{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,10]],"date-time":"2025-05-10T07:07:23Z","timestamp":1746860843905,"version":"3.37.3"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,8,27]],"date-time":"2019-08-27T00:00:00Z","timestamp":1566864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,8,27]],"date-time":"2019-08-27T00:00:00Z","timestamp":1566864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["2018R1D1A3B07041729"],"award-info":[{"award-number":["2018R1D1A3B07041729"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002560","name":"Soonchunhyang University","doi-asserted-by":"crossref","award":["NA"],"award-info":[{"award-number":["NA"]}],"id":[{"id":"10.13039\/501100002560","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Image Video Proc."],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1186\/s13640-019-0478-8","type":"journal-article","created":{"date-parts":[[2019,8,27]],"date-time":"2019-08-27T13:03:20Z","timestamp":1566911000000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An online graph-based anomalous change detection strategy for unsupervised video surveillance"],"prefix":"10.1186","volume":"2019","author":[{"given":"Jongwon","family":"KIM","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5162-1745","authenticated-orcid":false,"given":"Jeongho","family":"CHO","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,8,27]]},"reference":[{"key":"478_CR1","doi-asserted-by":"crossref","unstructured":"Wang, M. L., Huang, C. C., & Lin, H. Y. (2006, June). An intelligent surveillance system based on an omnidirectional vision sensor. In 2006 IEEE Conference on Cybernetics and Intelligent Systems (pp. 1-6). IEEE.","DOI":"10.1109\/ICCIS.2006.252312"},{"issue":"2","key":"478_CR2","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1049\/ip-vis:20041147","volume":"152","author":"M Valera","year":"2005","unstructured":"M. Valera, S.A. Velastin, Intelligent distributed surveillance systems: a review. IEE Proceedings-Vision, Image and Signal Processing 152(2), 192\u2013204 (2005)","journal-title":"IEE Proceedings-Vision, Image and Signal Processing"},{"key":"478_CR3","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1016\/j.eswa.2016.08.010","volume":"64","author":"F Ortega-Zamorano","year":"2016","unstructured":"F. Ortega-Zamorano, M.A. Molina-Cabello, E. L\u00f3pez-Rubio, E.J. Palomo, Smart motion detection sensor based on video processing using self-organizing maps. Expert Systems with Applications 64, 476\u2013489 (2016)","journal-title":"Expert Systems with Applications"},{"issue":"3","key":"478_CR4","first-page":"393","volume":"20","author":"B Sun","year":"2003","unstructured":"B. Sun, S. Velastin, Fusing visual and audio information in a distributed intelligent surveillance system for public transport systems. Acta Autom. Sin 20(3), 393\u2013407 (2003)","journal-title":"Acta Autom. Sin"},{"key":"478_CR5","unstructured":"Tao, J., Turjo, M., Wong, M. F., Wang, M., & Tan, Y. P. (2005, December). Fall incidents detection for intelligent video surveillance. In 2005 5th International Conference on Information Communications & Signal Processing (pp. 1590-1594). IEEE."},{"issue":"6","key":"478_CR6","first-page":"898","volume":"10","author":"A Singh","year":"1988","unstructured":"A. Singh, Digital change detection techniques using remotely sensed data. International Journal of Remote Sensing. 10(6), 898\u20131003 (1988)","journal-title":"International Journal of Remote Sensing."},{"key":"478_CR7","doi-asserted-by":"crossref","unstructured":"Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A. K., & Davis, L. S. (2016). Learning temporal regularity in video sequences. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 733-742).","DOI":"10.1109\/CVPR.2016.86"},{"key":"478_CR8","unstructured":"Xu, D., Ricci, E., Yan, Y., Song, J., & Sebe, N. (2015). Learning deep representations of appearance and motion for anomalous event detection. arXiv preprint arXiv:1510.01553."},{"key":"478_CR9","doi-asserted-by":"crossref","unstructured":"Malisiewicz, T., Gupta, A., & Efros, A. A. (2011, November). Ensemble of exemplar-SVMs for object detection and beyond. In Iccv (Vol. 1, No. 2, p. 6).","DOI":"10.1109\/ICCV.2011.6126229"},{"key":"478_CR10","doi-asserted-by":"crossref","unstructured":"Cui, X., Liu, Q., Gao, M., & Metaxas, D. N. (2011, June). Abnormal detection using interaction energy potentials. In CVPR 2011 (pp. 3161-3167). IEEE.","DOI":"10.1109\/CVPR.2011.5995558"},{"key":"478_CR11","first-page":"3","volume-title":"Lecture Notes in Computer Science","author":"Marco Leo","year":"2019","unstructured":"Leo M., Furnari A., Medioni G.G., Trivedi M., Farinella G.M. (2019) Deep learning for assistive computer vision. In: Leal-Taix\u00e9 L., Roth S. (eds) Computer Vision \u2013 ECCV 2018 Workshops. ECCV 2018. Lecture Notes in Computer Science, vol 11134. Springer, Cham"},{"issue":"6","key":"478_CR12","doi-asserted-by":"publisher","first-page":"2907","DOI":"10.1073\/pnas.96.6.2907","volume":"96","author":"P Tamayo","year":"1999","unstructured":"P. Tamayo, D. Slonim, J. Mesirov, Q. Zhu, S. Kitareewan, E. Dmitrovsky, et al., Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proceedings of the National Academy of Sciences 96(6), 2907\u20132912 (1999)","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"4","key":"478_CR13","doi-asserted-by":"publisher","first-page":"1672","DOI":"10.1109\/TPWRS.2006.881133","volume":"21","author":"SV Verd\u00fa","year":"2006","unstructured":"S.V. Verd\u00fa, M.O. Garcia, C. Senabre, A.G. Marin, F.G. Franco, Classification, filtering, and identification of electrical customer load patterns through the use of self-organizing maps. IEEE Transactions on Power Systems 21(4), 1672\u20131682 (2006)","journal-title":"IEEE Transactions on Power Systems"},{"issue":"7","key":"478_CR14","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1109\/34.598236","volume":"19","author":"CR Wren","year":"1997","unstructured":"C.R. Wren, A. Azarbayejani, T. Darrell, A.P. Pentland, Pfinder: real-time tracking of the human body. IEEE Transactions on pattern analysis and machine intelligence 19(7), 780\u2013785 (1997)","journal-title":"IEEE Transactions on pattern analysis and machine intelligence"},{"key":"478_CR15","doi-asserted-by":"crossref","unstructured":"Stauffer, C., & Grimson, W. E. L. (1999). Adaptive background mixture models for real-time tracking. In Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) (Vol. 2, pp. 246-252). IEEE.","DOI":"10.1109\/CVPR.1999.784637"},{"key":"478_CR16","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1016\/j.patcog.2017.09.040","volume":"76","author":"M Babaee","year":"2018","unstructured":"M. Babaee, D.T. Dinh, G. Rigoll, A deep convolutional neural network for video sequence background subtraction. Pattern Recognition 76, 635\u2013649 (2018)","journal-title":"Pattern Recognition"},{"key":"478_CR17","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.patrec.2018.08.002","volume":"112","author":"LA Lim","year":"2018","unstructured":"L.A. Lim, H.Y. Keles, Foreground segmentation using convolutional neural networks for multiscale feature encoding. Pattern Recognition Letters 112, 256\u2013262 (2018)","journal-title":"Pattern Recognition Letters"},{"issue":"8","key":"478_CR18","doi-asserted-by":"publisher","first-page":"723819","DOI":"10.1155\/2014\/723819","volume":"10","author":"D Vallejo","year":"2014","unstructured":"D. Vallejo, F.J. Villanueva, J.A. Albusac, C. Glez-Morcillo, J.J. Castro-Schez, Intelligent surveillance for understanding events in urban traffic environments. International Journal of Distributed Sensor Networks 10(8), 723819 (2014)","journal-title":"International Journal of Distributed Sensor Networks"},{"issue":"1","key":"478_CR19","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1007\/s00521-016-2363-z","volume":"28","author":"M Al-Nawashi","year":"2017","unstructured":"M. Al-Nawashi, O.M. Al-Hazaimeh, M. Saraee, A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments. Neural Computing and Applications 28(1), 565\u2013572 (2017)","journal-title":"Neural Computing and Applications"},{"issue":"5","key":"478_CR20","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1109\/34.291452","volume":"16","author":"D Murray","year":"1994","unstructured":"D. Murray, A. Basu, Motion tracking with an active camera. IEEE transactions on pattern analysis and machine intelligence 16(5), 449\u2013459 (1994)","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"issue":"10","key":"478_CR21","doi-asserted-by":"publisher","first-page":"1436","DOI":"10.1016\/j.cviu.2013.06.007","volume":"117","author":"MJ Roshtkhari","year":"2013","unstructured":"M.J. Roshtkhari, M.D. Levine, An on-line, real-time learning method for detecting anomalies in videos using spatio-temporal compositions. Computer vision and image understanding 117(10), 1436\u20131452 (2013)","journal-title":"Computer vision and image understanding"},{"key":"478_CR22","doi-asserted-by":"crossref","unstructured":"Sultani, W., Chen, C., & Shah, M. (2018). Real-world anomaly detection in surveillance videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 6479-6488).","DOI":"10.1109\/CVPR.2018.00678"},{"key":"478_CR23","doi-asserted-by":"crossref","unstructured":"Gordo, A., Almaz\u00e1n, J., Revaud, J., & Larlus, D. (2016, October). Deep image retrieval: Learning global representations for image search. In European conference on computer vision (pp. 241-257). Springer, Cham.","DOI":"10.1007\/978-3-319-46466-4_15"},{"issue":"4","key":"478_CR24","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.5194\/hess-11-1309-2007","volume":"11","author":"L. Peeters","year":"2007","unstructured":"L. Peeters, F. Bacao, V. Lobo, A. Dassargues, Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen's Self-Organizing Map. Hydrology and Earth System Sciences 11, 1309-1321 (2007)","journal-title":"Hydrology and Earth System Sciences"},{"issue":"4","key":"478_CR25","doi-asserted-by":"publisher","first-page":"488","DOI":"10.15388\/NA.16.4.14091","volume":"16","author":"Pavel Stefanovi\u010d","year":"2011","unstructured":"P. Stefanovic, O. Kurasova, Visual analysis of self-organizing maps. Nonlinear Analysis: Modeling and Control 16(4), 488-504 (2011)","journal-title":"Nonlinear Analysis: Modelling and Control"},{"key":"478_CR26","doi-asserted-by":"crossref","unstructured":"Wang, Y., Jodoin, P. M., Porikli, F., Konrad, J., Benezeth, Y., & Ishwar, P. (2014). CDnet 2014: an expanded change detection benchmark dataset. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 387-394).","DOI":"10.1109\/CVPRW.2014.126"},{"issue":"6","key":"478_CR27","doi-asserted-by":"publisher","first-page":"1460","DOI":"10.1016\/j.scient.2011.08.025","volume":"18","author":"ML Shahreza","year":"2011","unstructured":"M.L. Shahreza, D. Moazzami, B. Moshiri, M.R. Delavar, Anomaly detection using a self-organizing map and particle swarm optimization. Scientia Iranica 18(6), 1460\u20131468 (2011)","journal-title":"Scientia Iranica"},{"key":"478_CR28","doi-asserted-by":"publisher","first-page":"35915","DOI":"10.1109\/ACCESS.2018.2849110","volume":"6","author":"R Xiao","year":"2018","unstructured":"R. Xiao, R. Cui, M. Lin, L. Chen, Y. Ni, X. Lin, SOMDNCD: image change detection based on self-organizing maps and deep neural networks. IEEE Access 6, 35915\u201335925 (2018)","journal-title":"IEEE Access"},{"key":"478_CR29","doi-asserted-by":"crossref","unstructured":"Tian, J., Azarian, M. H., & Pecht, M. (2014, July). Anomaly detection using self-organizing maps-based k-nearest neighbor algorithm. In Proceedings of the European Conference of the Prognostics and Health Management Society.","DOI":"10.36001\/phme.2014.v2i1.1554"},{"key":"478_CR30","unstructured":"Olson, D. L., & Delen, D. (2008). Advanced data mining techniques. Springer Science & Business Media."},{"key":"478_CR31","unstructured":"Cohen, I., & Medioni, G. (1999, June). Detecting and tracking moving objects for video surveillance. In cvpr (p. 2319). IEEE."},{"issue":"7","key":"478_CR32","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1016\/j.patrec.2005.11.005","volume":"27","author":"Z Zivkovic","year":"2006","unstructured":"Z. Zivkovic, F. Van Der Heijden, Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern recognition letters 27(7), 773\u2013780 (2006)","journal-title":"Pattern recognition letters"},{"issue":"2","key":"478_CR33","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/S0014-5793(99)00524-4","volume":"451","author":"P T\u00f6r\u00f6nen","year":"1999","unstructured":"P. T\u00f6r\u00f6nen, M. Kolehmainen, G. Wong, E. Castr\u00e9n, Analysis of gene expression data using self-organizing maps. FEBS letters 451(2), 142\u2013146 (1999)","journal-title":"FEBS letters"},{"issue":"6","key":"478_CR34","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1109\/TNN.2010.2046497","volume":"21","author":"R Dlugosz","year":"2010","unstructured":"R. Dlugosz, T. Talaska, W. Pedrycz, R. Wojtyna, Realization of the conscience mechanism in CMOS implementation of winner-takes-all self-organizing neural networks. IEEE Transactions on Neural Networks 21(6), 961\u2013971 (2010)","journal-title":"IEEE Transactions on Neural Networks"},{"key":"478_CR35","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.fishres.2016.04.005","volume":"181","author":"T Russo","year":"2016","unstructured":"T. Russo, P. Carpentieri, F. Fiorentino, E. Arneri, M. Scardi, A. Cioffi, S. Cataudella, Modeling landings profiles of fishing vessels: An application of Self-Organizing Maps to VMS and logbook data. Fisheries Research 181, 34\u201347 (2016)","journal-title":"Fisheries Research"},{"key":"478_CR36","volume-title":"Self-organizing neural networks for visualisation and classification. In Information and classification (pp. 307-313)","author":"A Ultsch","year":"1993","unstructured":"A. Ultsch, Self-organizing neural networks for visualisation and classification. In Information and classification (pp. 307-313) (Springer, Berlin, Heidelberg, 1993)"},{"key":"478_CR37","unstructured":"A. Ultsch, Kohonen\u2019s self-organizing feature maps for exploratory data analysis. Proc. INNC90, 305\u2013308 (1990)"},{"issue":"6s","key":"478_CR38","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1016\/S0305-0548(97)00085-3","volume":"25","author":"G Yu","year":"1998","unstructured":"G. Yu, J. Yang, On the robust shortest path problem. Computers & Operations Research 25(6s), 457\u2013468 (1998)","journal-title":"Computers & Operations Research"},{"key":"478_CR39","doi-asserted-by":"crossref","unstructured":"Broumi, S., Bakal, A., Talea, M., Smarandache, F., & Vladareanu, L. (2016, November). Applying Dijkstra algorithm for solving neutrosophic shortest path problem. In 2016 International Conference on Advanced Mechatronic Systems (ICAMechS) (pp. 412-416). IEEE.","DOI":"10.1109\/ICAMechS.2016.7813483"},{"key":"478_CR40","doi-asserted-by":"crossref","unstructured":"Bouwmans, T., Porikli, F., H\u00f6ferlin, B., & Vacavant, A. (Eds.). (2014). Background modeling and foreground detection for video surveillance. CRC press.","DOI":"10.1201\/b17223"},{"key":"478_CR41","doi-asserted-by":"crossref","unstructured":"Vijverberg, J. A., Loomans, M. J., Koeleman, C. J., & de With, P. H. (2009, September). Global illumination compensation for background subtraction using Gaussian-based background difference modeling. In 2009.","DOI":"10.1109\/AVSS.2009.101"},{"key":"478_CR42","doi-asserted-by":"crossref","unstructured":"Zivkovic, Z. (2004, August). Improved adaptive Gaussian mixture model for background subtraction. In ICPR (2) (pp. 28-31).","DOI":"10.1109\/ICPR.2004.1333992"},{"key":"478_CR43","doi-asserted-by":"crossref","unstructured":"Maddalena, L., & Petrosino, A. (2012, June). The SOBS algorithm: what are the limits?. In 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp. 21-26). IEEE.","DOI":"10.1109\/CVPRW.2012.6238922"}],"container-title":["EURASIP Journal on Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13640-019-0478-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13640-019-0478-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13640-019-0478-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T16:25:23Z","timestamp":1721665523000},"score":1,"resource":{"primary":{"URL":"https:\/\/jivp-eurasipjournals.springeropen.com\/articles\/10.1186\/s13640-019-0478-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,27]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["478"],"URL":"https:\/\/doi.org\/10.1186\/s13640-019-0478-8","relation":{},"ISSN":["1687-5281"],"issn-type":[{"type":"electronic","value":"1687-5281"}],"subject":[],"published":{"date-parts":[[2019,8,27]]},"assertion":[{"value":"7 March 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 August 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"76"}}