Residual Spatiotemporal Autoencoder with Skip Connected and Memory Guided Network for Detecting Video Anomalies
Real-time video anomaly detection and localization still prevail as a challenging task. Autoencoders are expected to give high reconstruction error for abnormal events than normal events while trained on video segments of normal events. Nevertheless, this assumption is not always true in practice. S...
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| Vydáno v: | Neural processing letters Ročník 53; číslo 6; s. 4677 - 4692 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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01.12.2021
Springer Nature B.V |
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| ISSN: | 1370-4621, 1573-773X |
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| Abstract | Real-time video anomaly detection and localization still prevail as a challenging task. Autoencoders are expected to give high reconstruction error for abnormal events than normal events while trained on video segments of normal events. Nevertheless, this assumption is not always true in practice. Sometimes the autoencoder offers better generalization. Therefore, it also reconstructs abnormal events well, leading to slightly degraded performance for anomaly detection. To alleviate this issue, we propose a Skip connected and Memory Guided Network (SMGNet) for video anomaly detection. The memory guided network with skip connection help in avoiding loss of meaningful information such as foreground patterns, in addition to memorizing significant normality patterns. The effect of augmenting memory guided network with skip connection in the residual spatiotemporal autoencoder (R-STAE) architecture is evaluated. The proposed technique achieved improved results over three benchmark datasets. |
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| AbstractList | Real-time video anomaly detection and localization still prevail as a challenging task. Autoencoders are expected to give high reconstruction error for abnormal events than normal events while trained on video segments of normal events. Nevertheless, this assumption is not always true in practice. Sometimes the autoencoder offers better generalization. Therefore, it also reconstructs abnormal events well, leading to slightly degraded performance for anomaly detection. To alleviate this issue, we propose a Skip connected and Memory Guided Network (SMGNet) for video anomaly detection. The memory guided network with skip connection help in avoiding loss of meaningful information such as foreground patterns, in addition to memorizing significant normality patterns. The effect of augmenting memory guided network with skip connection in the residual spatiotemporal autoencoder (R-STAE) architecture is evaluated. The proposed technique achieved improved results over three benchmark datasets. |
| Author | Chandrakala, S. Deepak, K. Srinivas, V. |
| Author_xml | – sequence: 1 givenname: S. orcidid: 0000-0003-4723-1984 surname: Chandrakala fullname: Chandrakala, S. email: chandrakal@cse.sastra.edu, sckala@cse.iitm.ac.in organization: Intelligent Systems Group, SASTRA Deemed to be University – sequence: 2 givenname: V. surname: Srinivas fullname: Srinivas, V. organization: Intelligent Systems Group, SASTRA Deemed to be University – sequence: 3 givenname: K. surname: Deepak fullname: Deepak, K. organization: SRM Institute of Science and Technology |
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| Cites_doi | 10.1109/CVPR.2009.5206641 10.1109/ICCV.2015.510 10.2352/ISSN.2470-1173.2017.7.MWSF-330 10.1109/ICIP.2017.8296547 10.1016/j.patcog.2016.09.016 10.1109/IWBF.2017.7935096 10.1007/978-3-319-10602-1_48 10.1109/TMM.2017.2666540 10.1109/CVPR.2010.5539872 10.1016/j.neucom.2019.08.044 10.1109/NCVPRIPG.2013.6776164 10.1109/ACCESS.2020.2993373 10.1109/TSP.2017.8076061 10.1016/j.neucom.2016.09.063 10.1109/TIP.2015.2475625 10.1109/ICCV.2017.45 10.1007/s11760-020-01740-1 10.1109/WACV.2019.00212 10.1109/ICCV.2017.315 10.1109/ICME.2017.8019325 10.1007/978-3-319-46454-1_21 10.1109/ICCV.2019.00179 10.1109/CVPR.2018.00684 10.1145/3123266.3123451 10.1007/11744047_33 10.1109/CVPR.2019.00803 10.1109/ICCV.2017.391 10.1109/CVPR.2005.177 10.1109/CVPR.2009.5206771 10.1007/s11263-016-0981-7 10.1109/TMM.2020.2984093 10.1109/WACV45572.2020.9093417 10.1007/978-3-319-59081-3_23 10.5244/C.31.139 10.1109/ICCV.2015.169 10.1109/ICCV.2013.338 10.1109/CVPR.2016.86 10.1109/TIFS.2018.2856189 10.5244/C.29.8 |
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| Keywords | Video anomaly detection Residual blocks Normality modeling Memory guided network Spatio temporal autoencoders |
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| References_xml | – reference: Biswas S, Babu RV (2013) Real time anomaly detection in h. 264 compressed videos. In: 2013 Fourth national conference on computer vision, pattern recognition, image processing and graphics (NCVPRIPG), IEEE, pp 1–4 – reference: ChanTHJiaKGaoSLuJZengZMaYPcanet: a simple deep learning baseline for image classification?IEEE Trans Image Process2015241250175032340609910.1109/TIP.2015.2475625 – reference: KhanMUKParkHSKyungCMRejecting motion outliers for efficient crowd anomaly detectionIEEE Trans Inf Forensics Secur201814254155610.1109/TIFS.2018.2856189 – reference: 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 341–349 – reference: Tudor Ionescu R, Smeureanu S, Alexe B, Popescu M (2017) Unmasking the abnormal events in video. In: Proceedings of the ieee international conference on computer vision, pp 2895–2903 – reference: Dalal N, Triggs B, Schmid C (2006) Human detection using oriented histograms of flow and appearance. In: European conference on computer vision, Springer, pp 428–441 – reference: 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), IEEE, pp 439–444 – reference: Ravanbakhsh M, Nabi M, Sangineto E, Marcenaro L, Regazzoni C, Sebe N (2017) Abnormal event detection in videos using generative adversarial nets. In: 2017 IEEE international conference on image processing (ICIP), IEEE, pp 1577–1581 – reference: SunQLiuHHaradaTOnline growing neural gas for anomaly detection in changing surveillance scenesPattern Recogn20176418720110.1016/j.patcog.2016.09.016 – reference: Ionescu RT, Smeureanu S, Popescu M, Alexe B (2019) Detecting abnormal events in video using narrowed normality clusters. In: 2019 IEEE winter conference on applications of computer vision (WACV), IEEE, pp 1951–1960 – reference: Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft coco: Common objects in context. In: European conference on computer vision, Springer, pp 740–755 – reference: D’Avino D, Cozzolino D, Poggi G, Verdoliva L (2017) Autoencoder with recurrent neural networks for video forgery detection. Electron Imaging 2017(7):92–99 – reference: Medel JR (2016) Anomaly detection using predictive convolutional long short-term memory units. Thesis. Rochester Institute of Technology – reference: 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 733–742 – reference: Ramachandra B, Jones M, Vatsavai R (2020) Learning a distance function with a siamese network to localize anomalies in videos. In: The IEEE winter conference on applications of computer vision, pp 2598–2607 – reference: ShiYTianYWangYHuangTSequential deep trajectory descriptor for action recognition with three-stream cnnIEEE Trans Multimed20171971510152010.1109/TMM.2017.2666540 – reference: Chong YS, Tay YH (2017) Abnormal event detection in videos using spatiotemporal autoencoder. In: International symposium on neural networks, Springer, pp 189–196 – reference: 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 2720–2727 – reference: Del Giorno A, Bagnell JA, Hebert M (2016) A discriminative framework for anomaly detection in large videos. In: European conference on computer vision, Springer, pp 334–349 – reference: Srivastava N, Mansimov E, Salakhudinov R (2015) Unsupervised learning of video representations using lstms. 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| Title | Residual Spatiotemporal Autoencoder with Skip Connected and Memory Guided Network for Detecting Video Anomalies |
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