{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T19:30:37Z","timestamp":1776367837968,"version":"3.51.2"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T00:00:00Z","timestamp":1597881600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T00:00:00Z","timestamp":1597881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,5]]},"DOI":"10.1007\/s11042-020-09406-3","type":"journal-article","created":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T20:14:02Z","timestamp":1597954442000},"page":"16979-16995","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":245,"title":["CNN features with bi-directional LSTM for real-time anomaly detection in surveillance networks"],"prefix":"10.1007","volume":"80","author":[{"given":"Waseem","family":"Ullah","sequence":"first","affiliation":[]},{"given":"Amin","family":"Ullah","sequence":"additional","affiliation":[]},{"given":"Ijaz Ul","family":"Haq","sequence":"additional","affiliation":[]},{"given":"Khan","family":"Muhammad","sequence":"additional","affiliation":[]},{"given":"Muhammad","family":"Sajjad","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6678-7788","authenticated-orcid":false,"given":"Sung Wook","family":"Baik","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,8,20]]},"reference":[{"key":"9406_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 30:555\u2013560","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9406_CR2","doi-asserted-by":"publisher","first-page":"102080","DOI":"10.1016\/j.scs.2020.102080","volume":"56","author":"I Al Ridhawi","year":"2020","unstructured":"Al Ridhawi I, Otoum S, Aloqaily M, Jararweh Y, Baker T (2020) Providing secure and reliable communication for next generation networks in smart cities. Sustain Cities Soc 56:102080","journal-title":"Sustain Cities Soc"},{"key":"9406_CR3","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1016\/j.jocs.2017.11.006","volume":"27","author":"M Al-Smadi","year":"2018","unstructured":"Al-Smadi M, Qawasmeh O, Al-Ayyoub M, Jararweh Y, Gupta B (2018) Deep recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels\u2019 reviews. J Comput Sci 27:386\u2013393","journal-title":"J Comput Sci"},{"key":"9406_CR4","doi-asserted-by":"publisher","first-page":"22787","DOI":"10.1007\/s11042-017-4488-1","volume":"76","author":"MA Alsmirat","year":"2017","unstructured":"Alsmirat MA, Obaidat I, Jararweh Y, Al-Saleh M (2017) A security framework for cloud-based video surveillance system. Multimed Tools Appl 76:22787\u201322802","journal-title":"Multimed Tools Appl"},{"key":"9406_CR5","doi-asserted-by":"crossref","unstructured":"Benezeth Y, Jodoin P-M, Saligrama V, Rosenberger C (2009) Abnormal events detection based on spatio-temporal co-occurences. In: 2009 IEEE conference on computer vision and pattern recognition, pp 2458\u20132465","DOI":"10.1109\/CVPRW.2009.5206686"},{"key":"9406_CR6","doi-asserted-by":"publisher","first-page":"5288","DOI":"10.1109\/TIP.2015.2479561","volume":"24","author":"K-W Cheng","year":"2015","unstructured":"Cheng K-W, Chen Y-T, Fang W-H (2015) Gaussian process regression-based video anomaly detection and localization with hierarchical feature representation. IEEE Trans Image Process 24:5288\u20135301","journal-title":"IEEE Trans Image Process"},{"key":"9406_CR7","doi-asserted-by":"publisher","first-page":"15101","DOI":"10.1007\/s11042-015-2453-4","volume":"75","author":"K-W Cheng","year":"2016","unstructured":"Cheng K-W, Chen Y-T, Fang W-H (2016) An efficient subsequence search for video anomaly detection and localization. Multimed Tools Appl 75:15101\u201315122","journal-title":"Multimed Tools Appl"},{"key":"9406_CR8","doi-asserted-by":"crossref","unstructured":"Chong YS, Tay YH (2017) Abnormal event detection in videos using spatiotemporal autoencoder. In: International symposium on neural networks, pp 189\u2013196","DOI":"10.1007\/978-3-319-59081-3_23"},{"key":"9406_CR9","doi-asserted-by":"crossref","unstructured":"Cong Y, Yuan J, Liu J (2011) Sparse reconstruction cost for abnormal event detection. In: CVPR 2011, pp 3449\u20133456","DOI":"10.1109\/CVPR.2011.5995434"},{"key":"9406_CR10","unstructured":"Gianchandani U, Tirupattur P, Shah M Weakly-supervised spatiotemporal anomaly detection"},{"key":"9406_CR11","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":"9406_CR12","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":"9406_CR13","doi-asserted-by":"publisher","first-page":"29573","DOI":"10.1007\/s11042-017-5255-z","volume":"77","author":"C He","year":"2018","unstructured":"He C, Shao J, Sun J (2018) An anomaly-introduced learning method for abnormal event detection. Multimed Tools Appl 77:29573\u201329588","journal-title":"Multimed Tools Appl"},{"key":"9406_CR14","doi-asserted-by":"crossref","unstructured":"Huo J, Gao Y, Yang W, Yin H (2012) Abnormal event detection via multi-instance dictionary learning. In: International conference on intelligent data engineering and automated learning, pp 76\u201383","DOI":"10.1007\/978-3-642-32639-4_10"},{"key":"9406_CR15","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: 2009 IEEE conference on computer vision and pattern recognition, pp 2921\u20132928","DOI":"10.1109\/CVPR.2009.5206569"},{"key":"9406_CR16","doi-asserted-by":"crossref","unstructured":"Kratz L, Nishino K (2009) Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. In: 2009 IEEE conference on computer vision and pattern recognition, pp 1446\u20131453","DOI":"10.1109\/CVPRW.2009.5206771"},{"key":"9406_CR17","unstructured":"Landi F, Snoek CG, Cucchiara R (2019) Anomaly locality in video surveillance. arXiv preprint arXiv:1901.10364"},{"key":"9406_CR18","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 36:18\u201332","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9406_CR19","doi-asserted-by":"crossref","unstructured":"Liu W, Luo W, Lian D, Gao S (2018) Future frame prediction for anomaly detection\u2013a new baseline. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6536\u20136545","DOI":"10.1109\/CVPR.2018.00684"},{"key":"9406_CR20","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"},{"key":"9406_CR21","doi-asserted-by":"crossref","unstructured":"Luo W, Liu W, Gao S (2017) A revisit of sparse coding based anomaly detection in stacked rnn framework. In: Proceedings of the IEEE international conference on computer vision, pp 341\u2013349","DOI":"10.1109\/ICCV.2017.45"},{"key":"9406_CR22","doi-asserted-by":"crossref","unstructured":"Luo W, Liu W, Gao S (2017) Remembering history with convolutional lstm for anomaly detection. In: 2017 IEEE International Conference on Multimedia and Expo (ICME), pp 439\u2013444","DOI":"10.1109\/ICME.2017.8019325"},{"key":"9406_CR23","doi-asserted-by":"crossref","unstructured":"Mahadevan V, Li W, Bhalodia V, Vasconcelos N (2010) Anomaly detection in crowded scenes. In: 2010 IEEE computer society conference on computer vision and pattern recognition, pp 1975\u20131981","DOI":"10.1109\/CVPR.2010.5539872"},{"key":"9406_CR24","doi-asserted-by":"crossref","unstructured":"Mehran R, Oyama A, Shah M (2009) Abnormal crowd behavior detection using social force model. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp 935\u2013942","DOI":"10.1109\/CVPR.2009.5206641"},{"key":"9406_CR25","doi-asserted-by":"publisher","first-page":"4455","DOI":"10.1109\/JIOT.2019.2950469","volume":"7","author":"K Muhammad","year":"2020","unstructured":"Muhammad K, Hussain T, Tanveer M, Sannino G, de Albuquerque VHC (May 2020) Cost-effective video summarization using deep CNN with hierarchical weighted fusion for IoT surveillance networks. IEEE Internet Things J 7:4455\u20134463","journal-title":"IEEE Internet Things J"},{"key":"9406_CR26","doi-asserted-by":"crossref","unstructured":"Rabiee H, Haddadnia J, Mousavi H, Kalantarzadeh M, Nabi M, Murino V (2016) Novel dataset for fine-grained abnormal behavior understanding in crowd. In: 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp 95\u2013101","DOI":"10.1109\/AVSS.2016.7738074"},{"key":"9406_CR27","doi-asserted-by":"publisher","first-page":"1992","DOI":"10.1109\/TIP.2017.2670780","volume":"26","author":"M Sabokrou","year":"2017","unstructured":"Sabokrou M, Fayyaz M, Fathy M, Klette R (2017) Deep-cascade: cascading 3d deep neural networks for fast anomaly detection and localization in crowded scenes. IEEE Trans Image Process 26:1992\u20132004","journal-title":"IEEE Trans Image Process"},{"key":"9406_CR28","doi-asserted-by":"crossref","unstructured":"Sak H, Senior A, Beaufays F (2014) Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: Fifteenth annual conference of the international speech communication association","DOI":"10.21437\/Interspeech.2014-80"},{"key":"9406_CR29","doi-asserted-by":"crossref","unstructured":"Shao J, Loy C-C, Kang K, Wang X (2016) Slicing convolutional neural network for crowd video understanding. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5620\u20135628","DOI":"10.1109\/CVPR.2016.606"},{"key":"9406_CR30","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"9406_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":"9406_CR32","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"9406_CR33","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1109\/ACCESS.2017.2778011","volume":"6","author":"A Ullah","year":"2017","unstructured":"Ullah A, Ahmad J, Muhammad K, Sajjad M, Baik SW (2017) Action recognition in video sequences using deep bi-directional LSTM with CNN features. IEEE Access 6:1155\u20131166","journal-title":"IEEE Access"},{"key":"9406_CR34","doi-asserted-by":"publisher","first-page":"17375","DOI":"10.1007\/s11042-017-5309-2","volume":"77","author":"T Wang","year":"2018","unstructured":"Wang T, Qiao M, Zhu A, Niu Y, Li C, Snoussi H (2018) Abnormal event detection via covariance matrix for optical flow based feature. Multimed Tools Appl 77:17375\u201317395","journal-title":"Multimed Tools Appl"},{"key":"9406_CR35","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.cviu.2016.10.010","volume":"156","author":"D Xu","year":"2017","unstructured":"Xu D, Yan Y, Ricci E, Sebe N (2017) Detecting anomalous events in videos by learning deep representations of appearance and motion. Comput Vis Image Underst 156:117\u2013127","journal-title":"Comput Vis Image Underst"},{"key":"9406_CR36","doi-asserted-by":"crossref","unstructured":"Yu Y, Zhao T, Wang M, Wang K, He L (2020) Uni-OPU: an FPGA-based uniform accelerator for convolutional and transposed convolutional networks. In: IEEE transactions on very large scale integration (VLSI) systems","DOI":"10.1109\/TVLSI.2020.2995741"},{"key":"9406_CR37","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1007\/s11042-015-3133-0","volume":"76","author":"T Zhang","year":"2017","unstructured":"Zhang T, Jia W, Yang B, Yang J, He X, Zheng Z (2017) MoWLD: a robust motion image descriptor for violence detection. Multimed Tools Appl 76:1419\u20131438","journal-title":"Multimed Tools Appl"},{"key":"9406_CR38","doi-asserted-by":"crossref","unstructured":"Zhang J, Kalantidis Y, Rohrbach M, Paluri M, Elgammal A, Elhoseiny M (2019) Large-scale visual relationship understanding. In: Proceedings of the AAAI conference on artificial intelligence, pp 9185\u20139194","DOI":"10.1609\/aaai.v33i01.33019185"},{"key":"9406_CR39","doi-asserted-by":"crossref","unstructured":"Zhao B, Fei-Fei L, Xing EP (2011) Online detection of unusual events in videos via dynamic sparse coding. In: CVPR 2011, pp 3313\u20133320","DOI":"10.1109\/CVPR.2011.5995524"},{"key":"9406_CR40","doi-asserted-by":"crossref","unstructured":"Zhong J-X, 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: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1237\u20131246","DOI":"10.1109\/CVPR.2019.00133"},{"key":"9406_CR41","unstructured":"Zhu Y, Newsam S (2019) Motion-aware feature for improved video anomaly detection. arXiv preprint arXiv:1907.10211"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09406-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-020-09406-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09406-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T05:29:33Z","timestamp":1667971773000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-020-09406-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,20]]},"references-count":41,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2021,5]]}},"alternative-id":["9406"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-09406-3","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,20]]},"assertion":[{"value":"15 January 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 July 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 July 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 August 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}