{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T09:59:12Z","timestamp":1774951152674,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T00:00:00Z","timestamp":1697587200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T00:00:00Z","timestamp":1697587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100008628","name":"Ministry of Electronics and Information technology","doi-asserted-by":"publisher","award":["4(16)\/2019-ITEA"],"award-info":[{"award-number":["4(16)\/2019-ITEA"]}],"id":[{"id":"10.13039\/501100008628","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s11760-023-02750-5","type":"journal-article","created":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T14:02:35Z","timestamp":1697637755000},"page":"821-831","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["VALD-GAN: video anomaly detection using latent discriminator augmented GAN"],"prefix":"10.1007","volume":"18","author":[{"given":"Rituraj","family":"Singh","sequence":"first","affiliation":[]},{"given":"Anikeit","family":"Sethi","sequence":"additional","affiliation":[]},{"given":"Krishanu","family":"Saini","sequence":"additional","affiliation":[]},{"given":"Sumeet","family":"Saurav","sequence":"additional","affiliation":[]},{"given":"Aruna","family":"Tiwari","sequence":"additional","affiliation":[]},{"given":"Sanjay","family":"Singh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,18]]},"reference":[{"key":"2750_CR1","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.patcog.2016.09.016","volume":"64","author":"Q Sun","year":"2017","unstructured":"Sun, Q., Liu, H., Harada, T.: Online growing neural gas for anomaly detection in changing surveillance scenes. Pattern Recogn. 64, 187\u2013201 (2017)","journal-title":"Pattern Recogn."},{"key":"2750_CR2","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":"17","key":"2750_CR3","doi-asserted-by":"publisher","first-page":"25875","DOI":"10.1007\/s11042-021-10921-0","volume":"80","author":"Z Aziz","year":"2021","unstructured":"Aziz, Z., Bhatti, N., Mahmood, H., Zia, M.: Video anomaly detection and localization based on appearance and motion models. Multimed. Tools Appl. 80(17), 25875\u201325895 (2021)","journal-title":"Multimed. Tools Appl."},{"issue":"3","key":"2750_CR4","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1007\/s11390-017-1737-8","volume":"32","author":"R Ye","year":"2017","unstructured":"Ye, R., Li, X.: Collective representation for abnormal event detection. J. Comput. Sci. Technol. 32(3), 470\u2013479 (2017)","journal-title":"J. Comput. Sci. Technol."},{"key":"2750_CR5","doi-asserted-by":"crossref","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. In: International Conference on Information Processing in Medical Imaging, Springer, pp. 146\u2013157 (2017)","DOI":"10.1007\/978-3-319-59050-9_12"},{"issue":"4","key":"2750_CR6","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.3390\/s18041064","volume":"18","author":"S Mei","year":"2018","unstructured":"Mei, S., Wang, Y., Wen, G.: Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model. Sensors 18(4), 1064 (2018)","journal-title":"Sensors"},{"key":"2750_CR7","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.patcog.2016.11.026","volume":"64","author":"L Dong","year":"2017","unstructured":"Dong, L., Shulin, L., Zhang, H.: A method of anomaly detection and fault diagnosis with online adaptive learning under small training samples. Pattern Recogn. 64, 374\u2013385 (2017)","journal-title":"Pattern Recogn."},{"key":"2750_CR8","doi-asserted-by":"crossref","unstructured":"Yu, J.-H., Moon, J.-H., Sohn, K.-A.: Attention-guided residual frame learning for video anomaly detection. Multimed. Tools Appl. pp. 1\u201318 (2022)","DOI":"10.1007\/s11042-022-13643-z"},{"key":"2750_CR9","doi-asserted-by":"crossref","unstructured":"Isola, P., Xiao, J., Torralba, A., Oliva, A.: What makes an image memorable? In: CVPR 2011, IEEE, pp. 145\u2013152 (2011)","DOI":"10.1109\/CVPR.2011.5995721"},{"issue":"2","key":"2750_CR10","doi-asserted-by":"publisher","first-page":"2599","DOI":"10.1007\/s11042-020-09774-w","volume":"80","author":"A Chriki","year":"2021","unstructured":"Chriki, A., Touati, H., Snoussi, H., Kamoun, F.: Deep learning and handcrafted features for one-class anomaly detection in UAV video. Multimed. Tools Appl. 80(2), 2599\u20132620 (2021)","journal-title":"Multimed. Tools Appl."},{"key":"2750_CR11","doi-asserted-by":"crossref","unstructured":"Wang, J., Cherian, A.: Gods: Generalized one-class discriminative subspaces for anomaly detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8201\u20138211 (2019)","DOI":"10.1109\/ICCV.2019.00829"},{"issue":"16","key":"2750_CR12","doi-asserted-by":"publisher","first-page":"3337","DOI":"10.3390\/app9163337","volume":"9","author":"M Xu","year":"2019","unstructured":"Xu, M., Yu, X., Chen, D., Wu, C., Jiang, Y.: An efficient anomaly detection system for crowded scenes using variational autoencoders. Appl. Sci. 9(16), 3337 (2019)","journal-title":"Appl. Sci."},{"key":"2750_CR13","doi-asserted-by":"crossref","unstructured":"Liu, W., Luo, W., Lian, D., Gao, S.: Future frame prediction for anomaly detection\u2013a new baseline. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6536\u20136545 (2018)","DOI":"10.1109\/CVPR.2018.00684"},{"key":"2750_CR14","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 Conference on Computer Vision and Pattern Recognition, pp. 733\u2013742 (2016)","DOI":"10.1109\/CVPR.2016.86"},{"key":"2750_CR15","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":"2750_CR16","volume-title":"Computer vision-accv 2018","author":"S Akcay","year":"2019","unstructured":"Akcay, S., Atapour-Abarghouei, A., Breckon, T.: Computer vision-accv 2018. Semi-supervised Anomaly Detection via Adversarial Training, GANomaly (2019)"},{"issue":"7","key":"2750_CR17","doi-asserted-by":"publisher","first-page":"1885","DOI":"10.1007\/s11760-022-02148-9","volume":"16","author":"X Hu","year":"2022","unstructured":"Hu, X., Lian, J., Zhang, D., Gao, X., Jiang, L., Chen, W.: Video anomaly detection based on 3d convolutional auto-encoder. SIViP 16(7), 1885\u20131893 (2022)","journal-title":"SIViP"},{"key":"2750_CR18","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.: Spatial-temporal cascade autoencoder for video anomaly detection in crowded scenes. IEEE Trans. Multimed. 23, 203\u2013215 (2020)","journal-title":"IEEE Trans. Multimed."},{"key":"2750_CR19","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.patrec.2017.07.016","volume":"105","author":"M Ribeiro","year":"2018","unstructured":"Ribeiro, M., Lazzaretti, A.E., Lopes, H.S.: A study of deep convolutional auto-encoders for anomaly detection in videos. Pattern Recogn. Lett. 105, 13\u201322 (2018)","journal-title":"Pattern Recogn. Lett."},{"key":"2750_CR20","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"},{"issue":"1","key":"2750_CR21","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, C.K.: Residual spatiotemporal autoencoder for unsupervised video anomaly detection. SIViP 15(1), 215\u2013222 (2021)","journal-title":"SIViP"},{"key":"2750_CR22","doi-asserted-by":"crossref","unstructured":"Cong, Y., Yuan, J., Liu, J.: Sparse reconstruction cost for abnormal event detection. In: CVPR 2011, IEEE, pp. 3449\u20133456 (2011)","DOI":"10.1109\/CVPR.2011.5995434"},{"key":"2750_CR23","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, Springer, pp. 189\u2013196 (2017)","DOI":"10.1007\/978-3-319-59081-3_23"},{"key":"2750_CR24","doi-asserted-by":"crossref","unstructured":"Chalapathy, R., Menon, A.K., Chawla, S.: Robust, deep and inductive anomaly detection. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, pp. 36\u201351 (2017)","DOI":"10.1007\/978-3-319-71249-9_3"},{"key":"2750_CR25","doi-asserted-by":"crossref","unstructured":"Gordon, A., Li, H., Jonschkowski, R., Angelova, A.: Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8977\u20138986 (2019)","DOI":"10.1109\/ICCV.2019.00907"},{"key":"2750_CR26","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"},{"issue":"1","key":"2750_CR27","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/TCDS.2018.2883368","volume":"12","author":"S Yan","year":"2018","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 (2018)","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"2750_CR28","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhou, F., Li, Z., Zuo, W., Tan, H.: Abnormal event detection in videos using hybrid spatio-temporal autoencoder. In: 2018 25th IEEE International Conference on Image Processing (ICIP), IEEE, pp. 2276\u20132280 (2018)","DOI":"10.1109\/ICIP.2018.8451070"},{"issue":"1","key":"2750_CR29","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.: Spatiotemporal anomaly detection using deep learning for real-time video surveillance. IEEE Trans. Ind. Inf. 16(1), 393\u2013402 (2019)","journal-title":"IEEE Trans. Ind. Inf."},{"key":"2750_CR30","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Advances in Neural Information Processing Systems 27 (2014)"},{"issue":"2","key":"2750_CR31","doi-asserted-by":"publisher","first-page":"36","DOI":"10.3390\/jimaging4020036","volume":"4","author":"BR Kiran","year":"2018","unstructured":"Kiran, B.R., Thomas, D.M., Parakkal, R.: An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos. J. Imag. 4(2), 36 (2018)","journal-title":"J. Imag."},{"key":"2750_CR32","doi-asserted-by":"crossref","unstructured":"Lee, S., Kim, H.G., Ro, Y.M.: Stan: Spatio-temporal adversarial networks for abnormal event detection. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 1323\u20131327 (2018)","DOI":"10.1109\/ICASSP.2018.8462388"},{"key":"2750_CR33","doi-asserted-by":"crossref","unstructured":"Sabokrou, M., Pourreza, M., Fayyaz, M., Entezari, R., Fathy, M., Gall, J., Adeli, E.: Avid: Adversarial visual irregularity detection. In: Asian Conference on Computer Vision, Springer, pp. 488\u2013505 (2018)","DOI":"10.1007\/978-3-030-20876-9_31"},{"issue":"8","key":"2750_CR34","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. Multimed. 22(8), 2138\u20132148 (2019)","journal-title":"IEEE Trans. Multimed."},{"key":"2750_CR35","doi-asserted-by":"crossref","unstructured":"Ravanbakhsh, M., Sangineto, E., Nabi, M., Sebe, N.: Training adversarial discriminators for cross-channel abnormal event detection in crowds. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, pp. 1896\u20131904 (2019)","DOI":"10.1109\/WACV.2019.00206"},{"key":"2750_CR36","doi-asserted-by":"crossref","unstructured":"Pourreza, M., Mohammadi, B., Khaki, M., Bouindour, S., Snoussi, H., Sabokrou, M.: G2d: generate to detect anomaly. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2003\u20132012 (2021)","DOI":"10.1109\/WACV48630.2021.00205"},{"issue":"8","key":"2750_CR37","doi-asserted-by":"publisher","first-page":"3572","DOI":"10.1109\/TNNLS.2021.3053563","volume":"33","author":"J Yu","year":"2021","unstructured":"Yu, J., Lee, Y., Yow, K.C., Jeon, M., Pedrycz, W.: Abnormal event detection and localization via adversarial event prediction. IEEE Trans. Neural Netw. Learn. Syst. 33(8), 3572\u201386 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"2750_CR38","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning, PMLR, pp. 214\u2013223 (2017)"},{"key":"2750_CR39","unstructured":"Pidhorskyi, S., Almohsen, R., Doretto, G.: Generative probabilistic novelty detection with adversarial autoencoders. Advances in Neural Information Processing Systems 31 (2018)"},{"key":"2750_CR40","doi-asserted-by":"crossref","unstructured":"Puzicha, J., Hofmann, T., Buhmann, J.M.: Non-parametric similarity measures for unsupervised texture segmentation and image retrieval. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, pp. 267\u2013272 (1997)","DOI":"10.1109\/CVPR.1997.609331"},{"key":"2750_CR41","unstructured":"Chan, A., Vasconcelos, N.: UCSD pedestrian database. IEEE Trans. Pattern Anal. Mach. Intell. 6 (2008)"},{"key":"2750_CR42","doi-asserted-by":"crossref","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(3), 555\u2013560 (2008)","DOI":"10.1109\/TPAMI.2007.70825"},{"key":"2750_CR43","doi-asserted-by":"crossref","unstructured":"Tudor\u00a0Ionescu, R., Smeureanu, S., Alexe, B., Popescu, M.: Unmasking the abnormal events in video. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2895\u20132903 (2017)","DOI":"10.1109\/ICCV.2017.315"},{"key":"2750_CR44","doi-asserted-by":"crossref","unstructured":"Luo, W., Liu, W., Gao, S.: Remembering history with convolutional lstm for anomaly detection. In: 2017 IEEE International Conference on Multimedia and Expo (ICME), IEEE, pp. 439\u2013444 (2017)","DOI":"10.1109\/ICME.2017.8019325"},{"key":"2750_CR45","doi-asserted-by":"crossref","unstructured":"Perera, P., Nallapati, R., Xiang, B.: Ocgan: One-class novelty detection using gans with constrained latent representations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2898\u20132906 (2019)","DOI":"10.1109\/CVPR.2019.00301"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02750-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-023-02750-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02750-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T01:55:27Z","timestamp":1730339727000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-023-02750-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,18]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["2750"],"URL":"https:\/\/doi.org\/10.1007\/s11760-023-02750-5","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,18]]},"assertion":[{"value":"26 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 October 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declared that we have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This declaration is not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}