{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T11:49:06Z","timestamp":1778932146295,"version":"3.51.4"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"S1","license":[{"start":{"date-parts":[[2018,8,16]],"date-time":"2018-08-16T00:00:00Z","timestamp":1534377600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"The Science and Technology Research Project of Chongqing Municipal Education Committee","award":["KJ1704089"],"award-info":[{"award-number":["KJ1704089"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2019,1]]},"DOI":"10.1007\/s00521-018-3692-x","type":"journal-article","created":{"date-parts":[[2018,8,16]],"date-time":"2018-08-16T09:13:44Z","timestamp":1534410824000},"page":"175-184","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Video crowd detection and abnormal behavior model detection based on machine learning method"],"prefix":"10.1007","volume":"31","author":[{"given":"Shaoci","family":"Xie","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,8,16]]},"reference":[{"issue":"12","key":"3692_CR1","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/B978-0-12-809276-7.00013-8","volume":"1","author":"S Mohammadi","year":"2017","unstructured":"Mohammadi S, Galoogahi HK, Perina A, Murino V (2017) Physics-inspired models for detecting abnormal behaviors in crowded scenes. Group Crowd Behav Comput Vis 1(12):253\u2013272","journal-title":"Group Crowd Behav Comput Vis"},{"issue":"2","key":"3692_CR2","first-page":"1\u201312","volume":"2","author":"H Rabiee","year":"2017","unstructured":"Rabiee H, Mousavi H, Nabi M, Ravanbakhsh M (2017) Detection and localization of crowd behavior using a novel tracklet-based model. Int J Mach Learn Cybern 2(2):1\u201312","journal-title":"Int J Mach Learn Cybern"},{"issue":"5","key":"3692_CR3","first-page":"1144","volume":"9","author":"X Wang","year":"2014","unstructured":"Wang X, Gao M, He X et al (2014) An abnormal crowd behavior detection algorithm based on fluid mechanics. J Comput 9(5):1144\u20131149","journal-title":"J Comput"},{"issue":"6","key":"3692_CR4","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1016\/j.jvlc.2014.10.032","volume":"25","author":"J Cui","year":"2014","unstructured":"Cui J, Liu W, Xing W (2014) Crowd behaviors analysis and abnormal detection based on surveillance data. J Vis Lang Comput 25(6):628\u2013636","journal-title":"J Vis Lang Comput"},{"issue":"3","key":"3692_CR5","first-page":"135","volume":"8663","author":"ANS Roubtsova","year":"2013","unstructured":"Roubtsova ANS, With PHND (2013) Group localisation and unsupervised detection and classification of basic crowd behaviour events for surveillance applications. Proc SPIE Int Soc Opt Eng 8663(3):135\u2013145","journal-title":"Proc SPIE Int Soc Opt Eng"},{"issue":"5","key":"3692_CR6","first-page":"1106","volume":"36","author":"X Zhang","year":"2015","unstructured":"Zhang X, Wang M, Zuo J et al (2015) Abnormal crowd behavior detection based on motion clustering of mesoscopic group. Yi Qi Yi Biao Xue Bao\/Chin J Sci Instrum 36(5):1106\u20131114","journal-title":"Yi Qi Yi Biao Xue Bao\/Chin J Sci Instrum"},{"issue":"1","key":"3692_CR7","first-page":"29","volume":"9","author":"J Abardeig","year":"2016","unstructured":"Abardeig J, Cai J, Zhu Z (2016) Study on the method of detecting the crowd abnormality in the sensitive media image based on behavior analysis. Recent Adv Electr Electron Eng 9(1):29\u201333","journal-title":"Recent Adv Electr Electron Eng"},{"issue":"15","key":"3692_CR8","doi-asserted-by":"publisher","first-page":"9445","DOI":"10.1007\/s11042-015-3122-3","volume":"75","author":"S Zhu","year":"2016","unstructured":"Zhu S, Hu J, Shi Z (2016) Local abnormal behavior detection based on optical flow and spatio-temporal gradient. Multimed Tools Appl 75(15):9445\u20139459","journal-title":"Multimed Tools Appl"},{"issue":"5","key":"3692_CR9","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1007\/s00138-014-0615-4","volume":"25","author":"M Alvar","year":"2014","unstructured":"Alvar M, Torsello A, Sanchez-Miralles A et al (2014) Abnormal behavior detection using dominant sets. Mach Vis Appl 25(5):1351\u20131368","journal-title":"Mach Vis Appl"},{"issue":"2","key":"3692_CR10","doi-asserted-by":"publisher","first-page":"423","DOI":"10.3390\/s18020423","volume":"18","author":"X Zhang","year":"2018","unstructured":"Zhang X, Zhang Q, Hu S et al (2018) Energy level-based abnormal crowd behavior detection. Sensors 18(2):423","journal-title":"Sensors"},{"issue":"5","key":"3692_CR11","doi-asserted-by":"publisher","first-page":"051402","DOI":"10.1117\/1.JEI.26.5.051402","volume":"26","author":"YT Chan","year":"2017","unstructured":"Chan YT (2017) Extracting foreground ensemble features to detect abnormal crowd behavior in intelligent video-surveillance systems. J Electron Imaging 26(5):051402","journal-title":"J Electron Imaging"},{"issue":"3","key":"3692_CR12","first-page":"1050","volume":"3","author":"Y Balasubramanian","year":"2015","unstructured":"Balasubramanian Y (2015) Human crowd behavior analysis based on graph modeling and matching in a synoptic video. Volume 3(3):1050\u20131056","journal-title":"Volume"},{"issue":"10","key":"3692_CR13","first-page":"2598","volume":"9","author":"F Zhao","year":"2014","unstructured":"Zhao F, Li J (2014) Pedestrian motion tracking and crowd abnormal behavior detection based on intelligent video surveillance. J Netw 9(10):2598","journal-title":"J Netw"},{"issue":"1","key":"3692_CR14","first-page":"52","volume":"27","author":"HF Sang","year":"2016","unstructured":"Sang HF, Yu C, Da-HE K (2016) Crowd gathering and running behavior detection based on overall features. J Optoelectron Laser 27(1):52\u201360","journal-title":"J Optoelectron Laser"},{"issue":"19","key":"3692_CR15","doi-asserted-by":"publisher","first-page":"19741","DOI":"10.1007\/s11042-016-3439-6","volume":"76","author":"S Pan","year":"2017","unstructured":"Pan S, Sun W, Zheng Z (2017) Video segmentation algorithm based on superpixel link weight model. Multimed Tools Appl 76(19):19741\u201319760","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"3692_CR16","doi-asserted-by":"publisher","first-page":"10269","DOI":"10.1109\/ACCESS.2018.2799240","volume":"6","author":"A Shehab","year":"2018","unstructured":"Shehab A, Elhoseny M, Muhammad K, Sangaiah AK, Yang P, Huang H, Hou G (2018) Secure and robust fragile watermarking scheme for medical images. IEEE Access 6(1):10269\u201310278","journal-title":"IEEE Access"},{"issue":"5","key":"3692_CR17","doi-asserted-by":"publisher","first-page":"2611","DOI":"10.3233\/JIFS-169101","volume":"31","author":"Z Zheng","year":"2016","unstructured":"Zheng Z, Huang T, Zhang H et al (2016) Towards a resource migration method in cloud computing based on node failure rule. J Intell Fuzzy Syst 31(5):2611\u20132618","journal-title":"J Intell Fuzzy Syst"},{"issue":"1","key":"3692_CR18","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.eswa.2018.04.017","volume":"107","author":"A Tharwat","year":"2018","unstructured":"Tharwat A, Mahdi H, Elhoseny M, Hassanien AE (2018) Recognizing human activity in mobile crowdsensing environment using optimized k-NN algorithm. Expert Syst Appl 107(1):32\u201344","journal-title":"Expert Syst Appl"},{"issue":"17","key":"3692_CR19","doi-asserted-by":"publisher","first-page":"18027","DOI":"10.1007\/s11042-016-3681-y","volume":"76","author":"Z Zheng","year":"2017","unstructured":"Zheng Z, Jeong HY, Huang T et al (2017) KDE based outlier detection on distributed data streams in multimedia network. Multimed Tools Appl 76(17):18027\u201318045","journal-title":"Multimed Tools Appl"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-018-3692-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-3692-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-3692-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,15]],"date-time":"2019-08-15T23:15:44Z","timestamp":1565910944000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-018-3692-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,16]]},"references-count":19,"journal-issue":{"issue":"S1","published-print":{"date-parts":[[2019,1]]}},"alternative-id":["3692"],"URL":"https:\/\/doi.org\/10.1007\/s00521-018-3692-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,16]]},"assertion":[{"value":"17 May 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}