{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T02:55:35Z","timestamp":1784948135310,"version":"3.55.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"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":["Peer-to-Peer Netw. Appl."],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s12083-025-02134-1","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T11:23:57Z","timestamp":1765884237000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A context-aware multi-modal generative adversarial network for real-time anomaly detection in video surveillance"],"prefix":"10.1007","volume":"19","author":[{"given":"Pravinth","family":"Raja","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dhanalakshmi","family":"B K","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajan","family":"T","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vinayakumar","family":"Ravi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Norah Saleh","family":"Alghamdi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,16]]},"reference":[{"issue":"16","key":"2134_CR1","doi-asserted-by":"publisher","first-page":"4110","DOI":"10.3390\/rs14164110","volume":"14","author":"D Avola","year":"2022","unstructured":"Avola D, Cannistraci I, Cascio M, Cinque L, Diko A, Fagioli A, Foresti GL, Lanzino R, Mancini M, Mecca A et al (2022) A novel GAN-based anomaly detection and localization method for aerial video surveillance at low altitude. Remote Sens 14(16):4110","journal-title":"Remote Sens"},{"issue":"8","key":"2134_CR2","doi-asserted-by":"publisher","first-page":"4077","DOI":"10.1007\/s12652-021-03323-5","volume":"13","author":"T Alafif","year":"2022","unstructured":"Alafif T, Alzahrani B, Cao Y, Alotaibi R, Barnawi A, Chen M (2022) Generative adversarial network based abnormal behavior detection in massive crowd videos: a hajj case study. J Ambient Intell Humaniz Comput 13(8):4077\u20134088","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"1","key":"2134_CR3","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s12652-022-04393-9","volume":"14","author":"O AbuAlghanam","year":"2023","unstructured":"AbuAlghanam O, Alazzam H, Alhenawi E, Qatawneh M, Adwan O (2023) Fusion-based anomaly detection system using modified isolation forest for internet of things. J Ambient Intell Humaniz Comput 14(1):131\u2013145","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"2134_CR4","doi-asserted-by":"publisher","first-page":"107969","DOI":"10.1016\/j.patcog.2021.107969","volume":"116","author":"D Chen","year":"2021","unstructured":"Chen D, Yue L, Chang X, Xu M, Jia T (2021) Nm-GAN: noise-modulated generative adversarial network for video anomaly detection. Pattern Recogn 116:107969","journal-title":"Pattern Recogn"},{"key":"2134_CR5","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.matpr.2022.01.171","volume":"58","author":"S Anoopa","year":"2022","unstructured":"Anoopa S, Salim A (2022) Survey on anomaly detection in surveillance videos. Mater Today Proc 58:162\u2013167","journal-title":"Mater Today Proc"},{"issue":"37","key":"2134_CR6","doi-asserted-by":"publisher","first-page":"27511","DOI":"10.1007\/s11042-020-09277-8","volume":"79","author":"M George","year":"2020","unstructured":"George M, Jose BR, Mathew J (2020) Abnormal activity detection using shear transformed spatio-temporal regions at the surveillance network edge. Multimedia Tools Appl 79(37):27511\u201327532","journal-title":"Multimedia Tools Appl"},{"issue":"6","key":"2134_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3459992","volume":"54","author":"Z Cai","year":"2021","unstructured":"Cai Z, Xiong Z, Xu H, Wang P, Li W, Pan Y (2021) Generative adversarial networks: a survey toward private and secure applications. ACM Comput Surv (CSUR) 54(6):1\u201338","journal-title":"ACM Comput Surv (CSUR)"},{"key":"2134_CR8","doi-asserted-by":"crossref","unstructured":"Colangelo F, Battisti F, Carli M, Neri A, Calabr\u00f3 F (2017) Enhancing audio surveillance with hierarchical recurrent neural networks. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp 1\u20136. IEEE","DOI":"10.1109\/AVSS.2017.8078496"},{"issue":"1","key":"2134_CR9","doi-asserted-by":"publisher","first-page":"16291","DOI":"10.1038\/s41598-025-01146-4","volume":"15","author":"Y Wang","year":"2025","unstructured":"Wang Y, Zhao Y, Huo Y, Lu Y (2025) Multimodal anomaly detection in complex environments using video and audio fusion. Sci Rep 15(1):16291","journal-title":"Sci Rep"},{"key":"2134_CR10","doi-asserted-by":"crossref","unstructured":"Fayyaz M, Bahrami E, Diba A, Noroozi M, Adeli E, Van\u00a0Gool L, Gall J (2021) 3d cnns with adaptive temporal feature resolutions. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4731\u20134740","DOI":"10.1109\/CVPR46437.2021.00470"},{"issue":"2","key":"2134_CR11","doi-asserted-by":"publisher","first-page":"637","DOI":"10.3390\/s24020637","volume":"24","author":"X Qu","year":"2024","unstructured":"Qu X, Liu Z, Wu CQ, Hou A, Yin X, Chen Z (2024) Mfgan: multimodal fusion for industrial anomaly detection using attention-based autoencoder and generative adversarial network. Sensors 24(2):637","journal-title":"Sensors"},{"key":"2134_CR12","unstructured":"Yang X, Howley E, Schukat M (2023) Adt: agent-based dynamic thresholding for anomaly detection. arXiv:2312.01488"},{"key":"2134_CR13","doi-asserted-by":"crossref","unstructured":"Dewangan F, Biswal M (2023) Medium-term load forecasting using ann and rnn in microgrid integrating renewable energy source. In: 2023 2nd International Conference for Innovation in Technology (INOCON), pp 1\u20135. IEEE","DOI":"10.1109\/INOCON57975.2023.10101126"},{"issue":"10","key":"2134_CR14","doi-asserted-by":"publisher","first-page":"4590","DOI":"10.3390\/app11104590","volume":"11","author":"AB Ahmad","year":"2021","unstructured":"Ahmad AB, Tsuji T (2021) Traffic monitoring system based on deep learning and seismometer data. Appl Sci 11(10):4590","journal-title":"Appl Sci"},{"key":"2134_CR15","doi-asserted-by":"crossref","unstructured":"Chandel H, Vatta S (2015) Occlusion detection and handling: a review. Int J Comput Appl 120(10)","DOI":"10.5120\/21264-3857"},{"key":"2134_CR16","doi-asserted-by":"crossref","unstructured":"Chang Y, Tu Z, Xie W, Yuan J (2020) Clustering driven deep autoencoder for video anomaly detection. In: Computer vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XV 16, pp 329\u2013345. Springer","DOI":"10.1007\/978-3-030-58555-6_20"},{"issue":"29","key":"2134_CR17","doi-asserted-by":"publisher","first-page":"73363","DOI":"10.1007\/s11042-023-17770-z","volume":"83","author":"M Shoaib","year":"2024","unstructured":"Shoaib M, Shah B, Hussain T, Yang B, Ullah A, Khan J, Ali F (2024) A deep learning-assisted visual attention mechanism for anomaly detection in videos. Multimedia Tools Appl 83(29):73363\u201373390","journal-title":"Multimedia Tools Appl"},{"key":"2134_CR18","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.neucom.2021.01.097","volume":"439","author":"T Li","year":"2021","unstructured":"Li T, Chen X, Zhu F, Zhang Z, Yan H (2021) Two-stream deep spatial-temporal auto-encoder for surveillance video abnormal event detection. Neurocomputing 439:256\u2013270","journal-title":"Neurocomputing"},{"key":"2134_CR19","doi-asserted-by":"crossref","unstructured":"Ul Amin S, Kim Y, Sami I, Park S, Seo S (2023) An efficient attention-based strategy for anomaly detection in surveillance video. Comput Syst Sci Eng 46(3)","DOI":"10.32604\/csse.2023.034805"},{"issue":"7","key":"2134_CR20","doi-asserted-by":"publisher","first-page":"2300706","DOI":"10.1002\/aisy.202300706","volume":"6","author":"S Ul Amin","year":"2024","unstructured":"Ul Amin S, Kim B, Jung Y, Seo S, Park S (2024) Video anomaly detection utilizing efficient spatiotemporal feature fusion with 3d convolutions and long short-term memory modules. Adv Intell Syst 6(7):2300706","journal-title":"Adv Intell Syst"},{"issue":"2","key":"2134_CR21","doi-asserted-by":"publisher","first-page":"2075","DOI":"10.1007\/s13369-022-07096-7","volume":"48","author":"W Zhang","year":"2023","unstructured":"Zhang W, He P, Wang S, An L, Yang F (2023) A dynamic convolutional generative adversarial network for video anomaly detection. Arab J Sci Eng 48(2):2075\u20132085","journal-title":"Arab J Sci Eng"},{"key":"2134_CR22","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1016\/j.neucom.2021.12.093","volume":"493","author":"X Xia","year":"2022","unstructured":"Xia X, Pan X, Li N, He X, Ma L, Zhang X, Ding N (2022) Gan-based anomaly detection: a review. Neurocomputing 493:497\u2013535","journal-title":"Neurocomputing"},{"key":"2134_CR23","unstructured":"Amin SU, Abbas MS, Kim B, Jung Y, Seo S (2024) Enhanced anomaly detection in pandemic surveillance videos: an attention approach with efficientnet-b0 and CBAM integration. IEEE Access"},{"key":"2134_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109348","volume":"252","author":"Q Li","year":"2022","unstructured":"Li Q, Yang R, Xiao F, Bhanu B, Zhang F (2022) Attention-based anomaly detection in multi-view surveillance videos. Knowl-Based Syst 252:109348","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"2134_CR25","first-page":"8876056","volume":"2020","author":"W Hao","year":"2020","unstructured":"Hao W, Zhang R, Li S, Li J, Li F, Zhao S, Zhang W (2020) Anomaly event detection in security surveillance using two-stream based model. Secur Commun Netw 2020(1):8876056","journal-title":"Secur Commun Netw"},{"issue":"1","key":"2134_CR26","doi-asserted-by":"publisher","first-page":"2692","DOI":"10.1038\/s41598-025-85822-5","volume":"15","author":"SB Veesam","year":"2025","unstructured":"Veesam SB, Satish AR, Tupakula S, Chinnam Y, Prakash K, Bansal S, Faruque MRI (2025) Design of an integrated model with temporal graph attention and transformer-augmented RNNs for enhanced anomaly detection. Sci Rep 15(1):2692","journal-title":"Sci Rep"},{"key":"2134_CR27","unstructured":"Xiao D, Chen G, Peng P, Huang Y, Zhao Y, Dai Y, Tian Y (2025) When every millisecond counts: real-time anomaly detection via the multimodal asynchronous hybrid network. arXiv:2506.17457"},{"key":"2134_CR28","unstructured":"Wu P, Su W, Pang G, Sun Y, Yan Q, Wang P, Zhang Y (2025) Avadclip: audio-visual collaboration for robust video anomaly detection. arXiv:2504.04495"},{"issue":"1","key":"2134_CR29","first-page":"1","volume":"15","author":"U Verma","year":"2025","unstructured":"Verma U, Pai MMM, Pai RM et al (2025) Contextual information based anomaly detection for multi-scene aerial videos. Sci Rep 15(1):1\u201318","journal-title":"Sci Rep"},{"key":"2134_CR30","unstructured":"Tan M, Le Q (2021) Efficientnetv2: smaller models and faster training. In: International conference on machine learning, pp 10096\u201310106. PMLR"},{"key":"2134_CR31","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\u20136488","DOI":"10.1109\/CVPR.2018.00678"},{"key":"2134_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108213","volume":"122","author":"Y Chang","year":"2022","unstructured":"Chang Y, Tu Z, Xie W, Luo B, Zhang S, Sui H, Yuan J (2022) Video anomaly detection with spatio-temporal dissociation. Pattern Recogn 122:108213","journal-title":"Pattern Recogn"},{"key":"2134_CR33","doi-asserted-by":"crossref","unstructured":"Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) Eca-net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11534\u201311542","DOI":"10.1109\/CVPR42600.2020.01155"},{"issue":"4","key":"2134_CR34","doi-asserted-by":"publisher","first-page":"837","DOI":"10.1109\/TBC.2021.3099737","volume":"67","author":"Q Qu","year":"2021","unstructured":"Qu Q, Chen X, Chung V, Chen Z (2021) Light field image quality assessment with auxiliary learning based on depthwise and anglewise separable convolutions. IEEE Trans Broadcast 67(4):837\u2013850","journal-title":"IEEE Trans Broadcast"},{"issue":"5","key":"2134_CR35","doi-asserted-by":"publisher","first-page":"2239","DOI":"10.1109\/TVCG.2023.3247069","volume":"29","author":"Q Qu","year":"2023","unstructured":"Qu Q, Chen X, Chung YY, Cai W (2023) Lfacon: introducing anglewise attention to no-reference quality assessment in light field space. IEEE Trans Visual Comput Graphics 29(5):2239\u20132248","journal-title":"IEEE Trans Visual Comput Graphics"},{"key":"2134_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.111655","volume":"159","author":"SU Amin","year":"2025","unstructured":"Amin SU, Jung Y, Fayaz M, Kim B, Seo S (2025) Enhancing pine wilt disease detection with synthetic data and external attention-based transformers. Eng Appl Artif Intell 159:111655","journal-title":"Eng Appl Artif Intell"},{"key":"2134_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2022.104488","volume":"124","author":"AA Mohamed","year":"2022","unstructured":"Mohamed AA, Alqahtani F, Shalaby A, Tolba A (2022) Texture classification-based feature processing for violence-based anomaly detection in crowded environments. Image Vis Comput 124:104488","journal-title":"Image Vis Comput"},{"key":"2134_CR38","doi-asserted-by":"crossref","unstructured":"Liyuan Y, Osman G, Rahman SA, Mustapha MF (2024) Multi-modal fusion for multi-task fuzzy detection of rail anomalies. IEEE Access","DOI":"10.1109\/ACCESS.2024.3397002"},{"key":"2134_CR39","unstructured":"Mariswari R, Narayani V (2024) Real time video anomaly detection using deep belief network with semi supervised GAN. J Electr Syst 20"},{"issue":"4","key":"2134_CR40","doi-asserted-by":"publisher","first-page":"4955","DOI":"10.1007\/s40747-024-01417-z","volume":"10","author":"G Yang","year":"2024","unstructured":"Yang G, He Z, Su Z, Li Y, Hu B (2024) Keyframe recommendation based on feature intercross and fusion. Complex Intell Syst 10(4):4955\u20134971","journal-title":"Complex Intell Syst"},{"key":"2134_CR41","doi-asserted-by":"publisher","first-page":"107830","DOI":"10.1016\/j.engappai.2023.107830","volume":"131","author":"R Singh","year":"2024","unstructured":"Singh R, Sethi A, Saini K, Saurav S, Tiwari A, Singh S (2024) Attention-guided generator with dual discriminator GAN for real-time video anomaly detection. Eng Appl Artif Intell 131:107830","journal-title":"Eng Appl Artif Intell"},{"key":"2134_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2024.104950","volume":"143","author":"R Singh","year":"2024","unstructured":"Singh R, Sethi A, Saini K, Saurav S, Tiwari A, Singh S (2024) Cvad-gan: constrained video anomaly detection via generative adversarial network. Image Vis Comput 143:104950","journal-title":"Image Vis Comput"}],"container-title":["Peer-to-Peer Networking and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12083-025-02134-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12083-025-02134-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12083-025-02134-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T12:18:47Z","timestamp":1773145127000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12083-025-02134-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,16]]},"references-count":42,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["2134"],"URL":"https:\/\/doi.org\/10.1007\/s12083-025-02134-1","relation":{},"ISSN":["1936-6442","1936-6450"],"issn-type":[{"value":"1936-6442","type":"print"},{"value":"1936-6450","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,16]]},"assertion":[{"value":"17 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2025","order":3,"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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"29"}}