abnormal event detection at 150 FPS in MATLAB. In Proceedings of the IEEE International Conference on Computer Vision, pp. 2720\u20132727(2013)","DOI":"10.1109\/ICCV.2013.338"},{"key":"3210_CR11","doi-asserted-by":"crossref","unstructured":"Shao, J., Loy, C.-C., Kang, K., Wang, X.: Slicing convolutional neural network for crowd video understanding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5620\u20135628 (2016)","DOI":"10.1109\/CVPR.2016.606"},{"key":"3210_CR12","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G. E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst (2012). https:\/\/proceedings.neurips.cc\/paper\/2012\/file\/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf"},{"key":"3210_CR13","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"3210_CR14","unstructured":"Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. Adv. Neural Inf. Process. Syst (2014). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2014\/file\/00ec53c4682d36f5c4359f4ae7bd7ba1-Paper.pdf"},{"key":"3210_CR15","doi-asserted-by":"crossref","unstructured":"Mumtaz, A., Sargano, A. B., Habib, Z.: Violence detection in surveillance videos with deep network using transfer learning. In 2nd European Conference on Electrical Engineering and Computer Science (EECS), pp. 558\u2013563 (2018)","DOI":"10.1109\/EECS.2018.00109"},{"issue":"3","key":"3210_CR16","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1093\/comjnl\/bxaa061","volume":"65","author":"A Mumtaz","year":"2020","unstructured":"Mumtaz, A., Sargano, A. B., Habib, Z.: Fast learning through deep multi-net CNN model for violence recognition in video surveillance. Comput. J 65(3), 457\u2013472 (2020). https:\/\/academic.oup.com\/comjnl\/article-abstract\/65\/3\/457\/5867750","journal-title":"Comput. J"},{"key":"3210_CR17","doi-asserted-by":"crossref","unstructured":"Sargano, A. B., Wang, X., Angelov, P., Habib, Z.: Human action recognition using transfer learning with deep representations. In 2017 International Joint Conference on Neural Networks (IJCNN), pp. 463\u2013469 (2017)","DOI":"10.1109\/IJCNN.2017.7965890"},{"issue":"10","key":"3210_CR18","doi-asserted-by":"publisher","first-page":"309","DOI":"10.3390\/app6100309","volume":"6","author":"AB Sargano","year":"2016","unstructured":"Sargano, A.B., Angelov, P., Habib, Z.: Human action recognition from multiple views based on view-invariant feature descriptor using support vector machines. Appl. Sci. 6(10), 309 (2016)","journal-title":"Appl. Sci."},{"issue":"1","key":"3210_CR19","doi-asserted-by":"publisher","first-page":"110","DOI":"10.3390\/app7010110","volume":"7","author":"A Sargano","year":"2017","unstructured":"Sargano, A., Angelov, P., Habib, Z.: A comprehensive review on handcrafted and learning-based action representation approaches for human activity recognition. Appl. Sci. 7(1), 110 (2017)","journal-title":"Appl. Sci."},{"key":"3210_CR20","doi-asserted-by":"crossref","unstructured":"Wang, L., Koniusz, P., Huynh, D. Q.: Hallucinating IDT descriptors and I3D optical flow features for action recognition with CNNs. In Proceedings of the IEEE International Conference on Computer Vision, pp. 8698\u20138708 (2019)","DOI":"10.1109\/ICCV.2019.00879"},{"key":"3210_CR21","doi-asserted-by":"crossref","unstructured":"Wang, L., Koniusz, P.: Self-supervising action recognition by statistical moment and subspace descriptors. In Proceedings of the 29th ACM international conference on multimedia, pp. 4324\u20134333 (2021)","DOI":"10.1145\/3474085.3475572"},{"key":"3210_CR22","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In Proceedings of the IEEE International Conference on Computer Vision, pp. 4489\u20134497 (2015)","DOI":"10.1109\/ICCV.2015.510"},{"key":"3210_CR23","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pp. 448\u2013456 (2015)"},{"key":"3210_CR24","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition, pp. 1\u201314 (2014)"},{"key":"3210_CR25","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"3210_CR26","doi-asserted-by":"publisher","first-page":"3168","DOI":"10.1109\/TIP.2019.2957930","volume":"29","author":"Y Liu","year":"2019","unstructured":"Liu, Y., Lu, Z., Li, J., Yang, T., Yao, C.: Deep image-to-video adaptation and fusion networks for action recognition. IEEE Trans. Image Process. 29, 3168\u20133182 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"3210_CR27","doi-asserted-by":"crossref","unstructured":"Carreira, J., Zisserman, A.: Quo Vadis, action recognition? A new model and the kinetics dataset. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299\u20136308 (2017)","DOI":"10.1109\/CVPR.2017.502"},{"key":"3210_CR28","unstructured":"Kay, W. et al.: The kinetics human action video dataset. Preprint at arXiv Prepr. arXiv1705.06950, 2017."},{"key":"3210_CR29","unstructured":"Soomro, K., Zamir, A. R., Shah, M.: UCF101: a dataset of 101 human actions classes from videos in the wild. Preprint at arXiv Prepr. arXiv1212.0402, 2012."},{"key":"3210_CR30","doi-asserted-by":"crossref","unstructured":"Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: HMDB: a large video database for human motion recognition. In Proceedings of the IEEE International Conference on Computer Vision, pp. 2556\u20132563 (2011)","DOI":"10.1109\/ICCV.2011.6126543"},{"key":"3210_CR31","unstructured":"Zhu, Y., Newsam, S.: Motion-aware feature for improved video anomaly detection. 30th Br. Mach. Vis. Conf. 2019, BMVC 2019, 2019"},{"key":"3210_CR32","doi-asserted-by":"publisher","first-page":"3454","DOI":"10.1049\/ipr2.12258","volume":"15","author":"B Wan","year":"2021","unstructured":"Wan, B., Jiang, W., Fang, Y., Luo, Z., Ding, G.: Anomaly detection in video sequences: a benchmark and computational model. IET Image Process. 15, 3454 (2021)","journal-title":"IET Image Process."},{"key":"3210_CR33","doi-asserted-by":"crossref","unstructured":"Cao, C., Lu, Y., Wang, P., Zhang, Y.: A new comprehensive benchmark for semi-supervised video anomaly detection and anticipation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20392\u201320401 (2023)","DOI":"10.1109\/CVPR52729.2023.01953"},{"issue":"2","key":"3210_CR34","doi-asserted-by":"publisher","first-page":"1248","DOI":"10.1109\/TII.2022.3179243","volume":"19","author":"Y Zhu","year":"2022","unstructured":"Zhu, Y., et al.: Hybrid-order representation learning for electricity theft detection. IEEE Trans. Ind. Inf. 19(2), 1248\u20131259 (2022)","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"1","key":"3210_CR35","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/TPAMI.2013.111","volume":"36","author":"W Li","year":"2014","unstructured":"Li, W., Mahadevan, V., Vasconcelos, N.: Anomaly detection and localization in crowded scenes. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 18\u201332 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3210_CR36","doi-asserted-by":"crossref","unstructured":"Kratz, L., Nishino, K.: Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. In IEEE conference on computer vision and pattern recognition, pp. 1446\u20131453 (2009)","DOI":"10.1109\/CVPR.2009.5206771"},{"key":"3210_CR37","doi-asserted-by":"crossref","unstructured":"Zhao, B., Fei-Fei, L., Xing, E. P.: Online detection of unusual events in videos via dynamic sparse coding. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3313\u20133320 (2011)","DOI":"10.1109\/CVPR.2011.5995524"},{"key":"3210_CR38","doi-asserted-by":"crossref","unstructured":"Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 935\u2013942 (2009)","DOI":"10.1109\/CVPR.2009.5206641"},{"key":"3210_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2020.104078","volume":"106","author":"R Nayak","year":"2020","unstructured":"Nayak, R., Pati, U.C., Das, S.K.: A comprehensive review on deep learning-based methods for video anomaly detection. Image Vis. Comput. 106, 104078 (2020)","journal-title":"Image Vis. Comput."},{"key":"3210_CR40","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3040591","author":"B Ramachandra","year":"2020","unstructured":"Ramachandra, B., Jones, M.J., Vatsavai, R.R.: A survey of single-scene video anomaly detection. IEEE Trans. Pattern Anal. Mach. Intell. (2020). https:\/\/doi.org\/10.1109\/TPAMI.2020.3040591","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3210_CR41","doi-asserted-by":"crossref","unstructured":"Chidananda, K., Kumar, S.: Human anomaly detection in surveillance videos: a review. Inf. Commun. Technol. Compet. Strateg., pp. 791\u2013802, 2022.","DOI":"10.1007\/978-981-16-0739-4_75"},{"key":"3210_CR42","doi-asserted-by":"crossref","unstructured":"Zhu, S., Chen, C., Sultani, W.: Video anomaly detection for smart surveillance. Preprint at arXiv Prepr. arXiv2004.00222, 2020","DOI":"10.1007\/978-3-030-03243-2_845-1"},{"key":"3210_CR43","doi-asserted-by":"crossref","unstructured":"Kim, J., Grauman, K.: Observe locally, infer globally: a space-time MRF for detecting abnormal activities with incremental updates. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132928 (2009)","DOI":"10.1109\/CVPR.2009.5206569"},{"key":"3210_CR44","doi-asserted-by":"crossref","unstructured":"Ullah, H., Ullah, M., Conci, N.: Dominant motion analysis in regular and irregular crowd scenes. In International Workshop on Human Behavior Understanding, pp. 62\u201372 (2014)","DOI":"10.1007\/978-3-319-11839-0_6"},{"key":"3210_CR45","doi-asserted-by":"crossref","unstructured":"Cong, Y., Yuan, J., Liu, J.: Sparse reconstruction cost for abnormal event detection. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3449\u20133456 (2011)","DOI":"10.1109\/CVPR.2011.5995434"},{"key":"3210_CR46","doi-asserted-by":"crossref","unstructured":"Chong, Y. S., Tay, Y. H.: Abnormal event detection in videos using spatiotemporal autoencoder. In International Symposium on Neural Networks, pp. 189\u2013196 (2017)","DOI":"10.1007\/978-3-319-59081-3_23"},{"key":"3210_CR47","first-page":"2498","volume":"69","author":"Y Liu","year":"2022","unstructured":"Liu, Y., Liu, J., Lin, J., Zhao, M., Song, L.: Appearance-motion united auto-encoder framework for video anomaly detection. IEEE Trans. Circuits Syst. II Express Briefs 69, 2498 (2022)","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"issue":"1","key":"3210_CR48","first-page":"203","volume":"42","author":"FN Yuan","year":"2019","unstructured":"Yuan, F.N., Zhang, L., Shi, J.T., Xia, X., Li, G.: Theories and applications of auto-encoder neural networks: a literature survey. Jisuanji Xuebao\/Chinese J. Comput. 42(1), 203\u2013230 (2019)","journal-title":"Jisuanji Xuebao\/Chinese J. Comput."},{"issue":"8","key":"3210_CR49","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"3210_CR50","doi-asserted-by":"crossref","unstructured":"Luo, W., Liu, W., Gao, S.L Remembering history with convolutional LSTM for anomaly detection. In IEEE International Conference on Multimedia and Expo (ICME), pp. 439\u2013444 (2017)","DOI":"10.1109\/ICME.2017.8019325"},{"key":"3210_CR51","doi-asserted-by":"crossref","unstructured":"Luo, W., Liu, W., Gao, S.: Revisit of sparse coding based anomaly detection in stacked RNN framework. In Proceedings of the IEEE International Conference on Computer Vision, pp. 341\u2013349 (2017)","DOI":"10.1109\/ICCV.2017.45"},{"key":"3210_CR52","first-page":"13016","volume":"33","author":"L Shen","year":"2020","unstructured":"Shen, L., Li, Z., Kwok, J.T.: Timeseries anomaly detection using temporal hierarchical one-class network. Adv. Neural. Inf. Process. Syst. 33, 13016\u201313026 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3210_CR53","doi-asserted-by":"crossref","unstructured":"Gong, D. et al.: Memorizing normality to detect anomaly: memory-augmented deep autoencoder for unsupervised anomaly detection. In Proceedings of the IEEE International Conference on Computer Vision, pp. 1705\u20131714 (2019)","DOI":"10.1109\/ICCV.2019.00179"},{"key":"3210_CR54","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 Underst. 156, 117\u2013127 (2017)","journal-title":"Comput. Vis. Image Underst."},{"issue":"8","key":"3210_CR55","doi-asserted-by":"publisher","first-page":"2416","DOI":"10.1109\/TCSVT.2018.2868123","volume":"29","author":"Y Liu","year":"2019","unstructured":"Liu, Y., Lu, Z., Li, J., Yang, T.: Hierarchically learned view-invariant representations for cross-view action recognition. IEEE Trans. Circuits Syst. Video Technol. 29(8), 2416\u20132430 (2019)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"12","key":"3210_CR56","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11(12), 3371 (2010)","journal-title":"J. Mach. Learn. Res."},{"issue":"11","key":"3210_CR57","doi-asserted-by":"publisher","first-page":"13173","DOI":"10.1007\/s11042-017-4940-2","volume":"77","author":"MG Narasimhan","year":"2018","unstructured":"Narasimhan, M.G., Sowmya Kamath, S.: Dynamic video anomaly detection and localization using sparse denoising autoencoders. Multimed. Tools Appl. 77(11), 13173\u201313195 (2018)","journal-title":"Multimed. Tools Appl."},{"key":"3210_CR58","doi-asserted-by":"crossref","unstructured":"Dhole, H., Sutaone, M., Vyas, V.: Anomaly detection using convolutional spatiotemporal Autoencoder. In 2019 10th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2019, 2019","DOI":"10.1109\/ICCCNT45670.2019.8944523"},{"key":"3210_CR59","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Deng, B., Shen, C., Liu, Y., Lu, H., Hua, X. S.: Spatio-temporal AutoEncoder for video anomaly detection. In Proceedings of the 25th ACM international conference on Multimedia, pp. 1933\u20131941 (2017)","DOI":"10.1145\/3123266.3123451"},{"key":"3210_CR60","doi-asserted-by":"crossref","unstructured":"Chalapathy, R., Toth, E., Chawla, S.: Group anomaly detection using deep generative models. Lecture Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11051 LNAI, pp. 173\u2013189, 2019","DOI":"10.1007\/978-3-030-10925-7_11"},{"key":"3210_CR61","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"},{"key":"3210_CR62","doi-asserted-by":"publisher","first-page":"88170","DOI":"10.1109\/ACCESS.2020.2993373","volume":"8","author":"F Dong","year":"2020","unstructured":"Dong, F., Zhang, Y., Nie, X.: Dual discriminator generative adversarial network for video anomaly detection. IEEE Access 8, 88170 (2020)","journal-title":"IEEE Access"},{"key":"3210_CR63","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.: Integrating prediction and reconstruction for anomaly detection. Pattern Recognit. Lett. 129, 123\u2013130 (2020)","journal-title":"Pattern Recognit. Lett."},{"key":"3210_CR64","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. In Proceedings of the IEEE International Conference on Computer Vision, pp. 13588\u201313597 (2021)","DOI":"10.1109\/ICCV48922.2021.01333"},{"issue":"22","key":"3210_CR65","doi-asserted-by":"publisher","first-page":"29573","DOI":"10.1007\/s11042-017-5255-z","volume":"77","author":"C He","year":"2018","unstructured":"He, C., Shao, J., Sun, J.: An anomaly-introduced learning method for abnormal event detection. Multimed. Tools Appl. 77(22), 29573\u201329588 (2018)","journal-title":"Multimed. Tools Appl."},{"key":"3210_CR66","doi-asserted-by":"crossref","unstructured":"Zhong, J. X., Li, N., Kong, W., Liu, S., Li, T. H., Li, G.: Graph convolutional label noise cleaner: train a plug-and-play action classifier for anomaly detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1237\u20131246 (2019)","DOI":"10.1109\/CVPR.2019.00133"},{"key":"3210_CR67","doi-asserted-by":"crossref","unstructured":"Shah, A. P., Lamare, J. B., Nguyen-Anh, T., Hauptmann, A.: CADP: a novel dataset for CCTV traffic camera based accident analysis. In IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1\u20139 (2019)","DOI":"10.1109\/AVSS.2018.8639160"},{"key":"3210_CR68","unstructured":"Bai, S.: et al.: Traffic anomaly detection via perspective map based on spatial-temporal information matrix. In Proc. CVPR Workshops, pp. 117\u2013124 (2019)"},{"key":"3210_CR69","unstructured":"Wang, G., Yuan, X., Zhang, A., Hsu, H.-M., Hwang, J.-N.: Anomaly candidate identification and starting time estimation of vehicles from traffic videos. In AI City Challenge Workshop, IEEE\/CVF Computer Vision and Pattern Recognition (CVPR) Conference, Long Beach, California, pp. 382\u2013390 (2019)"},{"key":"3210_CR70","doi-asserted-by":"crossref","unstructured":"Hinami, R., Mei, T., Satoh, S.: Joint detection and recounting of abnormal events by learning deep generic knowledge. In Proceedings of the IEEE International Conference on Computer Vision (2017)","DOI":"10.1109\/ICCV.2017.391"},{"key":"3210_CR71","doi-asserted-by":"crossref","unstructured":"Se, S. A. P., Ravanbakhsh, M., Nabi, M., Mousavi, H., Sangineto, E., Sebe, N.: Plug-and-play CNN for crowd motion analysis: An application in abnormal event detection. In Proceedings-2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018 (2018)","DOI":"10.1109\/WACV.2018.00188"},{"key":"3210_CR72","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.cviu.2018.02.006","volume":"172","author":"M Sabokrou","year":"2018","unstructured":"Sabokrou, M., Fayyaz, M., Fathy, M., Moayed, Z., Klette, R.: Deep-anomaly: fully convolutional neural network for fast anomaly detection in crowded scenes. Comput. Vis. Image Underst. 172, 88\u201397 (2018)","journal-title":"Comput. Vis. Image Underst."},{"issue":"4","key":"3210_CR73","doi-asserted-by":"publisher","first-page":"1992","DOI":"10.1109\/TIP.2017.2670780","volume":"26","author":"M Sabokrou","year":"2017","unstructured":"Sabokrou, M., Fayyaz, M., Fathy, M., Klette, R.: Deep-cascade: cascading 3D deep neural networks for fast anomaly detection and localization in crowded scenes. IEEE Trans. Image Process. 26(4), 1992\u20132004 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"3210_CR74","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3284038","author":"Y Liu","year":"2023","unstructured":"Liu, Y., Li, G., Lin, L.: Cross-modal causal relational reasoning for event-level visual question answering. IEEE Trans. Pattern Anal. Mach. Intell. (2023). https:\/\/doi.org\/10.1109\/TPAMI.2023.3284038","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3210_CR75","doi-asserted-by":"publisher","first-page":"1978","DOI":"10.1109\/TIP.2022.3147032","volume":"31","author":"Y Liu","year":"2022","unstructured":"Liu, Y., Wang, K., Liu, L., Lan, H., Lin, L.: TCGL: temporal contrastive graph for self-supervised video representation learning. IEEE Trans. Image Process. 31, 1978\u20131993 (2022)","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"3210_CR76","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1007\/s11633-022-1362-z","volume":"19","author":"Y Liu","year":"2022","unstructured":"Liu, Y., Wei, Y.S., Yan, H., Bin Li, G., Lin, L.: Causal reasoning meets visual representation learning: a prospective study. Mach. Intell. Res. 19(6), 485\u2013511 (2022)","journal-title":"Mach. Intell. Res."},{"key":"3210_CR77","doi-asserted-by":"crossref","unstructured":"Wang, L., Huynh, D. Q., Mansour, M. R.: Loss switching fusion with similarity search for video classification. In IEEE International Conference on Image Processing (ICIP), pp. 974\u2013978 (2019)","DOI":"10.1109\/ICIP.2019.8803051"},{"key":"3210_CR78","doi-asserted-by":"crossref","unstructured":"Wang, L., Koniusz, P.: Uncertainty-DTW for time series and sequences. In European Conference on Computer Vision, pp. 176\u2013195 (2022)","DOI":"10.1007\/978-3-031-19803-8_11"},{"key":"3210_CR79","doi-asserted-by":"crossref","unstructured":"Wang, L., Koniusz, P.: Temporal-viewpoint transportation plan for skeletal few-shot action recognition. In Proceedings of the Asian Conference on Computer Vision, pp. 4176\u20134193 (2022)","DOI":"10.1007\/978-3-031-26316-3_19"},{"issue":"2","key":"3210_CR80","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1109\/TPAMI.2021.3107160","volume":"44","author":"P Koniusz","year":"2021","unstructured":"Koniusz, P., Wang, L., Cherian, A.: Tensor representations for action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 44(2), 648\u2013665 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3210_CR81","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3201518","author":"Z Qin","year":"2022","unstructured":"Qin, Z., et al.: Fusing Higher-Order Features in Graph Neural Networks for Skeleton-Based Action Recognition. IEEE Trans. Neural Netw. Learn. Syst. (2022). https:\/\/doi.org\/10.1109\/TNNLS.2022.3201518","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"3210_CR82","doi-asserted-by":"crossref","unstructured":"Wang, L., Koniusz, P.: 3Mformer: multi-order multi-mode transformer for skeletal action recognition. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5620\u20135631 (2023)","DOI":"10.1109\/CVPR52729.2023.00544"},{"key":"3210_CR83","doi-asserted-by":"crossref","unstructured":"Chang, Y., Tu, Z., Xie, W., Yuan, J.: Clustering driven deep autoencoder for video anomaly detection. In Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, Proceedings, Part XV 16, pp. 329\u2013345 (2020)","DOI":"10.1007\/978-3-030-58555-6_20"},{"key":"3210_CR84","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108213","volume":"122","author":"Y Chang","year":"2022","unstructured":"Chang, Y., et al.: Video anomaly detection with spatio-temporal dissociation. Pattern Recognit. 122, 108213 (2022)","journal-title":"Pattern Recognit."},{"key":"3210_CR85","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. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 11996\u201312004 (2019)","DOI":"10.1109\/CVPR.2019.01227"},{"key":"3210_CR86","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-023-02783-4","author":"M Yang","year":"2023","unstructured":"Yang, M., Feng, Y., Rao, A.S., Rajasegarar, S., Tian, S., Zhou, Z.: Evolving graph-based video crowd anomaly detection. Vis. Comput. (2023). https:\/\/doi.org\/10.1007\/s00371-023-02783-4","journal-title":"Vis. Comput."},{"key":"3210_CR87","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-023-02865-3","author":"TZ Ehsan","year":"2023","unstructured":"Ehsan, T.Z., Nahvi, M., Mohtavipour, S.M.: An accurate violence detection framework using unsupervised spatial\u2013temporal action translation network. Vis. Comput. (2023). https:\/\/doi.org\/10.1007\/s00371-023-02865-3","journal-title":"Vis. Comput."},{"key":"3210_CR88","doi-asserted-by":"crossref","unstructured":"Zach, C., Pock, T., Bischof, H.: A duality based approach for realtime TV-L 1 optical flow. In Joint pattern recognition symposium, pp. 214\u2013223 (2007)","DOI":"10.1007\/978-3-540-74936-3_22"},{"key":"3210_CR89","doi-asserted-by":"crossref","unstructured":"Bailer, C., Taetz, B., Stricker, D.: Flow fields: dense correspondence fields for highly accurate large displacement optical flow estimation. In Proceedings of the IEEE international conference on computer vision, pp. 4015\u20134023 (2015)","DOI":"10.1109\/ICCV.2015.457"},{"key":"3210_CR90","doi-asserted-by":"crossref","unstructured":"Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: Flownet 2.0: Evolution of optical flow estimation with deep networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2462\u20132470 (2017)","DOI":"10.1109\/CVPR.2017.179"},{"key":"3210_CR91","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. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 8934\u20138943 (2018)","DOI":"10.1109\/CVPR.2018.00931"},{"key":"3210_CR92","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.: Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Trans. Pattern Anal. Mach. Intell. 30, 555 (2008)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3210_CR93","doi-asserted-by":"crossref","unstructured":"Ramachandra, B., Jones, M.: Street Scene: a new dataset and evaluation protocol for video anomaly detection. In The IEEE Winter Conference on Applications of Computer Vision, pp. 2569\u20132578 (2020)","DOI":"10.1109\/WACV45572.2020.9093457"},{"issue":"8","key":"3210_CR94","doi-asserted-by":"publisher","first-page":"2811","DOI":"10.3390\/s21082811","volume":"21","author":"W Ullah","year":"2021","unstructured":"Ullah, W., Ullah, A., Hussain, T., Khan, Z.A., Baik, S.W.: An efficient anomaly recognition framework using an attention residual LSTM in surveillance videos. Sensors 21(8), 2811 (2021)","journal-title":"Sensors"},{"key":"3210_CR95","doi-asserted-by":"crossref","unstructured":"Ling, C. X., Huang, J., Zhang, H.: AUC: a better measure than accuracy in comparing learning algorithms. In Conference of the canadian society for computational studies of intelligence, pp. 329\u2013341 (2003)","DOI":"10.1007\/3-540-44886-1_25"},{"issue":"3","key":"3210_CR96","doi-asserted-by":"publisher","first-page":"1344","DOI":"10.3390\/app11031344","volume":"11","author":"S Dubey","year":"2021","unstructured":"Dubey, S., Boragule, A., Gwak, J., Jeon, M.: Anomalous event recognition in videos based on joint learning of motion and appearance with multiple ranking measures. Appl. Sci. 11(3), 1344 (2021)","journal-title":"Appl. Sci."},{"key":"3210_CR97","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. In IJCAI International Joint Conference on Artificial Intelligence, pp. 3023\u20133030 (2019)","DOI":"10.24963\/ijcai.2019\/419"},{"key":"3210_CR98","unstructured":"Gianchandani, U., Tirupattur, P., Shah, M.: Weakly-Supervised Spatiotemporal Anomaly Detection. University of Central Florida Center for Research in Computer Vision REU, 2019"},{"key":"3210_CR99","doi-asserted-by":"publisher","first-page":"8876056","DOI":"10.1155\/2020\/8876056","volume":"2020","author":"W Hao","year":"2020","unstructured":"Hao, W., et al.: Anomaly event detection in security surveillance using two-stream based model. Secur. Commun. Netw. 2020, 8876056 (2020)","journal-title":"Secur. Commun. Netw."},{"issue":"3","key":"3210_CR100","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-020-00169-0","volume":"1","author":"DG Shreyas","year":"2020","unstructured":"Shreyas, D.G., Raksha, S., Prasad, B.G.: Implementation of an anomalous human activity recognition system. SN Comput. Sci. 1(3), 1\u201310 (2020)","journal-title":"SN Comput. Sci."},{"key":"3210_CR101","unstructured":"Zaheer, M. Z., Lee, J., Astrid, M., Mahmood, A., Lee, S.-I.: Cleaning label noise with clusters for minimally supervised anomaly detection. Preprint at arXiv e-prints pp. 3\u20136 (2021)"},{"key":"3210_CR102","doi-asserted-by":"crossref","unstructured":"Majhi, S., Das, S., Bremond, F., Dash, R., Sa, P. K.: Weakly-supervised joint anomaly detection and classification. In Proceedings - 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2021, pp. 1\u20137 (2021)","DOI":"10.1109\/FG52635.2021.9667006"},{"issue":"11","key":"3210_CR103","doi-asserted-by":"publisher","first-page":"16979","DOI":"10.1007\/s11042-020-09406-3","volume":"80","author":"W Ullah","year":"2021","unstructured":"Ullah, W., Ullah, A., Haq, I.U., Muhammad, K., Sajjad, M., Baik, S.W.: CNN features with bi-directional LSTM for real-time anomaly detection in surveillance networks. Multimed. Tools Appl. 80(11), 16979\u201316995 (2021)","journal-title":"Multimed. Tools Appl."},{"key":"3210_CR104","doi-asserted-by":"crossref","unstructured":"Cao, C., Zhang, X., Zhang, S., Wang, P., Zhang, Y.: Adaptive graph convolutional networks for weakly supervised anomaly detection in videos. Preprint at arXiv e-prints (2022)","DOI":"10.1109\/LSP.2022.3226411"},{"key":"3210_CR105","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117030","volume":"201","author":"KV Thakare","year":"2022","unstructured":"Thakare, K.V., Sharma, N., Dogra, D.P., Choi, H., Kim, I.J.: A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection. Expert Syst. Appl. 201, 117030 (2022)","journal-title":"Expert Syst. Appl."},{"key":"3210_CR106","doi-asserted-by":"crossref","unstructured":"Chen, Y., Liu, Z., Zhang, B., Fok, W., Qi, X., Wu, Y.: MGFN\u202f: magnitude-contrastive glance-and-focus network for weakly- supervised video anomaly detection MGFN\u202f: magnitude-contrastive glance-and-focus network for weakly-supervised video anomaly detection. Preprint at arXiv Prepr. arXiv2211.15098 (2022)","DOI":"10.1609\/aaai.v37i1.25112"},{"issue":"12","key":"3210_CR107","doi-asserted-by":"publisher","first-page":"18693","DOI":"10.1007\/s11042-021-10570-3","volume":"80","author":"R Maqsood","year":"2021","unstructured":"Maqsood, R., Bajwa, U.I., Saleem, G., Raza, R.H., Anwar, M.W.: Anomaly recognition from surveillance videos using 3D convolution neural network. Multimed. Tools Appl. 80(12), 18693\u201318716 (2021)","journal-title":"Multimed. Tools Appl."},{"issue":"9","key":"3210_CR108","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s21093179","volume":"21","author":"TH Vu","year":"2021","unstructured":"Vu, T.H., Boonaert, J., Ambellouis, S., Taleb-Ahmed, A.: Multi-channel generative framework and supervised learning for anomaly detection in surveillance videos. Sensors 21(9), 1\u201316 (2021)","journal-title":"Sensors"},{"key":"3210_CR109","doi-asserted-by":"crossref","unstructured":"Hou, R., Chen, C., Shah, M.: Tube convolutional neural network (T-CNN) for action detection in videos. In Proceedings of the IEEE International Conference on Computer Vision, vol. 2017-Octob, pp. 5822\u20135831. (2017)","DOI":"10.1109\/ICCV.2017.620"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-03210-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-023-03210-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-03210-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T16:12:28Z","timestamp":1730909548000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-023-03210-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,2]]},"references-count":109,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["3210"],"URL":"https:\/\/doi.org\/10.1007\/s00371-023-03210-4","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,2]]},"assertion":[{"value":"22 November 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2024","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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent statement"}}]}}