{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T12:10:01Z","timestamp":1758975001078,"version":"3.44.0"},"reference-count":80,"publisher":"Springer Science and Business Media LLC","issue":"33","license":[{"start":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T00:00:00Z","timestamp":1743552000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T00:00:00Z","timestamp":1743552000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Generix Group"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-025-20814-1","type":"journal-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T05:23:30Z","timestamp":1743744210000},"page":"41237-41256","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Tensor deep-anomaly: robust tensor classifier for video anomaly detection in surveillance videos"],"prefix":"10.1007","volume":"84","author":[{"given":"Alaa","family":"El Ichi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wissam","family":"Kaddah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marwa","family":"El Bouz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Isabelle","family":"Badoc","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,2]]},"reference":[{"issue":"3","key":"20814_CR1","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1109\/TPAMI.2007.70825","volume":"30","author":"A Adam","year":"2008","unstructured":"Adam A, Rivlin E, Shimshoni I, Reinitz D (2008) Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Trans Pattern Anal Mach Intell 30(3):555\u2013560","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"20814_CR2","first-page":"1967","volume":"36","author":"S Bansod","year":"2019","unstructured":"Bansod S, Nandedkar A (2019) Transfer learning for video anomaly detection. J Intell Fuzzy Syst 36(3):1967\u20131975","journal-title":"J Intell Fuzzy Syst"},{"key":"20814_CR3","doi-asserted-by":"crossref","unstructured":"Bhakat S, Ramakrishnan G (2019) Anomaly Detection in Surveillance Videos. Proc ACM India Joint Int Conf Data Sci Manag Data 252\u2013255","DOI":"10.1145\/3297001.3297034"},{"key":"20814_CR4","doi-asserted-by":"crossref","unstructured":"Di Biase G, Blum H, Siegwart R, Cadena C (2021) Pixel-wise anomaly detection in complex driving scenes. IEEE\/CVF Conf Comput Vis Pattern Recognit (CVPR) 16918\u201316927","DOI":"10.1109\/CVPR46437.2021.01664"},{"issue":"22","key":"20814_CR5","doi-asserted-by":"publisher","first-page":"15101","DOI":"10.1007\/s11042-015-2453-4","volume":"75","author":"KW Cheng","year":"2015","unstructured":"Cheng KW, Chen YT, Fang WH (2015) An efficient subsequence search for video anomaly detection and localization. Multimed Tools Appl 75(22):15101\u201315122","journal-title":"Multimed Tools Appl"},{"key":"20814_CR6","doi-asserted-by":"crossref","unstructured":"Cheng KW, Chen YT, Fang WH (2015) Video anomaly detection and localization using hierarchical feature representation and Gaussian process regression. IEEE\/CVF Conf Comput Vis Pattern Recognit (CVPR) 2909\u20132917","DOI":"10.1109\/CVPR.2015.7298909"},{"issue":"4","key":"20814_CR7","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1137\/S0895479896305696","volume":"21","author":"L De Lathauwer","year":"2000","unstructured":"De Lathauwer L, De Moor B, Vandewalle J (2000) A multilinear singular value decomposition. SIAM J Matrix Anal Appl 21(4):1253\u20131278","journal-title":"SIAM J Matrix Anal Appl"},{"key":"20814_CR8","doi-asserted-by":"crossref","unstructured":"Doshi K, Yilmaz Y (2020) Any-Shot Sequential Anomaly Detection in Surveillance Videos. IEEE\/CVF Conf Comput Vis Pattern Recognit (CVPR) 934\u2013935","DOI":"10.1109\/CVPRW50498.2020.00475"},{"key":"20814_CR9","doi-asserted-by":"publisher","first-page":"107865","DOI":"10.1016\/j.patcog.2021.107865","volume":"114","author":"K Doshi","year":"2021","unstructured":"Doshi K, Yilmaz Y (2021) Online anomaly detection in surveillance videos with asymptotic bound on false alarm rate. Pattern Recognit 114:107865","journal-title":"Pattern Recognit"},{"key":"20814_CR10","doi-asserted-by":"crossref","unstructured":"Hasan M, Choi J, Neumann J, Roy-Chowdhury A, Davis LS (2016) Learning temporal regularity in video sequences. Proc IEEE Conf Comput Vis Pattern Recognit (CVPR) 733\u2013742","DOI":"10.1109\/CVPR.2016.86"},{"key":"20814_CR11","doi-asserted-by":"crossref","unstructured":"Huang Z, Wu Y (2022) A survey on explainable anomaly detection for industrial Internet of Things. IEEE Conf Dependable Secure Comput","DOI":"10.1109\/DSC54232.2022.9888874"},{"key":"20814_CR12","doi-asserted-by":"crossref","unstructured":"Mohammadi S, Perina A, Kiani H, Murino V (2016) Angry crowds: Detecting violent events in videos. IEEE Eur Conf Comput Vis (ECCV)","DOI":"10.1007\/978-3-319-46478-7_1"},{"key":"20814_CR13","doi-asserted-by":"crossref","unstructured":"Sultani W, Chen C, Shah M (2018) Real-world anomaly detection in surveillance videos. IEEE Conf Comput Vis Pattern Recognit (CVPR) 6479\u20136488","DOI":"10.1109\/CVPR.2018.00678"},{"issue":"2","key":"20814_CR14","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1109\/MSP.2013.2297439","volume":"32","author":"A Cichocki","year":"2015","unstructured":"Cichocki A, Mic D, De Lathauwer L, Zhou G, Zhao Q, Caiafa C, Phan HA (2015) Tensor decompositions for signal processing applications: From two-way to multiway component analysis. IEEE Signal Process Mag 32(2):145\u201363","journal-title":"IEEE Signal Process Mag"},{"key":"20814_CR15","doi-asserted-by":"crossref","unstructured":"Chalapathy R, Menon AK, Chawla S (2017) Robust, deep and inductive anomaly detection. In: Machine Learning and Knowledge Discovery in Databases, 10534","DOI":"10.1007\/978-3-319-71249-9_3"},{"key":"20814_CR16","doi-asserted-by":"crossref","unstructured":"Feng JC, Hong FT, Zheng WS (2021) Mist: Multiple instance self-training framework for video anomaly detection. In: Proceedings of the IEEE\/CVF Conf. on Computer Vision and Pattern Recognition, pp 14009\u201314018","DOI":"10.1109\/CVPR46437.2021.01379"},{"key":"20814_CR17","doi-asserted-by":"crossref","unstructured":"Gong D, Liu L, Le V, Saha B, Mansour MR, Venkatesh S, Hengel Avd (2019) Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In: Proceedings of the IEEE\/CVF Int. Conf. on Computer Vision, pp 1705\u20131714","DOI":"10.1109\/ICCV.2019.00179"},{"key":"20814_CR18","first-page":"1","volume":"16","author":"RA Harshman","year":"1970","unstructured":"Harshman RA (1970) Foundations of the PARAFAC procedure: Models and conditions for an \u201cexplanatory\u2019\u2019 multi-modal factor analysis. UCLA Wkly Pap Phon 16:1\u201384","journal-title":"UCLA Wkly Pap Phon"},{"key":"20814_CR19","doi-asserted-by":"crossref","unstructured":"Hasan M, Choi J, Neumann J, Roy-Chowdhury AK, Davis LS (2016) Learning temporal regularity in video sequences. In: Proc IEEE Conf Comput Vis Pattern Recognit (CVPR) 733\u2013742","DOI":"10.1109\/CVPR.2016.86"},{"issue":"1\u20134","key":"20814_CR20","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1002\/sapm192761164","volume":"6","author":"FL Hitchcock","year":"1927","unstructured":"Hitchcock FL (1927) The expression of a tensor or a polyadic as a sum of products. J Math Phys 6(1\u20134):164\u2013189","journal-title":"J Math Phys"},{"key":"20814_CR21","unstructured":"Hui L, Zhongqi Y, Qianru S, Bin L, Zhen C, Hanwang Z (2023) Unbiased multiple instance learning for weakly supervised video anomaly detection. In: Proc IEEE Conf Comput Vis Pattern Recognit (CVPR)"},{"issue":"3","key":"20814_CR22","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1137\/07070111X","volume":"51","author":"TG Kolda","year":"2009","unstructured":"Kolda TG, Bader BW (2009) Tensor decompositions and applications. SIAM Rev 51(3):455\u2013500","journal-title":"SIAM Rev"},{"key":"20814_CR23","unstructured":"Kossaifi J, Panagakis Y, Pantic M (2018) TensorLy: Tensor Learning in Python. J Mach Learn Res 18(152):1\u20136. https:\/\/www.jmlr.org\/"},{"key":"20814_CR24","doi-asserted-by":"crossref","unstructured":"Kim J, Grauman K (2009) Observe locally, infer globally: a space-time MRF for detecting abnormal activities with incremental updates. In: Proc IEEE Conf Comput Vis Pattern Recognit (CVPR):2921\u20132928","DOI":"10.1109\/CVPR.2009.5206569"},{"key":"20814_CR25","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet Classification with Deep Convolutional Neural Networks. In: Adv Neural Inf Process Syst (NIPS)"},{"key":"20814_CR26","doi-asserted-by":"crossref","unstructured":"Lee S, Kim HG, Ro YM (2018) STAN: Spatio-Temporal Adversarial Networks for Abnormal Event Detection. In: Proc IEEE Int Conf Acoust Speech Signal Process (ICASSP):1323\u20131327","DOI":"10.1109\/ICASSP.2018.8462388"},{"issue":"1","key":"20814_CR27","first-page":"18","volume":"36","author":"W Li","year":"2013","unstructured":"Li W, Mahadevan V, Vasconcelos N (2013) Anomaly detection and localization in crowded scenes. IEEE Trans Pattern Anal Mach Intell 36(1):18\u201332","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"20814_CR28","first-page":"1395","volume":"36","author":"S Li","year":"2022","unstructured":"Li S, Liu F, Jiao L (2022) Self-training multi-sequence learning with transformer for weakly supervised video anomaly detection. Proc AAAI Conf Artif Intell 36:1395\u20131403","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"20814_CR29","doi-asserted-by":"crossref","unstructured":"Li N, Chang F, Liu C (2020) Spatial-temporal Cascade Autoencoder for Video Anomaly Detection in Crowded Scenes. IEEE Trans Multimedia 1\u20131","DOI":"10.1109\/TMM.2020.2984093"},{"key":"20814_CR30","doi-asserted-by":"publisher","unstructured":"Liu D, Cui Y, Cao Z, Chen Y (2020) A large-scale simulation dataset: boost the detection accuracy for special weather conditions. Int Jt Conf Neural Netw (IJCNN) 1\u20138. https:\/\/doi.org\/10.1109\/IJCNN48605.2020.9206716","DOI":"10.1109\/IJCNN48605.2020.9206716"},{"key":"20814_CR31","doi-asserted-by":"crossref","unstructured":"Liu W, Luo W, Lian D, Gao S (2018) Future frame prediction for anomaly detection ? a new baseline. In: Proc IEEE Conf Comput Vis Pattern Recognit (CVPR) pp 6536\u20136545","DOI":"10.1109\/CVPR.2018.00684"},{"key":"20814_CR32","doi-asserted-by":"crossref","unstructured":"Liu K, Zhu M, Fu H, Ma H, Chua T-S (2020) Enhancing anomaly detection in surveillance videos with transfer learning from action recognition. In: Proc 28th ACM Int Conf Multimedia pp 4664\u20134668","DOI":"10.1145\/3394171.3416298"},{"key":"20814_CR33","doi-asserted-by":"crossref","unstructured":"Lu C, Shi J, Jia J (2013) Abnormal event detection at 150 FPS in MATLAB. In: Proc IEEE Int Conf Comput Vis 2720\u20132727","DOI":"10.1109\/ICCV.2013.338"},{"key":"20814_CR34","doi-asserted-by":"crossref","unstructured":"Liu W, Luo W, Lian D, Gao S (2018) Future frame prediction for anomaly detection ? a new baseline. In: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 6536\u20136545","DOI":"10.1109\/CVPR.2018.00684"},{"key":"20814_CR35","doi-asserted-by":"crossref","unstructured":"Lv H, Zhou C, Cui Z, Xu C, Li Y, Yang J (2021) Localizing anomalies from weakly-labeled videos. IEEE Trans Image Process (TIP)","DOI":"10.1109\/TIP.2021.3072863"},{"key":"20814_CR36","doi-asserted-by":"crossref","unstructured":"Mahadevan V, Li W, Bhalodia V, Vasconcelos N (2010) Anomaly detection in crowded scenes. In: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit pp 1975\u20131981","DOI":"10.1109\/CVPR.2010.5539872"},{"key":"20814_CR37","doi-asserted-by":"crossref","unstructured":"Marsden M, McGuinness K, Little S, OConnor NE (2016) Holistic features for real-time crowd behaviour anomaly detection. In: Proc IEEE Int Conf Image Process (ICIP) pp 918\u2013922","DOI":"10.1109\/ICIP.2016.7532491"},{"key":"20814_CR38","doi-asserted-by":"crossref","unstructured":"Mehran R, Oyama A, Shah M (2009) Abnormal crowd behavior detection using social force model. In: Proc IEEE Conf Comput Vis Pattern Recognit pp 935\u2013942","DOI":"10.1109\/CVPR.2009.5206641"},{"key":"20814_CR39","doi-asserted-by":"crossref","unstructured":"Mohammadi V, Perina A, Kiani H, Vittorio M (2016) Angry crowds: detecting violent events in videos. In: Proc Eur Conf Comput Vis (ECCV)","DOI":"10.1007\/978-3-319-46478-7_1"},{"key":"20814_CR40","doi-asserted-by":"crossref","unstructured":"Murugesan M, Thilagamani S (2020) Efficient anomaly detection in surveillance videos based on multi-layer perception recurrent neural network. Microprocess Microsyst","DOI":"10.1016\/j.micpro.2020.103303"},{"key":"20814_CR41","doi-asserted-by":"crossref","unstructured":"Nguyen T-N, Meunier J (2019) Anomaly detection in video sequence with appearance-motion correspondence. In: Proc IEEE\/CVF Int Conf Comput Vis (ICCV)","DOI":"10.1109\/ICCV.2019.00136"},{"key":"20814_CR42","unstructured":"Novikov A, Podoprikhin D, Osokin A, Vetrov D (2015) Tensorizing neural networks. In: Adv Neural Inf Process Syst (NeurIPS) pp 442\u2013450"},{"issue":"5","key":"20814_CR43","first-page":"2295","volume":"33","author":"IV Oseledets","year":"2011","unstructured":"Oseledets IV (2011) Tensor-train decomposition. SIAM. J Sci Comput 33(5):2295\u20132317","journal-title":"J Sci Comput"},{"key":"20814_CR44","first-page":"27","volume":"22","author":"KV Pawar","year":"2018","unstructured":"Pawar KV, Attar V (2018) Deep learning approaches for video-based anomalous activity detection. World Wide Web 22:27","journal-title":"World Wide Web"},{"issue":"2","key":"20814_CR45","first-page":"1","volume":"8","author":"EE Papalexakis","year":"2016","unstructured":"Papalexakis EE, Faloutsos C, Sidiropoulos ND (2016) Tensors for data mining and data fusion: models, applications, and scalable algorithms. ACM Trans Intell Syst Technol (TIST) 8(2):1\u201344","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"issue":"47\/48","key":"20814_CR46","doi-asserted-by":"publisher","first-page":"35275","DOI":"10.1007\/s11042-019-7702-5","volume":"79","author":"A Ramchandran","year":"2020","unstructured":"Ramchandran A, Sangaiah AK (2020) Unsupervised deep learning system for local anomaly event detection in crowded scenes. Multimed Tools Appl 79(47\/48):35275\u201335295","journal-title":"Multimed Tools Appl"},{"issue":"7","key":"20814_CR47","doi-asserted-by":"publisher","first-page":"3362","DOI":"10.1109\/JBHI.2022.3148820","volume":"26","author":"S Rao","year":"2022","unstructured":"Rao S, Li Y, Ramakrishnan R, Hassaine A, Canoy D, Cleland J, Lukasiewicz T, Salimi-Khorshidi G, Rahimi K (2022) An explainable transformer-based deep learning model for the prediction of incident heart failure. IEEE J Biomed Health Informat 26(7):3362\u20133372","journal-title":"IEEE J Biomed Health Informat"},{"key":"20814_CR48","doi-asserted-by":"crossref","unstructured":"Ravanbakhsh M, Nabi M, Sangineto E, Marcenaro L, Regazzoni C, Sebe N (2017) Abnormal event detection in videos using generative adversarial nets. In: Proc IEEE Int Conf Image Process (ICIP) pp 1577\u20131581","DOI":"10.1109\/ICIP.2017.8296547"},{"key":"20814_CR49","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proc IEEE Conf Comput Vis Pattern Recognit pp 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"20814_CR50","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2016) Yolo9000: better, faster, stronger. In: Proc IEEE Conf Comput Vis Pattern Recognit pp 7263\u20137271","DOI":"10.1109\/CVPR.2017.690"},{"key":"20814_CR51","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28"},{"issue":"6","key":"20814_CR52","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"20814_CR53","doi-asserted-by":"crossref","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg A.C, Fei-Fei L (2015) ImageNet Large Scale Visual Recognition Challenge. Int J Comput Vis (IJCV)","DOI":"10.1007\/s11263-015-0816-y"},{"key":"20814_CR54","doi-asserted-by":"crossref","unstructured":"Sainath TN, Kingsbury B, Sindhwani V, Arisoy E, Ramabhadran B (2013) Low-rank matrix factorization for deep neural network training with high-dimensional output targets. In: Proc. Int. Conf. Acoust. Speech Signal Process. (ICASSP) pp 6655\u20136659","DOI":"10.1109\/ICASSP.2013.6638949"},{"key":"20814_CR55","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) MobileNetV2: inverted residuals and linear bottlenecks. In: Proc IEEE Conf Comput Vis Pattern Recognit (CVPR) pp 4510- -4520","DOI":"10.1109\/CVPR.2018.00474"},{"issue":"13","key":"20814_CR56","doi-asserted-by":"publisher","first-page":"3551","DOI":"10.1109\/TSP.2017.2690524","volume":"65","author":"ND Sidiropoulos","year":"2017","unstructured":"Sidiropoulos ND, De Lathauwer L, Fu X, Huang K, Papalexakis EE, Faloutsos C (2017) Tensor decomposition for signal processing and machine learning. IEEE Trans Signal Process 65(13):3551\u20133582","journal-title":"IEEE Trans Signal Process"},{"key":"20814_CR57","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Proc Int Conf Learn Represent (ICLR)"},{"key":"20814_CR58","doi-asserted-by":"crossref","unstructured":"Sultani W, Choi JY (2010) Abnormal traffic detection using intelligent driver model. In: Proc ICPR","DOI":"10.1109\/ICPR.2010.88"},{"key":"20814_CR59","doi-asserted-by":"crossref","unstructured":"Sultani W, Chen C, Shah M (2018) Real-world anomaly detection in surveillance videos. In: Proc IEEE Conf Comput Vis Pattern Recognit (CVPR) pp 6479\u20136488","DOI":"10.1109\/CVPR.2018.00678"},{"key":"20814_CR60","doi-asserted-by":"crossref","unstructured":"Sun C, Jia Y, Hu Y, Wu Y (2020) Scene-aware context reasoning for unsupervised abnormal event detection in videos. In: Proc 28th ACM Int Conf Multimedia pp 184\u2013192","DOI":"10.1145\/3394171.3413887"},{"key":"20814_CR61","unstructured":"Szegedy C, Toshev A, Erhan D (2013) Deep neural networks for object detection. Adv Neural Inf Process Syst 26"},{"key":"20814_CR62","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proc IEEE Conf Comput Vis Pattern Recognit (CVPR) pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"20814_CR63","doi-asserted-by":"crossref","unstructured":"Tian Y, Pang G, Chen Y, Singh R, Verjans J.W, Carneiro G (2021) Weakly supervised video anomaly detection with robust temporal feature magnitude learning. In: Proc IEEE\/CVF Int Conf Comput Vis (ICCV) pp 4975\u20134986","DOI":"10.1109\/ICCV48922.2021.00493"},{"key":"20814_CR64","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/BF02289464","volume":"31","author":"LR Tucker","year":"1960","unstructured":"Tucker LR (1960) Some mathematical notes on three-mode factor analysis. Psychometrika 31:279\u2013311","journal-title":"Psychometrika"},{"key":"20814_CR65","doi-asserted-by":"crossref","unstructured":"Ullah W, Ullah A, Haq IU, Muhammad K, Sajjad M, Baik SW (2021) CNN features with bi-directional LSTM for real-time anomaly detection in surveillance networks. Multimed Tools Appl 16979\u201316995","DOI":"10.1007\/s11042-020-09406-3"},{"key":"20814_CR66","doi-asserted-by":"crossref","unstructured":"Wang J, Cherian A (2019) Gods: generalized one-class discriminative subspaces for anomaly detection. In: Proc IEEE\/CVF Int Conf Comput Vis 8201\u20138211","DOI":"10.1109\/ICCV.2019.00829"},{"key":"20814_CR67","doi-asserted-by":"crossref","unstructured":"Wang X, Zhang S, Qing Z, Gao C, Zhang Y, Zhao D, Sang N (2023) Molo: motion-augmented long-short contrastive learning for few-shot action recognition. In: Proc IEEE\/CVF Conf Comput Vis Pattern Recognit pp 18011\u201318021","DOI":"10.1109\/CVPR52729.2023.01727"},{"key":"20814_CR68","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: Computer Vision?ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, Proc. Part XXX 16, pp 322\u2013339","DOI":"10.1007\/978-3-030-58577-8_20"},{"key":"20814_CR69","doi-asserted-by":"crossref","unstructured":"Wu H, Shao J, Xu X, Shen F, Shen H (2017) A system for spatiotemporal anomaly localization in surveillance videos. In: Proc 25th ACM Int Conf Multimedia 1225\u20131226","DOI":"10.1145\/3123266.3127912"},{"key":"20814_CR70","doi-asserted-by":"crossref","unstructured":"Wu JC, Hsieh HY, Chen DJ, Fuh CS, Liu TL (2022) Self-supervised sparse representation for video anomaly detection. In: European Conf on Comput Vis pp 729\u2013745","DOI":"10.1007\/978-3-031-19778-9_42"},{"issue":"33","key":"20814_CR71","doi-asserted-by":"publisher","first-page":"23729","DOI":"10.1007\/s11042-020-08976-6","volume":"79","author":"Y Xiao","year":"2020","unstructured":"Xiao Y, Tian Z, Yu J, Zhang Y, Liu S, Du S, Lan X (2020) A review of object detection based on deep learning. Multimed Tools Appl 79(33):23729\u201323791","journal-title":"Multimed Tools Appl"},{"key":"20814_CR72","doi-asserted-by":"crossref","unstructured":"Xu D, Ricci E, Yan Y, Song J, Sebe N (2015) Learning deep representations of appearance and motion for anomalous event detection. In: Proc British Mach Vision Conf pp 8.1\u20138.12","DOI":"10.5244\/C.29.8"},{"issue":"1","key":"20814_CR73","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/TCDS.2018.2883368","volume":"12","author":"S Yan","year":"2020","unstructured":"Yan S, Smith JS, Lu W, Zhang B (2020) Abnormal event detection from videos using a two-stream recurrent variational autoencoder. IEEE Trans Cogn Dev Syst 12(1):30\u201342","journal-title":"IEEE Trans Cogn Dev Syst"},{"key":"20814_CR74","doi-asserted-by":"crossref","unstructured":"Yang Z, Liu J, Wu Z, Wu P, Liu X (2023) Video event restoration based on keyframes for video anomaly detection. In: Proc IEEE\/CVF Conf Comput Vis Pattern Recognit pp 14592\u201314601","DOI":"10.1109\/CVPR52729.2023.01402"},{"key":"20814_CR75","doi-asserted-by":"crossref","unstructured":"Yue Z, Sun Q, Luo B, Cui Z, Zhang H (2023) Unbiased multiple instance learning for weakly supervised video anomaly detection. In: Proc IEEE\/CVF Conf Comput Vis Pattern Recognit 8022\u20138031","DOI":"10.1109\/CVPR52729.2023.00775"},{"key":"20814_CR76","doi-asserted-by":"crossref","unstructured":"Zahid Y, Tahir MA, Durrani MN (2020) Ensemble learning using bagging and Inception-V3 for anomaly detection in surveillance videos. In: IEEE Int Conf Image Process (ICIP)","DOI":"10.1109\/ICIP40778.2020.9190673"},{"key":"20814_CR77","doi-asserted-by":"crossref","unstructured":"Zhang J, Qing L, Miao J (2019) Temporal convolutional network with complementary inner bag loss for weakly supervised anomaly detection. In: Proc IEEE Int Conf Image Process (ICIP)","DOI":"10.1109\/ICIP.2019.8803657"},{"key":"20814_CR78","doi-asserted-by":"crossref","unstructured":"Zhao B, Fei-Fei L, Xing E.P (2011) Online detection of unusual events in videos via dynamic sparse coding. In: Proc IEEE Conf Comput Vis Pattern Recognit (CVPR) pp 3313\u20133320","DOI":"10.1109\/CVPR.2011.5995524"},{"key":"20814_CR79","doi-asserted-by":"crossref","unstructured":"Zhao Y, Deng B, Shen C, Liu Y, Lu H, Hua X-S (2017) Spatio-temporal autoencoder for video anomaly detection. In: Proc 25th ACM Int Conf Multimedia pp 1933\u20131941","DOI":"10.1145\/3123266.3123451"},{"key":"20814_CR80","doi-asserted-by":"crossref","unstructured":"Zhong JX, Li N, Kong W, Liu S, Li TH, Li G (2019) Graph convolutional label noise cleaner: train a plug-and-play action classifier for anomaly detection. In: Proc IEEE\/CVF Conf Comput Vis Pattern Recognit pp 1237\u20131246","DOI":"10.1109\/CVPR.2019.00133"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-20814-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-025-20814-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-20814-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T11:56:13Z","timestamp":1758974173000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-025-20814-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,2]]},"references-count":80,"journal-issue":{"issue":"33","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["20814"],"URL":"https:\/\/doi.org\/10.1007\/s11042-025-20814-1","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2025,4,2]]},"assertion":[{"value":"7 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 February 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}