[1] CHANDOLA V, BANERJEE A, KUMAR V. Anomaly detection:A survey[J]. ACM Computing Surveys, 2009, 41(3):15.
[2] CHONG Y S, TAY Y H. Modeling representation of videos for anomaly detection using deep learning:A review[Z/OL]. arXiv:1505.00523, 2015.
[3] IONESCU R T, SMEUREANU S, POPESCU M, et al. Detecting abnormal events in video using narrowed normality clusters[C]//Proceedings of 2019 IEEE Winter Conference on Applications of Computer Vision. Hawaii, USA, 2019:1951-1960.
[4] LIU W, LUO W X, LIAN D Z, et al. Future frame prediction for anomaly detection:A new baseline[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018:6536-6545.
[5] POPOOLA O P, WANG K J. Video-based abnormal human behavior recognition:A review[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C:Applications and Reviews, 2012, 42(6):865-878.
[6] LI T, CHANG H, WANG M, et al. Crowded scene analysis:A survey[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2015, 25(3):367-386.
[7] AHMED S A, DOGRA D P, KAR S, et al. Trajectory-based surveillance analysis:A survey[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2019, 29(7):1985-1997.
[8] KIRAN B R, THOMAS D M, PARAKKAL R. An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos[J]. Journal of Imaging, 2018, 4(2):36.
[9] CHALAPATHY R, CHAWLA S. Deep learning for anomaly detection:A survey[Z/OL]. arXiv:1901.03407, 2019.
[10] CHENG K W, CHEN Y T, FANG W H. Video anomaly detection and localization using hierarchical feature representation and Gaussian process regression[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA, 2015:2909-2917.
[11] DEL GIORNO A, ANDREW BAGNELL J, HEBERT M. A discriminative framework for anomaly detection in large videos[C]//Proceedings of the 14th European Conference on Computer Vision. Amsterdam, Netherlands, 2016:334-349.
[12] IONESCU R T, SMEUREANU S, ALEXE B, et al. Unmasking the abnormal events in video[C]//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy, 2017:2914-2922.
[13] XU Y, OUYANG X, CHENG Y, et al. Dual-mode vehicle motion pattern learning for high performance road traffic anomaly detection[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City, USA, 2018:145-152.
[14] CAI Y F, WANG H, CHEN X B, et al. Trajectory-based anomalous behaviour detection for intelligent traffic surveillance[J]. IET Intelligent Transport Systems, 2015, 9(8):810-816.
[15] ZHOU X G, ZHANG L Q. Abnormal event detection using recurrent neural network[C]//Proceedings of 2015 International Conference on Computer Science and Applications. Wuhan, China, 2015:222-226.
[16] KIM J, GRAUMAN K. Observe locally, infer globally:A space-time MRF for detecting abnormal activities with incremental updates[C]//Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA, 2009:2921-2928.
[17] THIDA M, ENG H L, REMAGNINO P. Laplacian eigenmap with temporal constraints for local abnormality detection in crowded scenes[J]. IEEE Transactions on Cybernetics, 2013, 43(6):2147-2156.
[18] SINGH D, KRISHNA MOHAN C. Graph formulation of video activities for abnormal activity recognition[J]. Pattern Recognition, 2017, 65:265-272.
[19] XU J X, DENMAN S, SRIDHARAN S, et al. Dynamic texture reconstruction from sparse codes for unusual event detection in crowded scenes[C]//Proceedings of 2011 Joint ACM Workshop on Modeling and Representing Events. Scottsdale, USA, 2011:25-30.
[20] LEE D G, SUK H I, PARK S K, et al. Motion influence map for unusual human activity detection and localization in crowded scenes[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2015, 25(10):1612-1623.
[21] LEYVA R, SANCHEZ V, LI C T. Fast detection of abnormal events in videos with binary features[C]//Proceedings of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing. Calgary, Canada, 2018:1318-1322.
[22] 黄鑫, 肖世德, 宋波. 监控视频中的车辆异常行为检测[J]. 计算机系统应用, 2018, 27(2):125-131. HUANG X, XIAO S D, SONG B. Detection of vehicle's abnormal behaviors in surveillance video[J]. Computer Systems & Applications, 2018, 27(2):125-131. (in Chinese)
[23] YIN J, YANG Q, PAN J J. Sensor-based abnormal human-activity detection[J]. IEEE Transactions on Knowledge and Data Engineering, 2008, 20(8):1082-1090.
[24] SALIGRAMA V, CHEN Z. Video anomaly detection based on local statistical aggregates[C]//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA, 2012:2112-2119.
[25] HINAMI R, MEI T, SATOH S. Joint detection and recounting of abnormal events by learning deep generic knowledge[C]//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy, 2017:3639-3647.
[26] MEHRAN R, OYAMA A, SHAH M. Abnormal crowd behavior detection using social force model[C]//Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA, 2009:935-942.
[27] HASSNER T, ITCHER Y, KLIPER-GROSS O. Violent flows:Real-time detection of violent crowd behavior[C]//Proceedings of 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Providence, USA, 2012:1-6.
[28] MOHAMMADI S, PERINA A, KIANI H, et al. Angry crowds:Detecting violent events in videos[C]//Proceedings of the 14th European Conference on Computer Vision. Amsterdam, Netherlands, 2016:3-18.
[29] HU X, HUANG Y P, GAO X M, et al. Squirrel-cage local binary pattern and its application in video anomaly detection[J]. IEEE Transactions on Information Forensics and Security, 2019, 14(4):1007-1022.
[30] HU D H, ZHANG X X, YIN J, et al. Abnormal activity recognition based on HDP-HMM models[C]//Proceedings of the 21st International Jont Conference on Artifical Intelligence. Pasadena, USA:Morgan Kaufmann Publishers, 2009:1715-1720.
[31] YANG M Y, LIAO W T, CAO Y P, et al. Video event recognition and anomaly detection by combining Gaussian process and hierarchical Dirichlet process models[J]. Photogrammetric Engineering & Remote Sensing, 2018, 84(4):203-214.
[32] CANDÉS E, LI X D, MA Y, et al. Robust principal component analysis?:Recovering low-rank matrices from sparse errors[C]//Proceedings of 2010 IEEE Sensor Array and Multichannel Signal Processing Workshop. Jerusalem, Israel, 2010:201-204.
[33] XIONG L, CHEN X, SCHNEIDER J. Direct robust matrix factorizatoin for anomaly detection[C]//Proceedings of the 2011 IEEE 11th International Conference on Data Mining. Vancouver, Canada, 2011:844-853.
[34] DEBRUYNE M, VERDONCK T. Robust kernel principal component analysis and classification[J]. Advances in Data Analysis and Classification, 2010, 4(2-3):151-167.
[35] ZHU X B, LIU J, WANG J Q, et al. Sparse representation for robust abnormality detection in crowded scenes[J]. Pattern Recognition, 2014, 47(5):1791-1799.
[36] LUO W X, LIU W, GAO S H. A revisit of sparse coding based anomaly detection in stacked RNN framework[C]//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy, 2017:341-349.
[37] ZHAO B, LI F F, XING E P. Online detection of unusual events in videos via dynamic sparse coding[C]//Proceedings of CVPR 2011. Providence, USA, 2011:3313-3320.
[38] YUAN Y, FENG Y C, LU X Q. Structured dictionary learning for abnormal event detection in crowded scenes[J]. Pattern Recognition, 2018, 73:99-110.
[39] CONG Y, YUAN J S, LIU J. Abnormal event detection in crowded scenes using sparse representation[J]. Pattern Recognition, 2013, 46(7):1851-1864.
[40] CONG Y, YUAN J S, LIU J. Sparse reconstruction cost for abnormal event detection[C]//Proceedings of CVPR 2011. Providence, USA, 2011:3449-3456.
[41] LU C W, SHI J P, JIA J Y. Abnormal event detection at 150 FPS in MATLAB[C]//Proceedings of 2013 IEEE International Conference on Computer Vision. Sydney, Australia, 2013:2720-2727.
[42] REN H M, LIU W F, OLSEN S I, et al. Unsupervised behavior-specific dictionary learning for abnormal event detection[C]//Proceedings of 2015 British Machine Vision Conference. Swansea, UK:BMVA Press, 2015:28.1-28.13.
[43] MAHADEVAN V, LI W X, BHALODIA V, et al. Anomaly detection in crowded scenes[C]//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, USA, 2010:1975-1981.
[44] LI W X, MAHADEVAN V, VASCONCELOS N. Anomaly detection and localization in crowded scenes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(1):18-32.
[45] PRUTEANU-MALINICI I, CARIN L. Infinite hidden Markov models for unusual-event detection in video[J]. IEEE Transactions on Image Processing, 2008, 17(5):811-822.
[46] XIANG T, GONG S G. Incremental and adaptive abnormal behaviour detection[J]. Computer Vision and Image Understanding, 2008, 111(1):59-73.
[47] KRATZ L, KO N. Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models[C]//Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA, 2009:1446-1453.
[48] WANG B, YE M, LI X, et al. Abnormal crowd behavior detection using high-frequency and spatio-temporal features[J]. Machine Vision and Applications, 2012, 23(3):501-511.
[49] HOSPEDALES T, GONG S G, XIANG T. A Markov clustering topic model for mining behaviour in video[C]//Proceedings of the 2009 IEEE 12th International Conference on Computer Vision. Kyoto, Japan, 2009:1165-1172.
[50] SONG L, JIANG F, SHI Z K, et al. Toward dynamic scene understanding by hierarchical motion pattern mining[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(3):1273-1285.
[51] 李娟, 张冰怡, 冯志勇, 等. 基于隐马尔可夫模型的视频异常场景检测[J]. 计算机工程与科学, 2017, 39(7):1300-1308. LI J, ZHANG B Y, FENG Z Y, et al. Anomaly detection based on hidden Markov model in videos[J]. Computer Engineering and Science, 2017, 39(7):1300-1308. (in Chinese)
[52] QIAO M N, WANG T, LI J K, et al. Abnormal event detection based on deep autoencoder fusing optical flow[C]//Proceedings of the 2017 36th Chinese Control Conference. Dalian, China, 2017:11098-11103.
[53] TRAN H, HOGG D C. Anomaly detection using a convolutional winner-take-all autoencoder[C]//Proceedings of British Machine Vision Conference 2017. London, UK, 2017.
[54] XU D, RICCI E, YAN Y, et al. Learning deep representations of appearance and motion for anomalous event detection[Z/OL]. arXiv:1510.01553, 2015.
[55] XU D, YAN Y, RICCI E, et al. Detecting anomalous events in videos by learning deep representations of appearance and motion[J]. Computer Vision and Image Understanding, 2017, 156:117-127.
[56] SMEUREANU S, IONESCU R T, POPESCU M, et al. Deep appearance features for abnormal behavior detection in video[C]//Proceedings of the 19th International Conference on Image Analysis and Processing. Catania, Italy, 2017:779-789.
[57] NAZARE T S, DE MELLO R F, PONTI M A. Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?[Z/OL]. arXiv:1811.08495, 2018.
[58] SABOKROU M, FATHY M, HOSEINI M, et al. Real-time anomaly detection and localization in crowded scenes[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Boston, USA, 2015:56-62.
[59] SABOKROU M, FAYYAZ M, FATHY M, et al. Deep-cascade:Cascading 3D deep neural networks for fast anomaly detection and localization in crowded scenes[J]. IEEE Transactions on Image Processing, 2017, 26(4):1992-2004.
[60] SABOKROU M, FATHY M, MOAYED Z, et al. Fast and accurate detection and localization of abnormal behavior in crowded scenes[J]. Machine Vision and Applications, 2017, 28(8):965-985.
[61] SABOKROU M, FAYYAZ M, FATHY M, et al. Deep-anomaly:Fully convolutional neural network for fast anomaly detection in crowded scenes[J]. Computer Vision and Image Understanding, 2018, 172:88-97.
[62] FENG Y C, YUAN Y, LU X Q. Learning deep event models for crowd anomaly detection[J]. Neurocomputing, 2017, 219:548-556.
[63] SUN J Y, WANG X Z, XIONG N X, et al. Learning sparse representation with variational auto-encoder for anomaly detection[J]. IEEE Access, 2018, 6:33353-33361.
[64] CHU W Q, XUE H Y, YAO C W, et al. Sparse coding guided spatiotemporal feature learning for abnormal event detection in large videos[J]. IEEE Transactions on Multimedia, 2019, 21(1):246-255.
[65] 胡正平, 张乐, 尹艳华. 时空深度特征AP聚类的稀疏表示视频异常检测算法[J]. 信号处理, 2019, 35(3):386-395. HU Z P, ZHANG L, YIN Y H. Video anomaly detection by AP clustering sparse representation based on spatial-temporal deep feature model[J]. Journal of Signal Processing, 2019, 35(3):386-395. (in Chinese)
[66] FENG J, ZHANG C, HAO P W. Online learning with self-organizing maps for anomaly detection in crowd scenes[C]//Proceedings of the 2010 20th International Conference on Pattern Recognition. Istanbul, Turkey, 2010:3599-3602.
[67] SUN Q R, LIU H, HARADA T. Online growing neural gas for anomaly detection in changing surveillance scenes[J]. Pattern Recognition, 2017, 64:187-201.
[68] FAN Y X, WEN G J, LI D R, et al. Video anomaly detection and localization via Gaussian mixture fully convolutional variational autoencoder[Z/OL]. arXiv:1805.11223, 2018.
[69] CHALAPATHY R, MENON A K, CHAWLA S. Anomaly detection using one-class neural networks[Z/OL]. arXiv:1802.06360, 2018.
[70] HASAN M, CHOI J, NEUMANN J, et al. Learning temporal regularity in video sequences[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016:733-742.
[71] SABOKROU M, FATHY M, HOSEINI M. Video anomaly detection and localisation based on the sparsity and reconstruction error of auto-encoder[J]. Electronics Letters, 2016, 52(13):1122-1124.
[72] MUNAWAR A, VINAYAVEKHIN P, DE MAGISTRIS G. Limiting the reconstruction capability of generative neural network using negative learning[C]//Proceedings of the 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing. Tokyo, Japan, 2017:1-6.
[73] CHALAPATHY R, MENON A K, CHAWLA S. Robust, deep and inductive anomaly detection[C]//Proceedings of 2017 Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Skopje, Macedonia, 2017:36-51.
[74] NOGAS J, KHAN S S, MIHAILIDIS A. DeepFall:Non-invasive fall detection with deep spatio-temporal convolutional autoencoders[Z/OL]. arXiv:1809.00977, 2018.
[75] ZHAO Y R, DENG B, SHEN C, et al. Spatio-temporal autoencoder for video anomaly detection[C]//Proceedings of the 25th ACM International Conference on Multimedia. Mountain View, USA, 2017:1933-1941.
[76] WANG T, QIAO M N, LIN Z W, et al. Generative neural networks for anomaly detection in crowded scenes[J]. IEEE Transactions on Information Forensics and Security, 2019, 14(5):1390-1399.
[77] RAVANBAKHSH M, NABI M, SANGINETO E, et al. Abnormal event detection in videos using generative adversarial nets[C]//Proceedings of 2017 IEEE International Conference on Image Processing. Beijing, China, 2017:1577-1581.
[78] RAVANBAKHSH M, SANGINETO E, NABI M, et al. Training adversarial discriminators for cross-channel abnormal event detection in crowds[C]//Proceedings of 2019 IEEE Winter Conference on Applications of Computer Vision. Waikoloa Village, USA, 2019:1896-1904.
[79] SABOKROU M, KHALOOEI M, FATHY M, et al. Adversarially learned one-class classifier for novelty detection[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018:3379-3388.
[80] SABOKROU M, POURREZA M, FAYYAZ M, et al. AVID:Adversarial visual irregularity detection[Z/OL]. arXiv:1805.09521, 2018.
[81] ZENATI H, FOO C S, LECOUAT B, et al. Efficient GAN-based anomaly detection[Z/OL]. arXiv:1802.06222, 2018.
[82] AKCAY S, ATAPOUR-ABARGHOUEI A, BRECKON T P. GANomaly:Semi-supervised anomaly detection via adversarial training[Z/OL]. arXiv:1805.06725, 2018.
[83] SCHLEGL T, SEEBÖCK P, WALDSTEIN S M, et al. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery[C]//Proceedings of the 25th International Conference on Information Processing in Medical Imaging. Boone, USA, 2017:146-147.
[84] VU H, NGUYEN T D, TRAVERS A, et al. Energy-based localized anomaly detection in video surveillance[C]//Proceedings of the 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining. Jeju, South Korea, 2017:641-653.
[85] VU H, NGUYEN T D, LE T, et al. Robust anomaly detection in videos using multilevel representations[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1):5216-5223.
[86] DONAHUE J, KRÄHENBÜHL P, DARRELL T. Adversarial feature learning[Z/OL]. arXiv:1605.09782, 2016.
[87] VILLEGAS R, YANG J M, HONG S, et al. Decomposing motion and content for natural video sequence prediction[Z/OL]. arXiv:1706.08033, 2017.
[88] LEE S, KIM H G, RO Y M. STAN:Spatio-temporal adversarial networks for abnormal event detection[C]//Proceedings of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing. Calgary, Canada, 2018:1323-1327.
[89] D'AVINO D, COZZOLINO D, POGGI G, et al. Autoencoder with recurrent neural networks for video forgery detection[J]. Electronic Imaging, 2017, 2017(7):92-99.
[90] CHONG Y S, TAY Y H. Abnormal event detection in videos using spatiotemporal autoencoder[C]//Proceedings of the 14th International Symposium on Neural Networks. Hokkaido, Japan, 2017:189-196.
[91] MEDEL J R, SAVAKIS A. Anomaly detection in video using predictive convolutional long short-term memory networks[Z/OL]. arXiv:1612.00390, 2016.
[92] LUO W X, LIU W, GAO S H. Remembering history with convolutional LSTM for anomaly detection[C]//Proceedings of 2017 IEEE International Conference on Multimedia and Expo. Hong Kong, China, 2017:439-444.
[93] SULTANI W, CHEN C, SHAH M. Real-world anomaly detection in surveillance videos[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018:6479-6488.
[94] ADAM A, RIVLIN E, SHIMSHONI I, et al. Robust real-time unusual event detection using multiple fixed-location monitors[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(3):555-560.
[95] LI N N, WU X Y, XU D, et al. Spatio-temporal context analysis within video volumes for anomalous-event detection and localization[J]. Neurocomputing, 2015, 155:309-319.
[96] RAVANBAKHSH M, NABI M, MOUSAVI H, et al. Plug-and-play CNN for crowd motion analysis:An application in abnormal event detection[C]//Proceedings of 2018 IEEE Winter Conference on Applications of Computer Vision. Lake Tahoe, USA, 2018:1689-1698.
