{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T19:48:10Z","timestamp":1777060090586,"version":"3.51.4"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,5,18]],"date-time":"2022-05-18T00:00:00Z","timestamp":1652832000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,5,18]],"date-time":"2022-05-18T00:00:00Z","timestamp":1652832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["61402016"],"award-info":[{"award-number":["61402016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s13042-022-01572-0","type":"journal-article","created":{"date-parts":[[2022,5,18]],"date-time":"2022-05-18T17:04:36Z","timestamp":1652893476000},"page":"445-454","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A multi-stage fusion instance learning method for anomalous event detection in videos"],"prefix":"10.1007","volume":"14","author":[{"given":"Jian","family":"Cheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3329-539X","authenticated-orcid":false,"given":"Fengquan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guiling","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wancai","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,18]]},"reference":[{"issue":"2","key":"1572_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439950","volume":"54","author":"G Pang","year":"2021","unstructured":"Pang G, Shen C, Cao L, Van Den Hengel A (2021) Deep learning for anomaly detection: a review. ACM Comput Surv (CSUR) 54(2):1\u201338","journal-title":"ACM Comput Surv (CSUR)"},{"key":"1572_CR2","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":"1572_CR3","doi-asserted-by":"crossref","unstructured":"Akcay S, Atapour-Abarghouei A, Breckon TP (2018) Ganomaly: semi-supervised anomaly detection via adversarial training. In: Asian conference on computer vision. Springer, pp 622\u2013637","DOI":"10.1007\/978-3-030-20893-6_39"},{"key":"1572_CR4","doi-asserted-by":"crossref","unstructured":"Chalapathy R, Chawla S (2019) Deep learning for anomaly detection: a survey. arXiv preprint arXiv:1901.03407","DOI":"10.1145\/3394486.3406704"},{"key":"1572_CR5","doi-asserted-by":"crossref","unstructured":"Majhi S, Das S, Bremond F, Dash R, Sa PK (2021) Weakly-supervised joint anomaly detection and classification. arXiv preprint arXiv:2108.08996","DOI":"10.1109\/FG52635.2021.9667006"},{"key":"1572_CR6","doi-asserted-by":"crossref","unstructured":"Hasan M, Choi J, Neumann J, Roy-Chowdhury AK, Davis LS (2016) Learning temporal regularity in video sequences. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 733\u2013742","DOI":"10.1109\/CVPR.2016.86"},{"key":"1572_CR7","doi-asserted-by":"crossref","unstructured":"Pang G, Yan C, Shen C, van\u00a0den Hengel A, Bai X (2020) Self-trained deep ordinal regression for end-to-end video anomaly detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12173\u201312182","DOI":"10.1109\/CVPR42600.2020.01219"},{"key":"1572_CR8","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1572_CR9","doi-asserted-by":"crossref","unstructured":"Wang H, Schmid C (2013) Action recognition with improved trajectories. In Proceedings of the IEEE international conference on computer vision, pp 3551\u20133558","DOI":"10.1109\/ICCV.2013.441"},{"key":"1572_CR10","unstructured":"Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. arXiv preprint arXiv:1406.2199"},{"key":"1572_CR11","doi-asserted-by":"crossref","unstructured":"Carreira J, Zisserman A (2017) Quo vadis, action recognition? a new model and the kinetics dataset. In: proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 6299\u20136308","DOI":"10.1109\/CVPR.2017.502"},{"key":"1572_CR12","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C, Pinz A, Zisserman A (2016) Convolutional two-stream network fusion for video action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1933\u20131941","DOI":"10.1109\/CVPR.2016.213"},{"key":"1572_CR13","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C, Pinz A, Wildes RP (2017) Spatiotemporal residual networks for video action recognition. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 3476\u20133484","DOI":"10.1109\/CVPR.2017.787"},{"key":"1572_CR14","doi-asserted-by":"crossref","unstructured":"Donahue J, Anne\u00a0Hendricks L, Guadarrama S, Rohrbach M, Venugopalan S, Saenko K, Darrell T (2015) Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2625\u20132634","DOI":"10.1109\/CVPR.2015.7298878"},{"key":"1572_CR15","doi-asserted-by":"crossref","unstructured":"Wang L, Xiong Y, Wang Z, \u00a0Qiao Y, Lin D, Tang X, Van\u00a0Gool L (2016) Temporal segment networks: Towards good practices for deep action recognition. In: European conference on computer vision. Springer, pp 20\u201336","DOI":"10.1007\/978-3-319-46484-8_2"},{"key":"1572_CR16","doi-asserted-by":"crossref","unstructured":"Nievas EB, Suarez OD, Garc\u00eda GB, Sukthankar R (2011) Violence detection in video using computer vision techniques. In: International conference on Computer analysis of images and patterns. Springer, pp 332\u2013339","DOI":"10.1007\/978-3-642-23678-5_39"},{"key":"1572_CR17","doi-asserted-by":"crossref","unstructured":"Tenenboim-Chekina L, Rokach L, Shapira B (2013) Ensemble of feature chains for anomaly detection. In: International workshop on multiple classifier systems. Springer, pp 295\u2013306","DOI":"10.1007\/978-3-642-38067-9_26"},{"key":"1572_CR18","doi-asserted-by":"crossref","unstructured":"Lu C, Shi J, Jia J (2013) Abnormal event detection at 150 fps in matlab. In: Proceedings of the IEEE international conference on computer vision, pp 2720\u20132727","DOI":"10.1109\/ICCV.2013.338"},{"issue":"1","key":"1572_CR19","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/s10618-011-0234-x","volume":"25","author":"K Noto","year":"2012","unstructured":"Noto K, Brodley C, Slonim D (2012) Frac: a feature-modeling approach for semi-supervised and unsupervised anomaly detection. Data Min Knowl Disc 25(1):109\u2013133","journal-title":"Data Min Knowl Disc"},{"key":"1572_CR20","doi-asserted-by":"crossref","unstructured":"Morais R, Le V, Tran T, Saha B, Mansour M, Venkatesh S (2019) Learning regularity in skeleton trajectories for anomaly detection in videos. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11996\u201312004","DOI":"10.1109\/CVPR.2019.01227"},{"key":"1572_CR21","doi-asserted-by":"crossref","unstructured":"Islam Z, Rukonuzzaman M, Ahmed R, Kabir Md, Farazi M, et\u00a0al (2021) Efficient two-stream network for violence detection using separable convolutional ISTM. arXiv preprint arXiv:2102.10590","DOI":"10.1109\/IJCNN52387.2021.9534280"},{"issue":"7","key":"1572_CR22","first-page":"2609","volume":"31","author":"W Peng","year":"2019","unstructured":"Peng W, Liu J, Shen F (2019) A deep one-class neural network for anomalous event detection in complex scenes. IEEE Trans Neural Networks Learn Syst 31(7):2609\u20132622","journal-title":"IEEE Trans Neural Networks Learn Syst"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01572-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-022-01572-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01572-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T09:56:02Z","timestamp":1674640562000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-022-01572-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,18]]},"references-count":22,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["1572"],"URL":"https:\/\/doi.org\/10.1007\/s13042-022-01572-0","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,18]]},"assertion":[{"value":"8 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 May 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}