{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T03:34:43Z","timestamp":1775705683125,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,24]],"date-time":"2019-09-24T00:00:00Z","timestamp":1569283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672528"],"award-info":[{"award-number":["61672528"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773392"],"award-info":[{"award-number":["61773392"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&amp;D Program of China","award":["2018YFB1003203"],"award-info":[{"award-number":["2018YFB1003203"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Video anomaly detection is widely applied in modern society, which is achieved by sensors such as surveillance cameras. This paper learns anomalies by exploiting videos under the fully unsupervised setting. To avoid massive computation caused by back-prorogation in existing methods, we propose a novel efficient three-stage unsupervised anomaly detection method. In the first stage, we adopt random projection instead of autoencoder or its variants in previous works. Then we formulate the optimization goal as a least-square regression problem which has a closed-form solution, leading to less computational cost. The discriminative reconstruction losses of normal and abnormal events encourage us to roughly estimate normality that can be further sifted in the second stage with one-class support vector machine. In the third stage, to eliminate the instability caused by random parameter initializations, ensemble technology is performed to combine multiple anomaly detectors\u2019 scores. To the best of our knowledge, it is the first time that unsupervised ensemble technology is introduced to video anomaly detection research. As demonstrated by the experimental results on several video anomaly detection benchmark datasets, our algorithm robustly surpasses the recent unsupervised methods and performs even better than some supervised approaches. In addition, we achieve comparable performance contrast with the state-of-the-art unsupervised method with much less running time, indicating the effectiveness, efficiency, and robustness of our proposed approach.<\/jats:p>","DOI":"10.3390\/s19194145","type":"journal-article","created":{"date-parts":[[2019,9,25]],"date-time":"2019-09-25T03:51:18Z","timestamp":1569383478000},"page":"4145","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["An Efficient and Robust Unsupervised Anomaly Detection Method Using Ensemble Random Projection in Surveillance Videos"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0836-2973","authenticated-orcid":false,"given":"Jingtao","family":"Hu","sequence":"first","affiliation":[{"name":"School of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2305-7555","authenticated-orcid":false,"given":"En","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siqi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinwang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xifeng","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianping","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Cyberspace Science, Dongguan University of Technology, Dongguan 523808, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Del Giorno, A., Bagnell, J.A., and Hebert, M. (2016, January 8\u201316). A discriminative framework for anomaly detection in large videos. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46454-1_21"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tudor Ionescu, R., Smeureanu, S., Alexe, B., and Popescu, M. (2017, January 22\u201329). Unmasking the abnormal events in video. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.315"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, S., Zeng, Y., Liu, Q., Zhu, C., Zhu, E., and Yin, J. (2018, January 22\u201326). Detecting abnormality without knowing normality: A two-stage approach for unsupervised video abnormal event detection. Proceedings of the 2018 ACM Multimedia Conference on Multimedia Conference (MM), Seoul, Korea.","DOI":"10.1145\/3240508.3240615"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.1016\/j.patcog.2007.10.022","article-title":"A daily behavior enabled hidden Markov model for human behavior understanding","volume":"41","author":"Chung","year":"2008","journal-title":"Pattern Recognit."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Foroughi, H., Rezvanian, A., and Paziraee, A. (2008, January 16\u201319). Robust fall detection using human shape and multi-class support vector machine. Proceedings of the Sixth Indian Conference on Computer Vision, Graphics & Image Processing (ICVGIP), Bhubaneswar, India.","DOI":"10.1109\/ICVGIP.2008.49"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1109\/TIFS.2015.2406533","article-title":"Detection of face spoofing using visual dynamics","volume":"10","author":"Tirunagari","year":"2015","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lin, C.H., Liu, J.C., and Ho, C.H. (2008, January 24\u201326). Anomaly detection using LibSVM training tools. Proceedings of the IEEE 2008 International Conference on Information Security and Assurance (ISA 2008), Busan, South Korea.","DOI":"10.1109\/ISA.2008.12"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Basharat, A., Gritai, A., and Shah, M. (2008, January 24\u201326). Learning object motion patterns for anomaly detection and improved object detection. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska.","DOI":"10.1109\/CVPR.2008.4587510"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1109\/TCSVT.2008.2005599","article-title":"Trajectory-based anomalous event detection","volume":"18","author":"Piciarelli","year":"2008","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_10","unstructured":"Fu, Z., Hu, W., and Tan, T. (2005, January 11\u201314). Similarity based vehicle trajectory clustering and anomaly detection. Proceedings of the 2005 International Conference on Image Processing (ICIP), Genoa, Italy."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1109\/TPAMI.2007.70825","article-title":"Robust real-time unusual event detection using multiple fixed-location monitors","volume":"30","author":"Adam","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kim, J., and Grauman, K. (2009, January 20\u201325). Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach, FL, USA.","DOI":"10.1109\/CVPR.2009.5206569"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.cviu.2011.09.009","article-title":"Multi-scale and real-time non-parametric approach for anomaly detection and localization","volume":"116","author":"Bertini","year":"2012","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mahadevan, V., Li, W., Bhalodia, V., and Vasconcelos, N. (2010, January 13\u201318). Anomaly detection in crowded scenes. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539872"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/TPAMI.2013.111","article-title":"Anomaly detection and localization in crowded scenes","volume":"36","author":"Li","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Cong, Y., Yuan, J., and Liu, J. (2011, January 20\u201325). Sparse reconstruction cost for abnormal event detection. Proceedings of the IEEE CVPR 2011, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995434"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lu, C., Shi, J., and Jia, J. (2013, January 1\u20138). Abnormal event detection at 150 fps in matlab. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.338"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"33353","DOI":"10.1109\/ACCESS.2018.2848210","article-title":"Learning sparse representation with variational auto-encoder for anomaly detection","volume":"6","author":"Sun","year":"2018","journal-title":"IEEE Access"},{"key":"ref_19","unstructured":"Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A.K., and Davis, L.S. (July, January 26). Learning temporal regularity in video sequences. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tran, H.T., and Hogg, D. (2017, January 4\u20137). Anomaly detection using a convolutional winner-take-all autoencoder. Proceedings of the British Machine Vision Conference, London, UK.","DOI":"10.5244\/C.31.139"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Deng, B., Shen, C., Liu, Y., Lu, H., and Hua, X.S. (2017, January 23\u201327). Spatio-temporal autoencoder for video anomaly detection. Proceedings of the 25th ACM International Conference on Multimedia, Silicon Valley, CA, USA.","DOI":"10.1145\/3123266.3123451"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Luo, W., Liu, W., and Gao, S. (2017, January 22\u201329). A revisit of sparse coding based anomaly detection in stacked rnn framework. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.45"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, W., Luo, W., Lian, D., and Gao, S. (2018, January 18\u201322). Future frame prediction for anomaly detection\u2014A new baseline. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00684"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"17130","DOI":"10.3390\/s131217130","article-title":"Online least squares one-class support vector machines-based abnormal visual event detection","volume":"13","author":"Wang","year":"2013","journal-title":"Sensors"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"10407","DOI":"10.3390\/s120810407","article-title":"A semantic autonomous video surveillance system for dense camera networks in Smart Cities","volume":"12","author":"Lorena","year":"2012","journal-title":"Sensors"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1541880.1541882","article-title":"Anomaly detection:A survey","volume":"41","author":"Chandola","year":"2009","journal-title":"Acm Comput. Surv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cheng, K.W., Chen, Y.T., and Fang, W.H. (2015, January 7\u201312). Video anomaly detection and localization using hierarchical feature representation and Gaussian process regression. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298909"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Javan Roshtkhari, M., and Levine, M.D. (2013, January 23\u201328). Online dominant and anomalous behavior detection in videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.337"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1016\/j.cviu.2013.06.007","article-title":"An on-line, real-time learning method for detecting anomalies in videos using spatio-temporal compositions","volume":"117","author":"Roshtkhari","year":"2013","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_30","unstructured":"Brax, C., Niklasson, L., and Laxhammar, R. (2009, January 6\u20139). An ensemble approach for increased anomaly detection performance in video surveillance data. Proceedings of the 2009 12th International Conference on Information Fusion, Seattle, WA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1049\/el.2016.0440","article-title":"Video anomaly detection and localisation based on the sparsity and reconstruction error of auto-encoder","volume":"52","author":"Sabokrou","year":"2016","journal-title":"Electron. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chong, Y.S., and Tay, Y.H. (2017, January 21\u201323). Abnormal event detection in videos using spatiotemporal autoencoder. Proceedings of the International Symposium on Neural Networks, Sapporo, Japan.","DOI":"10.1007\/978-3-319-59081-3_23"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.patrec.2017.07.016","article-title":"A study of deep convolutional auto-encoders for anomaly detection in videos","volume":"105","author":"Ribeiro","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Xia, Y., Cao, X., Wen, F., Hua, G., and Sun, J. (2015, January 13\u201316). Learning discriminative reconstructions for unsupervised outlier removal. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.177"},{"key":"ref_35","first-page":"1","article-title":"Extensions of Lipschitz mappings into a Hilbert space","volume":"26","author":"Johnson","year":"1984","journal-title":"Contemp. Math."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1109\/18.661502","article-title":"The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network","volume":"44","author":"Bartlett","year":"1998","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_37","first-page":"394","article-title":"On the reciprocal of the general algebraic matrix","volume":"26","author":"Moore","year":"1920","journal-title":"Bull. Am. Math. Soc."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bjerhammar, A. (1951). Application of Calculus of Matrices to Method of Least Squares: With Special Reference to Geodetic Calculations, Elander.","DOI":"10.1007\/BF02526278"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1017\/S0305004100030401","article-title":"A generalized inverse for matrices","volume":"Volume 51","author":"Penrose","year":"1955","journal-title":"Mathematical Proceedings of the Cambridge Philosophical Society"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ionescu, R.T., Smeureanu, S., Popescu, M., and Alexe, B. (2018). Detecting abnormal events in video using Narrowed Motion Clusters. arXiv.","DOI":"10.1109\/WACV.2019.00212"},{"key":"ref_41","first-page":"139","article-title":"One-class SVMs for document classification","volume":"2","author":"Manevitz","year":"2001","journal-title":"J. Mach. Learn. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1162\/089976601750264965","article-title":"Estimating the support of a high-dimensional distribution","volume":"13","author":"Platt","year":"2001","journal-title":"Neural Comput."},{"key":"ref_43","first-page":"46","article-title":"Combining ELM with random projections","volume":"28","author":"Gastaldo","year":"2013","journal-title":"IEEE Intell. Syst."},{"key":"ref_44","first-page":"28","article-title":"Time complexity analysis of support vector machines (SVM) in LibSVM","volume":"128","author":"Abdiansah","year":"2015","journal-title":"Int. J. Comput. Appl."},{"key":"ref_45","unstructured":"Wang, S., Zhu, E., Yin, J., and Porikli, F. (2016, January 4\u20138). Anomaly detection in crowded scenes by SL-HOF descriptor and foreground classification. Proceedings of the 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Mehran, R., Oyama, A., and Shah, M. (2009, January 20\u201325). Abnormal crowd behavior detection using social force model. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach, FL, USA.","DOI":"10.1109\/CVPR.2009.5206641"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lin, H., Deng, J.D., Woodford, B.J., and Shahi, A. (2016, January 15\u201319). Online weighted clustering for real-time abnormal event detection in video surveillance. Proceedings of the 2016 ACM on Multimedia Conference, Amsterdam, The Netherlands.","DOI":"10.1145\/2964284.2967279"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4145\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:23:51Z","timestamp":1760189031000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4145"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,24]]},"references-count":47,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["s19194145"],"URL":"https:\/\/doi.org\/10.3390\/s19194145","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,24]]}}}