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The paper proposes an approach that considers individual and crowd metrics to determine anomaly. An individual\u2019s abnormal behaviour alone cannot indicate behaviour, which can be threatening toward other individuals, as this behaviour can also be triggered by positive emotions or events. To avoid individuals whose abnormal behaviour is potentially unrelated to aggression and is not environmentally dangerous, it is suggested to use emotional state of individuals. The aim of the proposed approach is to automate video surveillance systems by enabling them to automatically detect potentially dangerous situations.<\/jats:p>","DOI":"10.2478\/acss-2019-0017","type":"journal-article","created":{"date-parts":[[2020,2,21]],"date-time":"2020-02-21T04:31:24Z","timestamp":1582259484000},"page":"134-140","source":"Crossref","is-referenced-by-count":3,"title":["Affective State Based Anomaly Detection in Crowd"],"prefix":"10.2478","volume":"24","author":[{"given":"Glorija","family":"Baliniskite","sequence":"first","affiliation":[{"name":"Riga Technical University , Riga , Latvia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9912-035X","authenticated-orcid":false,"given":"Egons","family":"Lavendelis","sequence":"additional","affiliation":[{"name":"Riga Technical University , Riga , Latvia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9188-5478","authenticated-orcid":false,"given":"Mara","family":"Pudane","sequence":"additional","affiliation":[{"name":"Riga Technical University , Riga , Latvia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2020,2,20]]},"reference":[{"key":"2026042709091231974_j_acss-2019-0017_ref_001_w2aab2b8c15b1b7b1ab1ab1Aa","unstructured":"[1] IHS Markit video surveillance, \u201cTop Video Surveillance Trends for 2017,\u201d Mark. Week, pp. 14\u201318, 2016."},{"key":"2026042709091231974_j_acss-2019-0017_ref_002_w2aab2b8c15b1b7b1ab1ab2Aa","unstructured":"[2] M. Andersson, J. Rydell, and J. Ahlberg, \u201cEstimation of Crowd Behavior Using Sensor Networks and Sensor Fusion,\u201d 12th Int. Conf. Inf. Fusion, Aug. 2009."},{"key":"2026042709091231974_j_acss-2019-0017_ref_003_w2aab2b8c15b1b7b1ab1ab3Aa","unstructured":"[3] A. Kondrova, Kongnit\u012bvo procesu sist\u0113ma. R\u012bga, 2010."},{"key":"2026042709091231974_j_acss-2019-0017_ref_004_w2aab2b8c15b1b7b1ab1ab4Aa","unstructured":"[4] H. U. Keval, \u201cEffective, Design, Configuration, and Use of Digital CCTV,\u201d Doctoral thesis, University College London, 2009."},{"key":"2026042709091231974_j_acss-2019-0017_ref_005_w2aab2b8c15b1b7b1ab1ab5Aa","doi-asserted-by":"crossref","unstructured":"[5] M. W. Baig, E. I. Barakova, L. Marcenaro, M. Rauterberg, and C. S. Regazzoni, \u201cCrowd Emotion Detection Using Dynamic Probabilistic Models,\u201d in From Animals to Animals 13, A. P. del Pobil, E. Martinez-Martin, J. Hallam, E. Cervera, A. Morales, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014, pp. 328\u2013337. https:\/\/doi.org\/10.1007\/978-3-319-08864-8_3210.1007\/978-3-319-08864-8_32","DOI":"10.1007\/978-3-319-08864-8_32"},{"key":"2026042709091231974_j_acss-2019-0017_ref_006_w2aab2b8c15b1b7b1ab1ab6Aa","unstructured":"[6] W. Little, Introduction to Sociology \u2013 1st Canadian Edition Edition. pp. 141\u2013168, 2014."},{"key":"2026042709091231974_j_acss-2019-0017_ref_007_w2aab2b8c15b1b7b1ab1ab7Aa","doi-asserted-by":"crossref","unstructured":"[7] Y. Koizumi, S. Saito, H. Uematsu, Y. Kawachi, and N. Harada, \u201cUnsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma,\u201d IEEE\/ACM Transactions on Audio, Speech, and Language Processing, vol. 27, no. 1, pp. 212\u2013224, Jan. 2019. https:\/\/doi.org\/10.1109\/taslp.2018.287725810.1109\/TASLP.2018.2877258","DOI":"10.1109\/TASLP.2018.2877258"},{"key":"2026042709091231974_j_acss-2019-0017_ref_008_w2aab2b8c15b1b7b1ab1ab8Aa","doi-asserted-by":"crossref","unstructured":"[8] D. Chakrabarty and M. Elhilali, \u201cAbnormal Sound Event Detection Using Temporal Trajectories Mixtures,\u201d 2016 IEEE Int. Conf. Acoust. Speech Signal Process., pp. 216\u2013220, 2016. https:\/\/doi.org\/10.1109\/ICASSP.2016.747166810.1109\/ICASSP.2016.7471668","DOI":"10.1109\/ICASSP.2016.7471668"},{"key":"2026042709091231974_j_acss-2019-0017_ref_009_w2aab2b8c15b1b7b1ab1ab9Aa","unstructured":"[9] A. G. Abuarafah, M. O. Khozium, and E. Abdrabou, \u201cReal-Time Crowd Monitoring Using Infrared Thermal Video Sequences,\u201d J. Am. Sci., vol. 8, no. 3, 2012."},{"key":"2026042709091231974_j_acss-2019-0017_ref_010_w2aab2b8c15b1b7b1ab1ac10Aa","doi-asserted-by":"crossref","unstructured":"[10] V. Chandola, A. Banerjee, and V. Kumar, \u201cAnomaly Detection: A Survey,\u201d ACM Computing Surveys, vol. 41, no. 3, pp. 1\u201358, Jul. 2009. https:\/\/doi.org\/10.1145\/1541880.154188210.1145\/1541880.1541882","DOI":"10.1145\/1541880.1541882"},{"key":"2026042709091231974_j_acss-2019-0017_ref_011_w2aab2b8c15b1b7b1ab1ac11Aa","unstructured":"[11] H. Parvin, H. Alizadeh, and B. Minati, \u201cA Modification on k-Nearest Neighbor Classifier,\u201d Global Journal of Computer Science and Technology, vol. 10, no. 14, pp. 37\u201341, 2010."},{"key":"2026042709091231974_j_acss-2019-0017_ref_012_w2aab2b8c15b1b7b1ab1ac12Aa","unstructured":"[12] R. Chalapathy and S. Chawla, \u201cDeep Learning for Anomaly Detection: A Survey,\u201d pp. 1\u201350, 2019.10.1145\/3394486.3406704"},{"key":"2026042709091231974_j_acss-2019-0017_ref_013_w2aab2b8c15b1b7b1ab1ac13Aa","doi-asserted-by":"crossref","unstructured":"[13] Y. Chen, J. Qian, and V. Saligrama, \u201cA New One-Class SVM for Anomaly Detection,\u201d in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3567\u20133571, 2013. https:\/\/doi.org\/10.1109\/ICASSP.2013.663832210.1109\/ICASSP.2013.6638322","DOI":"10.1109\/ICASSP.2013.6638322"},{"key":"2026042709091231974_j_acss-2019-0017_ref_014_w2aab2b8c15b1b7b1ab1ac14Aa","doi-asserted-by":"crossref","unstructured":"[14] F. T. Liu and K. M. Ting, \u201cIsolation Forest,\u201d 2008 Eighth IEEE Int. Conf. Data Min., pp. 413\u2013422, Dec. 2008. https:\/\/doi.org\/10.1109\/ICDM.2008.1710.1109\/ICDM.2008.17","DOI":"10.1109\/ICDM.2008.17"},{"key":"2026042709091231974_j_acss-2019-0017_ref_015_w2aab2b8c15b1b7b1ab1ac15Aa","doi-asserted-by":"crossref","unstructured":"[15] C. Chen, Y. Shao, and X. Bi, \u201cDetection of Anomalous Crowd Behavior Based on the Acceleration Feature,\u201d IEEE Sens. J., vol. 15, no. 12, pp. 7252\u20137261, 2015. https:\/\/doi.org\/10.1109\/JSEN.2015.247296010.1109\/JSEN.2015.2472960","DOI":"10.1109\/JSEN.2015.2472960"},{"key":"2026042709091231974_j_acss-2019-0017_ref_016_w2aab2b8c15b1b7b1ab1ac16Aa","doi-asserted-by":"crossref","unstructured":"[16] J. Z. C. Lai and T. J. Huang, \u201cFast Global k-Means Clustering Using Cluster Membership and Inequality,\u201d Pattern Recognit., vol. 43, no. 5, pp. 1954\u20131963, 2010. https:\/\/doi.org\/10.1016\/j.patcog.2009.11.02110.1016\/j.patcog.2009.11.021","DOI":"10.1016\/j.patcog.2009.11.021"},{"key":"2026042709091231974_j_acss-2019-0017_ref_017_w2aab2b8c15b1b7b1ab1ac17Aa","doi-asserted-by":"crossref","unstructured":"[17] M. M. Breunig, H. Kriegel, R. T. Ng, and J. Sander, \u201cLOF: Identifying Density-Based Local Outliers,\u201d in Proceedings of the 2000 ACM SIGMOD international conference on Management of data (SIGMOD \u201900), pp. 1\u201312, 2000. https:\/\/doi.org\/10.1145\/342009.33538810.1145\/342009.335388","DOI":"10.1145\/342009.335388"},{"key":"2026042709091231974_j_acss-2019-0017_ref_018_w2aab2b8c15b1b7b1ab1ac18Aa","doi-asserted-by":"crossref","unstructured":"[18] M. Markou and S. Singh, \u201cNovelty Detection: A Review \u2013 Part 1: Statistical Approaches,\u201d Signal Processing, vol. 83, no. 12, pp. 2481\u20132497, Dec. 2003. https:\/\/doi.org\/10.1016\/j.sigpro.2003.07.01810.1016\/j.sigpro.2003.07.018","DOI":"10.1016\/j.sigpro.2003.07.018"},{"key":"2026042709091231974_j_acss-2019-0017_ref_019_w2aab2b8c15b1b7b1ab1ac19Aa","doi-asserted-by":"crossref","unstructured":"[19] W.-L. Hsu, Y.-C. Wang, and C.-L. Lin, \u201cAbnormal Crowd Event Detection Based on Outlier in Time,\u201d in 2014 Int. Conf. Mach. Learn. Cybern., vol. 1, pp. 359\u2013363, 2014. https:\/\/doi.org\/10.1109\/icmlc.2014.700914210.1109\/ICMLC.2014.7009142","DOI":"10.1109\/ICMLC.2014.7009142"},{"key":"2026042709091231974_j_acss-2019-0017_ref_020_w2aab2b8c15b1b7b1ab1ac20Aa","doi-asserted-by":"crossref","unstructured":"[20] T. Hassner, Y. Itcher, and O. Kliper-Gross, \u201cViolent Flows: Real-Time Detection of Violent Crowd Behavior *,\u201d 2012 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., pp. 1\u20136, 2012. https:\/\/doi.org\/10.1109\/CVPRW.2012.623934810.1109\/CVPRW.2012.6239348","DOI":"10.1109\/CVPRW.2012.6239348"},{"key":"2026042709091231974_j_acss-2019-0017_ref_021_w2aab2b8c15b1b7b1ab1ac21Aa","unstructured":"[21] C.-L. L. Wei-Lieh Hsu, Yu-Cheng Wang, \u201cSpatio-Temporal Anomaly Detection in Crowd Movement Using SIFT,\u201d in Proc. 2014 Int. Conf. Mach. Learn. Cybern., 2014."},{"key":"2026042709091231974_j_acss-2019-0017_ref_022_w2aab2b8c15b1b7b1ab1ac22Aa","doi-asserted-by":"crossref","unstructured":"[22] Y.-T. Matsuda, T. Fujimura, K. Katahira, M. Okada, K. Ueno, K. Cheng, and K. Okanoya, \u201cThe Implicit Processing of Categorical and Dimensional Strategies: An fMRI Study of Facial Emotion Perception,\u201d Front. Hum. Neurosci., vol. 7, no. September, 2013. https:\/\/doi.org\/10.3389\/fnhum.2013.0055110.3389\/fnhum.2013.00551378383924133426","DOI":"10.3389\/fnhum.2013.00551"},{"key":"2026042709091231974_j_acss-2019-0017_ref_023_w2aab2b8c15b1b7b1ab1ac23Aa","unstructured":"[23] R. Fan, K. Xu, and J. Zhao, \u201cHigher Contagion and Weaker Ties Mean Anger Spreads Faster Than Joy in Social Media,\u201d pp. 1\u201323, 2016."},{"key":"2026042709091231974_j_acss-2019-0017_ref_024_w2aab2b8c15b1b7b1ab1ac24Aa","doi-asserted-by":"crossref","unstructured":"[24] L. Coviello, J. H. Fowler, and M. Franceschetti, \u201cWords on the Web: Noninvasive Detection of Emotional Contagion in Online Social Networks,\u201d in Proc. IEEE, vol. 102, no. 12, pp. 1911\u20131921, 2014. https:\/\/doi.org\/10.1109\/JPROC.2014.236605210.1109\/JPROC.2014.2366052","DOI":"10.1109\/JPROC.2014.2366052"},{"key":"2026042709091231974_j_acss-2019-0017_ref_025_w2aab2b8c15b1b7b1ab1ac25Aa","doi-asserted-by":"crossref","unstructured":"[25] D. Mehta, M. F. H. Siddiqui, and A. Y. Javaid, \u201cFacial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality,\u201d Sensors, vol. 18, no. 2, pp. 416, 2018. https:\/\/doi.org\/10.3390\/s1802041610.3390\/s18020416585613229389845","DOI":"10.3390\/s18020416"},{"key":"2026042709091231974_j_acss-2019-0017_ref_026_w2aab2b8c15b1b7b1ab1ac26Aa","unstructured":"[26] S. Petrovica and H. K. Ekene, \u201cEmotion Recognition for Intelligent Tutoring,\u201d CEUR Workshop Proc., vol. 1684, 2016."},{"key":"2026042709091231974_j_acss-2019-0017_ref_027_w2aab2b8c15b1b7b1ab1ac27Aa","unstructured":"[27] Z. S. Hippe, J. L. Kulikowski, T. Mroczek, and J. Wtorek, \u201cEmotion Recognition and Its Applications,\u201d Adv. Intell. Syst. Comput., vol. 300, no. July, 2014."},{"key":"2026042709091231974_j_acss-2019-0017_ref_028_w2aab2b8c15b1b7b1ab1ac28Aa","unstructured":"[28] K. Glanz, \u201cSocial and Behavioral Theories 1. Learning Objectives,\u201d Off. Behav. Soc. Sci. Res., 2005."},{"key":"2026042709091231974_j_acss-2019-0017_ref_029_w2aab2b8c15b1b7b1ab1ac29Aa","unstructured":"[29] G. Bradski, \u201cThe OpenCV Library,\u201d Dr. Dobb\u2019s J. Softw. Tools, vol. 120, pp. 122\u2013125, 2000."},{"key":"2026042709091231974_j_acss-2019-0017_ref_030_w2aab2b8c15b1b7b1ab1ac30Aa","unstructured":"[30] J. Redmon and A. Farhadi, YOLO v.3, 2018."},{"key":"2026042709091231974_j_acss-2019-0017_ref_031_w2aab2b8c15b1b7b1ab1ac31Aa","unstructured":"[31] J. Redmon, \u201cDarknet: Open Source Neural Networks in C,\u201d Pjreddie. [Online]. Available: http:\/\/pjreddie.com\/darknet\/"},{"key":"2026042709091231974_j_acss-2019-0017_ref_032_w2aab2b8c15b1b7b1ab1ac32Aa","doi-asserted-by":"crossref","unstructured":"[32] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll\u00e1r, and C. L. Zitnick, \u201cMicrosoft COCO: Common Objects in Context,\u201d Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8693 LNCS, no. PART 5, pp. 740\u2013755, 2014. https:\/\/doi.org\/10.1007\/978-3-319-10602-1_4810.1007\/978-3-319-10602-1_48","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"2026042709091231974_j_acss-2019-0017_ref_033_w2aab2b8c15b1b7b1ab1ac33Aa","doi-asserted-by":"crossref","unstructured":"[33] A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, \u201cSimple Online and Realtime Tracking,\u201d in 2016 IEEE International Conference on Image Processing (ICIP), 2016, pp. 3464\u20133468. https:\/\/doi.org\/10.1109\/ICIP.2016.753300310.1109\/ICIP.2016.7533003","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"2026042709091231974_j_acss-2019-0017_ref_034_w2aab2b8c15b1b7b1ab1ac34Aa","doi-asserted-by":"crossref","unstructured":"[34] T. Simon, H. Joo, I. Matthews, and Y. Sheikh, \u201cHand Keypoint Detection in Single Images Using Multiview Bootstrapping,\u201d in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4645\u20134653, 2017. https:\/\/doi.org\/10.1109\/CVPR.2017.49410.1109\/CVPR.2017.494","DOI":"10.1109\/CVPR.2017.494"},{"key":"2026042709091231974_j_acss-2019-0017_ref_035_w2aab2b8c15b1b7b1ab1ac35Aa","doi-asserted-by":"crossref","unstructured":"[35] Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh, \u201cRealtime Multi-Person 2D Pose Estimation Using Part Affinity Fields,\u201d in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1302\u20131310, 2017. https:\/\/doi.org\/10.1109\/CVPR.2017.14310.1109\/CVPR.2017.143","DOI":"10.1109\/CVPR.2017.143"},{"key":"2026042709091231974_j_acss-2019-0017_ref_036_w2aab2b8c15b1b7b1ab1ac36Aa","doi-asserted-by":"crossref","unstructured":"[36] S.-E. Wei, V. Ramakrishna, T. Kanade, and Y. Sheikh, \u201cConvolutional Pose Machines,\u201d in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 4724\u20134732, 2016. https:\/\/doi.org\/10.1109\/CVPR.2016.51110.1109\/CVPR.2016.511","DOI":"10.1109\/CVPR.2016.511"}],"container-title":["Applied Computer Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/content.sciendo.com\/view\/journals\/acss\/24\/2\/article-p134.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/reference-global.com\/pdf\/10.2478\/acss-2019-0017","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T11:24:16Z","timestamp":1777289056000},"score":1,"resource":{"primary":{"URL":"https:\/\/reference-global.com\/article\/10.2478\/acss-2019-0017"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,1]]},"references-count":36,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,2,20]]},"published-print":{"date-parts":[[2019,12,1]]}},"alternative-id":["10.2478\/acss-2019-0017"],"URL":"https:\/\/doi.org\/10.2478\/acss-2019-0017","relation":{},"ISSN":["2255-8691"],"issn-type":[{"value":"2255-8691","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,1]]}}}