{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T03:28:33Z","timestamp":1769743713035,"version":"3.49.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T00:00:00Z","timestamp":1669334400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T00:00:00Z","timestamp":1669334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Fundamental Research Funds for the Central Universities","award":["2682021CX078"],"award-info":[{"award-number":["2682021CX078"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s10489-022-04233-5","type":"journal-article","created":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T20:02:34Z","timestamp":1669406554000},"page":"15695-15710","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["An efficient deep neural model for detecting crowd anomalies in videos"],"prefix":"10.1007","volume":"53","author":[{"given":"Meng","family":"Yang","sequence":"first","affiliation":[]},{"given":"Shucong","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Aravinda S.","family":"Rao","sequence":"additional","affiliation":[]},{"given":"Sutharshan","family":"Rajasegarar","sequence":"additional","affiliation":[]},{"given":"Marimuthu","family":"Palaniswami","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0228-7119","authenticated-orcid":false,"given":"Zhengchun","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,25]]},"reference":[{"key":"4233_CR1","doi-asserted-by":"crossref","unstructured":"Varadarajan J, Odobez J-M (2009) Topic models for scene analysis and abnormality detection. In: 2009 IEEE 12th international conference on computer vision workshops, pp 1338\u20131345","DOI":"10.1109\/ICCVW.2009.5457456"},{"issue":"2","key":"4233_CR2","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/S0953-5438(00)00038-2","volume":"13","author":"P Luff","year":"2000","unstructured":"Luff P, Heath C, Jirotka M (2000) Surveying the scene: technologies for everyday awareness and monitoring in control rooms. Interact Comput 13(2):193\u2013228","journal-title":"Interact Comput"},{"issue":"3","key":"4233_CR3","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1006\/cviu.1998.0744","volume":"73","author":"JK Aggarwal","year":"1999","unstructured":"Aggarwal JK, Cai Q (1999) Human motion analysis: a review. Comput Vis Image Underst 73(3):428\u2013440","journal-title":"Comput Vis Image Underst"},{"issue":"13","key":"4233_CR4","doi-asserted-by":"publisher","first-page":"1473","DOI":"10.1163\/156855307782148578","volume":"21","author":"V Kr\u00fcger","year":"2007","unstructured":"Kr\u00fcger V, Kragic D, Ude A, Geib C (2007) The meaning of action: a review on action recognition and mapping. Adv Robot 21(13):1473\u20131501","journal-title":"Adv Robot"},{"key":"4233_CR5","doi-asserted-by":"crossref","unstructured":"Rao AS, Gubbi J, Rajasegarar S, Marusic S, Palaniswami M (2014) Detection of anomalous crowd behaviour using hyperspherical clustering. In: 2014 International conference on digital image computing: techniques and applications (DICTA), pp 1\u20138","DOI":"10.1109\/DICTA.2014.7008100"},{"key":"4233_CR6","doi-asserted-by":"crossref","unstructured":"Yang M, Rajasegarar S, Erfani SM, Leckie C (2019) Deep learning and one-class svm based anomalous crowd detection. In: 2019 International joint conference on neural networks (IJCNN). IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN.2019.8852256"},{"key":"4233_CR7","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.patcog.2016.03.028","volume":"58","author":"SM Erfani","year":"2016","unstructured":"Erfani SM, Rajasegarar S, Karunasekera S, Leckie C (2016) High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. Pattern Recogn 58:121\u2013134","journal-title":"Pattern Recogn"},{"key":"4233_CR8","unstructured":"(2013). UCSD anomaly detection dataset. http:\/\/www.svcl.ucsd.edu\/projects\/anomaly\/dataset.html. Last Accessed 26 Feb 2022"},{"key":"4233_CR9","unstructured":"(2013). Avenue dataset for abnormal event detection. http:\/\/www.cse.cuhk.edu.hk\/leojia\/projects\/detectabnormal\/dataset.html. Last Accessed 26 Feb 2022"},{"key":"4233_CR10","doi-asserted-by":"crossref","unstructured":"Lu C, Shi J, Jia J (2013) Abnormal event detection at 150 fps in matlab. In: ICCV, pp 2720\u20132727","DOI":"10.1109\/ICCV.2013.338"},{"issue":"3","key":"4233_CR11","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 (2008) Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Trans Pattern Anal Mach Intell 30(3):555\u2013560","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"11","key":"4233_CR12","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1007\/s00371-014-1032-4","volume":"31","author":"AS Rao","year":"2015","unstructured":"Rao AS, Gubbi J, Marusic S, Palaniswami M (2015) Estimation of crowd density by clustering motion cues. Vis Comput 31(11):1533\u20131552","journal-title":"Vis Comput"},{"issue":"7540","key":"4233_CR13","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529\u2013533","journal-title":"Nature"},{"issue":"4","key":"4233_CR14","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1109\/TCSVT.2013.2280061","volume":"24","author":"X Mo","year":"2014","unstructured":"Mo X, Monga V, Bala R, Fan Z (2014) Adaptive sparse representations for video anomaly detection. IEEE Trans Circuits Syst Video Technol 24(4):631\u2013645","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"4233_CR15","doi-asserted-by":"crossref","unstructured":"Bird N, Atev S, Caramelli N, Martin R, Masoud O, Papanikolopoulos N (2006) Real time, online detection of abandoned objects in public areas. In: ICRA 2006. IEEE, pp 3775\u20133780","DOI":"10.1109\/ROBOT.2006.1642279"},{"key":"4233_CR16","doi-asserted-by":"crossref","unstructured":"Mohammadi S, Perina A, Kiani H, Murino V (2016) Angry crowds: detecting violent events in videos. In: European conference on computer vision. Springer, pp 3\u201318","DOI":"10.1007\/978-3-319-46478-7_1"},{"key":"4233_CR17","doi-asserted-by":"crossref","unstructured":"Mahadevan V, Li W, Bhalodia V, Vasconcelos N (2010) Anomaly detection in crowded scenes. In: 2010 IEEE computer society conference on computer vision and pattern recognition, pp 1975\u20131981","DOI":"10.1109\/CVPR.2010.5539872"},{"key":"4233_CR18","doi-asserted-by":"crossref","unstructured":"Liu W, Luo W, Lian D, Gao S (2018) Future frame prediction for anomaly detection\u2013a new baseline. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6536\u20136545","DOI":"10.1109\/CVPR.2018.00684"},{"key":"4233_CR19","doi-asserted-by":"crossref","unstructured":"Luo W, Liu W, Gao S (2017) A revisit of sparse coding based anomaly detection in stacked RNN framework. In: Proceedings of the IEEE international conference on computer vision, pp 341\u2013349","DOI":"10.1109\/ICCV.2017.45"},{"key":"4233_CR20","doi-asserted-by":"crossref","unstructured":"Xu D, Ricci E, Yan Y, Song J, Sebe N (2015) Learning deep representations of appearance and motion for anomalous event detection. arXiv:1510.01553","DOI":"10.5244\/C.29.8"},{"key":"4233_CR21","doi-asserted-by":"crossref","unstructured":"Feng Y, Yuan Y, Lu X (2016) Deep representation for abnormal event detection in crowded scenes. In: 2016 ACM on multimedia conference, pp 591\u2013595","DOI":"10.1145\/2964284.2967290"},{"key":"4233_CR22","doi-asserted-by":"crossref","unstructured":"Hasan M, Choi J, Neumann J, Roy-Chowdhury AK, Davis LS (2016) Learning temporal regularity in video sequences. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 733\u2013742","DOI":"10.1109\/CVPR.2016.86"},{"key":"4233_CR23","doi-asserted-by":"crossref","unstructured":"Chong YS, Tay YH (2017) Abnormal event detection in videos using spatiotemporal autoencoder. In: International symposium on neural networks. Springer, pp 189\u2013196","DOI":"10.1007\/978-3-319-59081-3_23"},{"issue":"3","key":"4233_CR24","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 (2021) Anomalous event recognition in videos based on joint learning of motion and appearance with multiple ranking measures. Appl Sci 11(3):1344","journal-title":"Appl Sci"},{"key":"4233_CR25","doi-asserted-by":"crossref","unstructured":"Morais R, Le V, Tran T, Saha B, Mansour M, Venkatesh S (2019) Learning regularity in skeleton trajectories for anomaly detection in videos. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11996\u201312004","DOI":"10.1109\/CVPR.2019.01227"},{"key":"4233_CR26","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 (2018) Deep-anomaly: fully convolutional neural network for fast anomaly detection in crowded scenes. Comput Vis Image Underst 172:88\u201397","journal-title":"Comput Vis Image Underst"},{"key":"4233_CR27","doi-asserted-by":"crossref","unstructured":"Ravanbakhsh M, Nabi M, Mousavi H, Sangineto E, Sebe N (2018) Plug-and-play cnn for crowd motion analysis: an application ine abnormal event detection. In: 2018 IEEE winter conference on applications of computer vision (WACV), pp 1689\u20131698","DOI":"10.1109\/WACV.2018.00188"},{"key":"4233_CR28","doi-asserted-by":"crossref","unstructured":"Lu X, Wang W, Ma C, Shen J, Shao L, Porikli F (2019) See more, know more: unsupervised video object segmentation with co-attention siamese networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3623\u20133632","DOI":"10.1109\/CVPR.2019.00374"},{"key":"4233_CR29","doi-asserted-by":"crossref","unstructured":"Lu X, Wang W, Shen J, Crandall D, Luo J (2020) Zero-shot video object segmentation with co-attention siamese networks. IEEE transactions on pattern analysis and machine intelligence","DOI":"10.1109\/TPAMI.2020.3040258"},{"key":"4233_CR30","doi-asserted-by":"crossref","unstructured":"Mishra SR, Mishra TK, Sarkar A, Sanyal G (2020) Detection of anomalies in human action using optical flow and gradient tensor. In: Smart intelligent computing and applications. Springer, pp 561\u2013570","DOI":"10.1007\/978-981-13-9282-5_53"},{"key":"4233_CR31","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/j.patrec.2020.04.031","volume":"135","author":"SR Mishra","year":"2020","unstructured":"Mishra SR, Mishra TK, Sanyal G, Sarkar A, Satapathy SC (2020) Real time human action recognition using triggered frame extraction and a typical cnn heuristic. Pattern Recogn Lett 135:329\u2013336","journal-title":"Pattern Recogn Lett"},{"issue":"4","key":"4233_CR32","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1109\/TMI.2021.3123547","volume":"41","author":"MH Jafari","year":"2021","unstructured":"Jafari MH, Luong C, Tsang M, Gu AN, Van Woudenberg N, Rohling R, Tsang T, Abolmaesumi P (2021) U-land: uncertainty-driven video landmark detection. IEEE Trans Med Imaging 41(4):793\u2013804","journal-title":"IEEE Trans Med Imaging"},{"issue":"6","key":"4233_CR33","doi-asserted-by":"publisher","first-page":"1290","DOI":"10.1109\/TCSVT.2016.2539878","volume":"27","author":"J Shao","year":"2016","unstructured":"Shao J, Loy CC, Wang X (2016) Learning scene-independent group descriptors for crowd understanding. IEEE Trans Circuits Syst Video Technol 27(6):1290\u20131303","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"4233_CR34","doi-asserted-by":"crossref","unstructured":"Ghafoori Z, Rajasegarar S, Erfani SM, Karunasekera S, Leckie CA (2016) Unsupervised parameter estimation for one-class support vector machines. In: Pacific-asia conference on knowledge discovery and data mining. Springer, pp 183\u2013195","DOI":"10.1007\/978-3-319-31750-2_15"},{"key":"4233_CR35","doi-asserted-by":"crossref","unstructured":"Snoek CG, Worring M, Smeulders AW (2005) Early versus late fusion in semantic video analysis. In: Proceedings of the 13th annual ACM international conference on multimedia, pp 399\u2013402","DOI":"10.1145\/1101149.1101236"},{"key":"4233_CR36","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"4233_CR37","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"issue":"3","key":"4233_CR38","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.1109\/TPAMI.2019.2944377","volume":"43","author":"W Luo","year":"2019","unstructured":"Luo W, Liu W, Lian D, Tang J, Duan L, Peng X, Gao S (2019) Video anomaly detection with sparse coding inspired deep neural networks. IEEE Trans Pattern Anal Mach Intell 43(3):1070\u20131084","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4233_CR39","doi-asserted-by":"crossref","unstructured":"Kim J, Grauman K (2009) Observe locally, infer globally: a space-time mrf for detecting abnormal activities with incremental updates. In: 2009 IEEE conference on computer vision and pattern recognition, pp 2921\u20132928","DOI":"10.1109\/CVPR.2009.5206569"},{"key":"4233_CR40","doi-asserted-by":"crossref","unstructured":"Reddy V, Sanderson C, Lovell BC (2011) Improved anomaly detection in crowded scenes via cell-based analysis of foreground speed, size and texture. In: CVPRW. IEEE, pp 55\u201361","DOI":"10.1109\/CVPRW.2011.5981799"},{"key":"4233_CR41","doi-asserted-by":"crossref","unstructured":"Cong Y, Yuan J, Liu J (2011) Sparse reconstruction cost for abnormal event detection. In: CVPR. IEEE, pp 3449\u20133456","DOI":"10.1109\/CVPR.2011.5995434"},{"issue":"7","key":"4233_CR42","doi-asserted-by":"publisher","first-page":"3463","DOI":"10.1109\/TIP.2017.2695105","volume":"26","author":"R Leyva","year":"2017","unstructured":"Leyva R, Sanchez V, Li C-T (2017) Video anomaly detection with compact feature sets for online performance. IEEE Trans Image Process 26(7):3463\u20133478","journal-title":"IEEE Trans Image Process"},{"key":"4233_CR43","doi-asserted-by":"crossref","unstructured":"Turchini F, Seidenari L, Bimbo AD (2017) Convex polytope ensembles for spatio-temporal anomaly detection. In: International conference on image analysis and processing. Springer, pp 174\u2013184","DOI":"10.1007\/978-3-319-68560-1_16"},{"key":"4233_CR44","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1016\/j.patcog.2016.06.016","volume":"61","author":"R Chaker","year":"2017","unstructured":"Chaker R, Al Aghbari Z, Junejo IN (2017) Social network model for crowd anomaly detection and localization. Pattern Recogn 61:266\u2013281","journal-title":"Pattern Recogn"},{"key":"4233_CR45","doi-asserted-by":"crossref","unstructured":"Luo W, Liu W, Gao S (2017) Remembering history with convolutional lstm for anomaly detection. In: 2017 IEEE international conference on multimedia and expo (ICME). IEEE, pp 439\u2013444","DOI":"10.1109\/ICME.2017.8019325"},{"key":"4233_CR46","doi-asserted-by":"crossref","unstructured":"Ionescu RT, Smeureanu S, Popescu M, Alexe B (2018) Detecting abnormal events in video using narrowed motion clusters. arXiv:1801.05030","DOI":"10.1109\/WACV.2019.00212"},{"key":"4233_CR47","doi-asserted-by":"crossref","unstructured":"Smeureanu S, Ionescu RT, Popescu M, Alexe B (2017) Deep appearance features for abnormal behavior detection in video. In: International conference on image analysis and processing. Springer, pp 779\u2013789","DOI":"10.1007\/978-3-319-68548-9_70"},{"key":"4233_CR48","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04233-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-04233-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04233-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T04:02:30Z","timestamp":1685592150000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-04233-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,25]]},"references-count":48,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["4233"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-04233-5","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,25]]},"assertion":[{"value":"1 October 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}