{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T17:26:45Z","timestamp":1770226005636,"version":"3.49.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T00:00:00Z","timestamp":1663545600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T00:00:00Z","timestamp":1663545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s11042-022-13827-7","type":"journal-article","created":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T06:02:47Z","timestamp":1663567367000},"page":"12493-12512","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Learning deep latent space for unsupervised violence detection"],"prefix":"10.1007","volume":"82","author":[{"given":"Tahereh Zarrat","family":"Ehsan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9846-314X","authenticated-orcid":false,"given":"Manoochehr","family":"Nahvi","sequence":"additional","affiliation":[]},{"given":"Seyed Mehdi","family":"Mohtavipour","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,19]]},"reference":[{"key":"13827_CR1","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1002\/wics.101","volume":"2","author":"H Abdi","year":"2010","unstructured":"Abdi H, Williams LJ (2010) Principal component analysis. Wiley interdisciplinary reviews: computational statistics 2:433\u2013459","journal-title":"Wiley interdisciplinary reviews: computational statistics"},{"key":"13827_CR2","doi-asserted-by":"publisher","first-page":"8213","DOI":"10.1007\/s11042-019-08469-1","volume":"79","author":"R Anusha","year":"2020","unstructured":"Anusha R, Jaidhar CD (2020) Human gait recognition based on histogram of oriented gradients and Haralick texture descriptor. Multimed Tools Appl 79:8213\u20138234","journal-title":"Multimed Tools Appl"},{"key":"13827_CR3","unstructured":"Baldi P (2012) Autoencoders, unsupervised learning, and deep architectures. In: 2012 Proceedings of ICML workshop on unsupervised and transfer learning, Bellevue, Washington, USA, 37\u201349"},{"key":"13827_CR4","doi-asserted-by":"publisher","unstructured":"Bermejo Nievas E, Deniz Suarez O, Bueno Garc\u00eda G, Sukthankar R (2011). Violence detection in video using computer vision techniques. In: 2011 14th international conference on computer analysis of images and patterns, Seville, Spain, 332\u2013339. https:\/\/doi.org\/10.1007\/978-3-642-23678-5_39","DOI":"10.1007\/978-3-642-23678-5_39"},{"key":"13827_CR5","doi-asserted-by":"crossref","unstructured":"Blumstein A, Wallman J (2020). The recent rise and fall of American violence. In: Vogel E (ed) Crime, Inequality and the State, 1st edn. Routledge, 103\u2013124","DOI":"10.4324\/9781003060581-8"},{"key":"13827_CR6","doi-asserted-by":"publisher","first-page":"3835","DOI":"10.1109\/TIP.2020.2965299","volume":"29","author":"C Dhiman","year":"2020","unstructured":"Dhiman C, Vishwakarma DK (2020) View-invariant deep architecture for human action recognition using two-stream motion and shape temporal dynamics. IEEE Trans Image Process 29:3835\u20133844","journal-title":"IEEE Trans Image Process"},{"key":"13827_CR7","doi-asserted-by":"crossref","unstructured":"Ehsan TZ, Mohtavipour SM (2020) Vi-net: a deep violent flow network for violence detection in video sequences. In: 11th International Conference on Information and Knowledge Technology, 88\u201392","DOI":"10.1109\/IKT51791.2020.9345617"},{"key":"13827_CR8","doi-asserted-by":"crossref","unstructured":"Ehsan TZ, Nahvi M (2018). Violence detection in indoor surveillance cameras using motion trajectory and differential histogram of optical flow. In: 8th International Conference on Computer and Knowledge Engineering, 153\u2013158","DOI":"10.1109\/ICCKE.2018.8566460"},{"key":"13827_CR9","doi-asserted-by":"crossref","unstructured":"Ehsan TZ, Nahvi M, Mohtavipour SM (2022). DABA-net: deep acceleration-based AutoEncoder network for violence detection in surveillance cameras, In: Proceedings of IEEE International Conference on Machine Vision and Image Processing (MVIP), 1\u20136","DOI":"10.1109\/MVIP53647.2022.9738791"},{"key":"13827_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cviu.2015.02.008","volume":"134","author":"D Fortun","year":"2015","unstructured":"Fortun D, Bouthemy P, Kervrann C (2015) Optical flow modeling and computation: a survey. Comput Vis Image Underst 134:1\u201321","journal-title":"Comput Vis Image Underst"},{"key":"13827_CR11","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.imavis.2016.01.006","volume":"48","author":"Y Gao","year":"2016","unstructured":"Gao Y, Liu H, Sun X, Wang C, Liu Y (2016) Violence detection using oriented violent flows. Image Vis Comput 48:37\u201341","journal-title":"Image Vis Comput"},{"key":"13827_CR12","doi-asserted-by":"publisher","first-page":"1576","DOI":"10.1109\/TPAMI.2011.253","volume":"34","author":"T Guha","year":"2011","unstructured":"Guha T, Ward RK (2011) Learning sparse representations for human action recognition. IEEE Trans Pattern Anal Mach Intell 34:1576\u20131588. https:\/\/doi.org\/10.1109\/TPAMI.2011.253","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"13827_CR13","doi-asserted-by":"publisher","unstructured":"Hassner T, Itcher Y, Kliper-Gross O (2012). Violent flows: real-time detection of violent crowd behavior. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 1\u20136. https:\/\/doi.org\/10.1109\/CVPRW.2012.6239348","DOI":"10.1109\/CVPRW.2012.6239348"},{"key":"13827_CR14","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/0004-3702(81)90024-2","volume":"17","author":"BK Horn","year":"1981","unstructured":"Horn BK, Schunck BG (1981) Determining optical flow. Artif Intell 17:185\u2013203","journal-title":"Artif Intell"},{"key":"13827_CR15","doi-asserted-by":"publisher","first-page":"76270","DOI":"10.1109\/ACCESS.2021.3083273","volume":"9","author":"MS Kang","year":"2021","unstructured":"Kang MS, Park RH, Park HM (2021) Efficient spatio-temporal modeling methods for real-time violence recognition. IEEE Access 9:76270\u201376285","journal-title":"IEEE Access"},{"key":"13827_CR16","doi-asserted-by":"publisher","first-page":"1796","DOI":"10.1109\/TKDE.2018.2806975","volume":"30","author":"SS Khan","year":"2018","unstructured":"Khan SS, Ahmad A (2018) Relationship between variants of one-class nearest neighbors and creating their accurate ensembles. IEEE Trans Knowl Data Eng 30:1796\u20131809","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"13827_CR17","unstructured":"Kingma DP, Ba J (2014). Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"13827_CR18","doi-asserted-by":"crossref","unstructured":"Mao D, Lin X, Liu Y, Xu M, Wang G, Chen J, Zhang W (2021). Activity recognition from skeleton and acceleration data using cnn and gcn. In: Human activity recognition challenge, Springer, Singapore, 15\u201325","DOI":"10.1007\/978-981-15-8269-1_2"},{"key":"13827_CR19","doi-asserted-by":"publisher","unstructured":"Materzynska J, Xiao T, Herzig R, Xu H, Wang X, Darrell T (2020). Something-else: compositional action recognition with spatial-temporal interaction networks. In: 2020 Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 1049\u20131059. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00113","DOI":"10.1109\/CVPR42600.2020.00113"},{"key":"13827_CR20","doi-asserted-by":"crossref","unstructured":"Mohamed MA, Mertsching B (2012). TV-L1 optical flow estimation with image details recovering based on modified census transform. In: International Symposium on Visual Computing, Springer, Berlin, Heidelberg, 482\u2013491","DOI":"10.1007\/978-3-642-33179-4_46"},{"key":"13827_CR21","doi-asserted-by":"publisher","first-page":"2057","DOI":"10.1007\/s00371-021-02266-4","volume":"38","author":"SM Mohtavipour","year":"2022","unstructured":"Mohtavipour SM, Saeidi M, Arabsorkhi A (2022) A multi-stream CNN for deep violence detection in video sequences using handcrafted features. Vis Comput 38:2057\u20132072","journal-title":"Vis Comput"},{"key":"13827_CR22","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/TNNLS.2012.2189581","volume":"23","author":"S Moon","year":"2012","unstructured":"Moon S, Qi H (2012) Hybrid dimensionality reduction method based on support vector machine and independent component analysis. IEEE transactions on neural networks and learning systems 23:749\u2013761","journal-title":"IEEE transactions on neural networks and learning systems"},{"key":"13827_CR23","doi-asserted-by":"publisher","first-page":"18365","DOI":"10.1007\/s11042-021-10682-w","volume":"80","author":"AJ Naik","year":"2021","unstructured":"Naik AJ, Gopalakrishna MT (2021) Deep-violence: individual person violent activity detection in video. Multimed Tools Appl 80:18365\u201318380","journal-title":"Multimed Tools Appl"},{"key":"13827_CR24","doi-asserted-by":"publisher","first-page":"103174","DOI":"10.1016\/j.jvcir.2021.103174","volume":"78","author":"BM Peixoto","year":"2021","unstructured":"Peixoto BM, Lavi B, Dias Z, Rocha A (2021) Harnessing high-level concepts, visual, and auditory features for violence detection in videos. J Vis Commun Image Represent 78:103174","journal-title":"J Vis Commun Image Represent"},{"key":"13827_CR25","doi-asserted-by":"publisher","unstructured":"Pock T, Urschler M, Zach C, Beichel R, Bischof H (2007). A duality based algorithm for TV-L 1-optical-flow image registration. In: 10th international conference on medical image computing and computer-assisted intervention, Brisbane, Australia, 511\u2013518. https:\/\/doi.org\/10.1007\/978-3-540-75759-7_62","DOI":"10.1007\/978-3-540-75759-7_62"},{"key":"13827_CR26","doi-asserted-by":"crossref","unstructured":"Saad K, El-Ghandour M, Raafat A, Ahmed R, Amer E (2022) A Markov model-based approach for predicting violence scenes from movies. In IEEE 2nd international Mobile, intelligent, and ubiquitous computing conference (MIUCC), 21-26","DOI":"10.1109\/MIUCC55081.2022.9781703"},{"key":"13827_CR27","doi-asserted-by":"publisher","first-page":"2945","DOI":"10.1109\/TIFS.2017.2725820","volume":"12","author":"T Senst","year":"2017","unstructured":"Senst T, Eiselein V, Kuhn A, Sikora T (2017) Crowd violence detection using global motion-compensated lagrangian features and scale-sensitive video-level representation. IEEE transactions on information forensics and security 12:2945\u20132956","journal-title":"IEEE transactions on information forensics and security"},{"key":"13827_CR28","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0120448","volume":"10","author":"I Serrano Gracia","year":"2015","unstructured":"Serrano Gracia I, Deniz Suarez O, Bueno Garcia G, Kim TK (2015) Fast fight detection. PLoS One 10:e0120448","journal-title":"PLoS One"},{"key":"13827_CR29","doi-asserted-by":"crossref","unstructured":"Shafiee MJ, Chywl B, Li F, Wong A (2017) Fast YOLO: a fast you only look once system for real-time embedded object detection in video. arXiv preprint arXiv:1709.05943","DOI":"10.15353\/vsnl.v3i1.171"},{"key":"13827_CR30","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/j.patcog.2017.01.001","volume":"65","author":"D Singh","year":"2017","unstructured":"Singh D, Mohan CK (2017) Graph formulation of video activities for abnormal activity recognition. Pattern Recogn 65:265\u2013272","journal-title":"Pattern Recogn"},{"key":"13827_CR31","doi-asserted-by":"publisher","unstructured":"Soliman MM, Kamal MH, Nashed MAEM, Mostafa YM, Chawky BS, Khattab D (2019). Violence recognition from videos using deep learning techniques. In: 2019 9th International Conference on Intelligent Computing and Information Systems, 80\u201385. https:\/\/doi.org\/10.1109\/ICICIS46948.2019.9014714","DOI":"10.1109\/ICICIS46948.2019.9014714"},{"key":"13827_CR32","doi-asserted-by":"crossref","unstructured":"Su Y, Lin G, Zhu J, Wu Q (2020). Human interaction learning on 3d skeleton point clouds for video violence recognition. In: European Conference on Computer Vision, Springer, Cham, 74\u201390","DOI":"10.1007\/978-3-030-58548-8_5"},{"key":"13827_CR33","doi-asserted-by":"crossref","unstructured":"Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3d convolutional networks. In: Proceedings of the IEEE international conference on computer vision, 4489\u20134497","DOI":"10.1109\/ICCV.2015.510"},{"key":"13827_CR34","doi-asserted-by":"publisher","first-page":"2472","DOI":"10.3390\/s19112472","volume":"19","author":"FUM Ullah","year":"2019","unstructured":"Ullah FUM, Ullah A, Muhammad K, Haq IU, Baik SW (2019) Violence detection using spatiotemporal features with 3D convolutional neural network. Sensors 19:2472","journal-title":"Sensors"},{"key":"13827_CR35","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.patrec.2020.11.018","volume":"142","author":"P Wang","year":"2021","unstructured":"Wang P, Wang P, Fan E (2021) Violence detection and face recognition based on deep learning. Pattern Recogn Lett 142:20\u201324","journal-title":"Pattern Recogn Lett"},{"key":"13827_CR36","doi-asserted-by":"crossref","unstructured":"Wu P, Liu J, Shi Y, Sun Y, Shao F, Wu Z, Yang Z (2020) Not only look, but also listen: learning multimodal violence detection under weak supervision. In: European conference on computer vision, Springer, Cham, 322\u2013339","DOI":"10.1007\/978-3-030-58577-8_20"},{"key":"13827_CR37","unstructured":"Yu L, Liu H (2003). Feature selection for high-dimensional data: a fast correlation-based filter solution. In: Proceedings of the 20th International Conference on Machine Learning, 856\u2013863"},{"key":"13827_CR38","doi-asserted-by":"publisher","first-page":"8497","DOI":"10.1007\/s11042-018-6923-3","volume":"78","author":"J Yu","year":"2019","unstructured":"Yu J, Song W, Zhou G, Hou JJ (2019) Violent scene detection algorithm based on kernel extreme learning machine and three-dimensional histograms of gradient orientation. Multimed Tools Appl 78:8497\u20138512","journal-title":"Multimed Tools Appl"},{"key":"13827_CR39","doi-asserted-by":"publisher","first-page":"7327","DOI":"10.1007\/s11042-015-2648-8","volume":"75","author":"T Zhang","year":"2016","unstructured":"Zhang T, Yang Z, Jia W, Yang B, Yang J, He X (2016) A new method for violence detection in surveillance scenes. Multimed Tools Appl 75:7327\u20137349","journal-title":"Multimed Tools Appl"},{"key":"13827_CR40","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1109\/TCSVT.2016.2589858","volume":"27","author":"T Zhang","year":"2016","unstructured":"Zhang T, Jia W, He X, Yang J (2016) Discriminative dictionary learning with motion weber local descriptor for violence detection. IEEE transactions on circuits and systems for video technology 27:696\u2013709","journal-title":"IEEE transactions on circuits and systems for video technology"},{"key":"13827_CR41","doi-asserted-by":"publisher","unstructured":"Zhou T, Wang S, Zhou Y, Yao Y, Li J, Shao L (2020). Motion-attentive transition for zero-shot video object segmentation. In: 2020 Proceedings of the AAAI Conference on Artificial Intelligence, 34:13066\u201313073. https:\/\/doi.org\/10.1609\/aaai.v34i07.7008","DOI":"10.1609\/aaai.v34i07.7008"},{"key":"13827_CR42","doi-asserted-by":"publisher","unstructured":"Zhou T, Wang W, Qi S, Ling H, Shen J (2020) Cascaded human-object interaction recognition. In: 2020 Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 4263\u20134272. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00432","DOI":"10.1109\/CVPR42600.2020.00432"},{"key":"13827_CR43","doi-asserted-by":"publisher","unstructured":"Zhou T, Wang W, Liu S, Yang Y, Van Gool L (2021). Differentiable multi-granularity human representation learning for instance-aware human semantic parsing. In: 2021 Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 1622\u20131631. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00167","DOI":"10.1109\/CVPR46437.2021.00167"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-13827-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-13827-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-13827-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T03:04:22Z","timestamp":1728011062000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-13827-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,19]]},"references-count":43,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["13827"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-13827-7","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,19]]},"assertion":[{"value":"11 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 September 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors certified that they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}