{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T14:49:44Z","timestamp":1776782984939,"version":"3.51.2"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T00:00:00Z","timestamp":1617321600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T00:00:00Z","timestamp":1617321600000},"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":["Vis Comput"],"published-print":{"date-parts":[[2022,5]]},"DOI":"10.1007\/s00371-021-02100-x","type":"journal-article","created":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T05:02:27Z","timestamp":1617339747000},"page":"1719-1730","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Crowd anomaly detection with LSTMs using optical features and domain knowledge for improved inferring"],"prefix":"10.1007","volume":"38","author":[{"given":"Mohammad","family":"Sabih","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1026-0047","authenticated-orcid":false,"given":"Dinesh Kumar","family":"Vishwakarma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,4,2]]},"reference":[{"key":"2100_CR1","doi-asserted-by":"publisher","unstructured":"Hettiarachchi, A.L., Thathsarani, H.O., Wickramasinghe, P.U., Wickramasuriya, D.S., Rodrigo, R.: Extensible video surveillance software with simultaneous event detection for low and high density crowd analysis. In: 7th International Conference on Information and Automation for Sustainability, Colombo, Sri Lanka, pp. 1\u20136 (2014). https:\/\/doi.org\/10.1109\/ICIAFS.2014.7069590","DOI":"10.1109\/ICIAFS.2014.7069590"},{"key":"2100_CR2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2008.2005599","author":"C Piciarelli","year":"2008","unstructured":"Piciarelli, C., Micheloni, C., Foresti, G.L.: Trajectory-based anomalous event detection. IEEE Trans. Circuits Syst. Video Technol. (2008). https:\/\/doi.org\/10.1109\/TCSVT.2008.2005599","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"2100_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2010.11.003","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. (2011). https:\/\/doi.org\/10.1016\/j.imavis.2010.11.003","journal-title":"Image Vis. Comput."},{"key":"2100_CR4","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2670780","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. (2017). https:\/\/doi.org\/10.1109\/TIP.2017.2670780","journal-title":"IEEE Trans. Image Process."},{"key":"2100_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3692-x","author":"S Xie","year":"2019","unstructured":"Xie, S., Zhang, X., Cai, J.: Video crowd detection and abnormal behavior model detection based on machine learning method. Neural Comput. Appl. (2019). https:\/\/doi.org\/10.1007\/s00521-018-3692-x","journal-title":"Neural Comput. Appl."},{"key":"2100_CR6","doi-asserted-by":"publisher","unstructured":"Ojha, N., Vaish, A.: Spatiooral anomaly detection in crowd movement using SIFT. In: 2nd International Conference on Inventive Systems and Control (ICISC), Coimbatore, India, pp. 646\u2013654 (2018). https:\/\/doi.org\/10.1109\/ICISC.2018.8398878","DOI":"10.1109\/ICISC.2018.8398878"},{"key":"2100_CR7","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.neucom.2019.08.059","volume":"371","author":"K Singh","year":"2020","unstructured":"Singh, K., Rajora, S., Vishwakarma, D.K., Tripathi, G., Kumar, S., Walia, G.S.: Crowd anomaly detection using aggregation of ensembles of fine-tuned ConvNets. Neurocomputing 371, 188\u2013198 (2020). https:\/\/doi.org\/10.1016\/j.neucom.2019.08.059","journal-title":"Neurocomputing"},{"key":"2100_CR8","doi-asserted-by":"publisher","unstructured":"Zhuang, N., Ye, J., Hua, K.A.: Convolutional DLSTM for crowd scene understanding. In: Proc.\u20142017 IEEE Int. Symp. Multimedia, ISM 2017, vol. 2017-Janua, pp. 61\u201368. https:\/\/doi.org\/10.1109\/ISM.2017.19 (2017)","DOI":"10.1109\/ISM.2017.19"},{"key":"2100_CR9","doi-asserted-by":"publisher","unstructured":"Dhole, H., Sutaone, M., Vyas, V.: Anomaly detection using convolutional spatiotemporal autoencoder. In: 2019 10th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2019, pp. 1\u20135. https:\/\/doi.org\/10.1109\/ICCCNT45670.2019.8944523 (2019)","DOI":"10.1109\/ICCCNT45670.2019.8944523"},{"key":"2100_CR10","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. (1997). https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput."},{"key":"2100_CR11","doi-asserted-by":"publisher","DOI":"10.1109\/78.650093","author":"M Schuster","year":"1997","unstructured":"Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. (1997). https:\/\/doi.org\/10.1109\/78.650093","journal-title":"IEEE Trans. Signal Process."},{"key":"2100_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.09.063","author":"Y Feng","year":"2017","unstructured":"Feng, Y., Yuan, Y., Lu, X.: Learning deep event models for crowd anomaly detection. Neurocomputing (2017). https:\/\/doi.org\/10.1016\/j.neucom.2016.09.063","journal-title":"Neurocomputing"},{"key":"2100_CR13","doi-asserted-by":"publisher","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 8.1\u20138.12 (2015). https:\/\/doi.org\/10.5244\/c.29.8","DOI":"10.5244\/c.29.8"},{"key":"2100_CR14","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.111","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. (2014). https:\/\/doi.org\/10.1109\/TPAMI.2013.111","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2100_CR15","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.neucom.2014.06.011","volume":"143","author":"D Xu","year":"2014","unstructured":"Xu, D., Song, R., Wu, X., Li, N., Feng, W., Qian, H.: Video anomaly detection based on a hierarchical activity discovery within spatio-temporal contexts. Neurocomputing 143, 144\u2013152 (2014). https:\/\/doi.org\/10.1016\/j.neucom.2014.06.011","journal-title":"Neurocomputing"},{"issue":"10","key":"2100_CR16","doi-asserted-by":"publisher","first-page":"1590","DOI":"10.1109\/TIFS.2013.2272243","volume":"8","author":"Y Cong","year":"2013","unstructured":"Cong, Y., Yuan, J., Tang, Y.: Video anomaly search in crowded scenes via spatio-temporal motion context. IEEE Trans. Inf. Forensics Secur. 8(10), 1590\u20131599 (2013). https:\/\/doi.org\/10.1109\/TIFS.2013.2272243","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"2","key":"2100_CR17","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/s11760-016-0935-0","volume":"11","author":"AR Revathi","year":"2017","unstructured":"Revathi, A.R., Kumar, D.: An efficient system for anomaly detection using deep learning classifier. Signal Image Video Process. 11(2), 291\u2013299 (2017). https:\/\/doi.org\/10.1007\/s11760-016-0935-0","journal-title":"Signal Image Video Process."},{"key":"2100_CR18","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2019.2900907","author":"JT Zhou","year":"2019","unstructured":"Zhou, J.T., Du, J., Zhu, H., Peng, X., Liu, Y., Goh, R.S.M.: AnomalyNet: an anomaly detection network for video surveillance. IEEE Trans. Inf. Forensics Secur. (2019). https:\/\/doi.org\/10.1109\/TIFS.2019.2900907","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"1","key":"2100_CR19","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1109\/TMM.2018.2846411","volume":"21","author":"W Chu","year":"2019","unstructured":"Chu, W., Xue, H., Yao, C., Cai, D.: Sparse coding guided spatiotemporal feature learning for abnormal event detection in large videos. IEEE Trans. Multimed. 21(1), 246\u2013255 (2019). https:\/\/doi.org\/10.1109\/TMM.2018.2846411","journal-title":"IEEE Trans. Multimed."},{"issue":"2","key":"2100_CR20","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1109\/TIFS.2018.2856189","volume":"14","author":"MUK Khan","year":"2018","unstructured":"Khan, M.U.K., Park, H.S., Kyung, C.M.: Rejecting motion outliers for efficient crowd anomaly detection. IEEE Trans. Inf. Forensics Secur. 14(2), 541\u2013556 (2018). https:\/\/doi.org\/10.1109\/TIFS.2018.2856189","journal-title":"IEEE Trans. Inf. Forensics Secur."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-021-02100-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-021-02100-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-021-02100-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T13:12:46Z","timestamp":1649855566000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-021-02100-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,2]]},"references-count":20,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,5]]}},"alternative-id":["2100"],"URL":"https:\/\/doi.org\/10.1007\/s00371-021-02100-x","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,2]]},"assertion":[{"value":"23 February 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}