17.1.2.3.2 Learning for Detecting Anomalies

Chapter Contents (Back)
Anomaly Detection. Abnormal Event. Learning. General learning techniques. Deep learning in:
See also Deep Learning for Detecting Anomalies.

Han, S.J.[Sang-Jun], Cho, S.B.[Sung-Bae],
Evolutionary neural networks for anomaly detection based on the behavior of a program,
SMC-B(36), No. 3, June 2006, pp. 559-570.
IEEE DOI 0606
Really other kinds of anomalies. BibRef

Markou, M.[Markos], Singh, S.[Sameer],
A Neural Network-Based Novelty Detector for Image Sequence Analysis,
PAMI(28), No. 10, October 2006, pp. 1664-1677.
IEEE DOI 0609
BibRef

Xiang, T.[Tao], Gong, S.G.[Shao-Gang],
Video Behavior Profiling for Anomaly Detection,
PAMI(30), No. 5, May 2008, pp. 893-908.
IEEE DOI 0803
BibRef
Earlier:
Optimal Dynamic Graphs for Video Content Analysis,
BMVC06(I:177).
PDF File. 0609
BibRef
Earlier:
Online Video Behaviour Abnormality Detection Using Reliability Measure,
BMVC05(xx-yy).
HTML Version. 0509
BibRef
Earlier:
Activity Based Video Content Trajectory Representation and Segmentation,
BMVC04(xx-yy).
HTML Version. 0508
group behaviors through learning. Find anomalies. BibRef

Loy, C.C.[Chen Change], Xiang, T.[Tao], Gong, S.G.[Shao-Gang],
Incremental Activity Modeling in Multiple Disjoint Cameras,
PAMI(34), No. 9, September 2012, pp. 1799-1813.
IEEE DOI 1208
BibRef
And:
Stream-Based Active Unusual Event Detection,
ACCV10(I: 161-175).
Springer DOI 1011
BibRef

Loy, C.C.[Chen Change], Hospedales, T.M.[Timothy M.], Xiang, T.[Tao], Gong, S.G.[Shao-Gang],
Stream-based joint exploration-exploitation active learning,
CVPR12(1560-1567).
IEEE DOI 1208
BibRef

Li, J.[Jian], Gong, S.G.[Shao-Gang], Xiang, T.[Tao],
Learning Behavioural Context,
IJCV(97), No. 3, May 2012, pp. 276-304.
WWW Link. 1203
BibRef
Earlier:
Global Behaviour Inference using Probabilistic Latent Semantic Analysis,
BMVC08(xx-yy).
PDF File. 0809
Complex behavior recogniton and anomaly detection. Behavior spatiao, correlation, temporal context.
See also Quantifying and Transferring Contextual Information in Object Detection. BibRef

Xiang, T.[Tao], Gong, S.G.[Shao-Gang],
Model Selection for Unsupervised Learning of Visual Context,
IJCV(69), No. 2, August 2006, pp. 181-201.
Springer DOI 0606
Choosing the model for learning. Bayesian Information Criterion. (small data sets) Completed Likelihood Akaike's Information Criterion. (otherwise)
See also Optimising dynamic graphical models for video content analysis. BibRef

Xiang, T.[Tao], Gong, S.G.[Shao-Gang],
Incremental and adaptive abnormal behaviour detection,
CVIU(111), No. 1, July 2008, pp. 59-73.
Elsevier DOI 0711
Behaviour analysis and recognition; Visual surveillance; Abnormality detection; Incremental learning; Likelihood ratio test; Dynamic scene modelling; Dynamic Bayesian networks BibRef

Xiang, T.[Tao], Gong, S.G.[Shao-Gang], Parkinson, D.,
Autonomous Visual Events Detection and Classification without Explicit Object-Centred Segmentation and Tracking,
BMVC02(Poster Session). 0208
BibRef

Hospedales, T.M.[Timothy M.], Li, J.[Jian], Gong, S.G.[Shao-Gang], Xiang, T.[Tao],
Identifying Rare and Subtle Behaviors: A Weakly Supervised Joint Topic Model,
PAMI(33), No. 12, December 2011, pp. 2451-2464.
IEEE DOI 1110
Identify rare event, e.g. dangerous or illegal activities have few prior examples. BibRef

Bregonzio, M.[Matteo], Li, J.[Jian], Gong, S.G.[Shao-Gang], Xiang, T.[Tao],
Discriminative Topics Modelling for Action Feature Selection and Recognition,
BMVC10(xx-yy).
HTML Version. 1009
BibRef

Bregonzio, M.[Matteo], Gong, S.G.[Shao-Gang], Xiang, T.[Tao],
Recognising action as clouds of space-time interest points,
CVPR09(1948-1955).
IEEE DOI 0906
BibRef

Fu, Y.W.[Yan-Wei], Hospedales, T.M.[Timothy M.], Xiang, T.[Tao], Gong, S.G.[Shao-Gang],
Learning Multimodal Latent Attributes,
PAMI(36), No. 2, February 2014, pp. 303-316.
IEEE DOI 1402
BibRef
Earlier:
Attribute Learning for Understanding Unstructured Social Activity,
ECCV12(IV: 530-543).
Springer DOI 1210
learning (artificial intelligence)
See also Unsupervised Domain Adaptation for Zero-Shot Learning.
See also Transductive Multi-label Zero-shot Learning. BibRef

Hospedales, T.M.[Timothy M.], Gong, S.G.[Shao-Gang], Xiang, T.[Tao],
A Unifying Theory of Active Discovery and Learning,
ECCV12(V: 453-466).
Springer DOI 1210
BibRef

Li, J.[Jian], Hospedales, T.M.[Timothy M.], Gong, S.G.[Shao-Gang], Xiang, T.[Tao],
Learning Rare Behaviours,
ACCV10(II: 293-307).
Springer DOI 1011
BibRef

Xu, X.[Xun], Hospedales, T.M.[Timothy M.], Gong, S.G.[Shao-Gang],
Transductive Zero-Shot Action Recognition by Word-Vector Embedding,
IJCV(123), No. 3, July 2017, pp. 309-333.
Springer DOI 1706
BibRef
Earlier:
Semantic embedding space for zero-shot action recognition,
ICIP15(63-67)
IEEE DOI 1512
action recognition; zero-shot learning BibRef

Jager, M., Knoll, C., Hamprecht, F.A.,
Weakly Supervised Learning of a Classifier for Unusual Event Detection,
IP(17), No. 9, September 2008, pp. 1700-1708.
IEEE DOI 0810
BibRef

Ouivirach, K.[Kan], Gharti, S.[Shashi], Dailey, M.N.[Matthew N.],
Incremental behavior modeling and suspicious activity detection,
PR(46), No. 3, March 2013, pp. 671-680.
Elsevier DOI 1212
Hidden Markov models; Incremental learning; Behavior clustering; Sufficient statistics; Anomaly detection; Bootstrapping BibRef

Roshtkhari, M.J.[Mehrsan Javan], Levine, M.D.[Martin D.],
An on-line, real-time learning method for detecting anomalies in videos using spatio-temporal compositions,
CVIU(117), No. 10, 2013, pp. 1436-1452.
Elsevier DOI 1309
BibRef
And:
Online Dominant and Anomalous Behavior Detection in Videos,
CVPR13(2611-2618)
IEEE DOI 1309
BibRef
Earlier:
A Multi-Scale Hierarchical Codebook Method for Human Action Recognition in Videos Using a Single Example,
CRV12(182-189).
IEEE DOI 1207
Video surveillance Anomaly detection BibRef

Roshtkhari, M.J.[Mehrsan Javan], Levine, M.D.[Martin D.],
Multiple Object Tracking Using Local Motion Patterns,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Roshtkhari, M.J.[Mehrsan Javan], Levine, M.D.[Martin D.],
Human activity recognition in videos using a single example,
IVC(31), No. 11, 2013, pp. 864-876.
Elsevier DOI 1311
Action recognition BibRef

Ren, W.Y.[Wei-Ya], Li, G.H.[Guo-Hui], Sun, B.L.[Bo-Liang], Huang, K.H.[Kui-Hua],
Unsupervised kernel learning for abnormal events detection,
VC(31), No. 3, March 2015, pp. 245-255.
WWW Link. 1503
BibRef

Xiao, T., Zhang, C., Zha, H.,
Learning to Detect Anomalies in Surveillance Video,
SPLetters(22), No. 9, September 2015, pp. 1477-1481.
IEEE DOI 1503
Context modeling BibRef

Zhang, Z., Mei, X., Xiao, B.,
Abnormal Event Detection via Compact Low-Rank Sparse Learning,
IEEE_Int_Sys(31), No. 2, March 2016, pp. 29-36.
IEEE DOI 1604
Event detection BibRef

Sun, Q.[Qianru], Liu, H.[Hong], Harada, T.[Tatsuya],
Online growing neural GAS for anomaly detection in changing surveillance scenes,
PR(64), No. 1, 2017, pp. 187-201.
Elsevier DOI 1701
Anomaly detection BibRef

Yu, J.M.[Jong-Min], Yow, K.C.[Kin Choong], Jeon, M.[Moongu],
Joint representation learning of appearance and motion for abnormal event detection,
MVA(29), No. 7, October 2018, pp. 1157-1170.
WWW Link. 1810
BibRef

Chu, W., Xue, H., Yao, C., Cai, D.,
Sparse Coding Guided Spatiotemporal Feature Learning for Abnormal Event Detection in Large Videos,
MultMed(21), No. 1, January 2019, pp. 246-255.
IEEE DOI 1901
Feature extraction, Videos, Spatiotemporal phenomena, Event detection, Encoding, Anomaly detection, Task analysis, anomaly detection BibRef

George, M.[Michael], Jose, B.R.[Babita Roslind], Mathew, J.[Jimson], Kokare, P.[Pranjali],
Autoencoder-based abnormal activity detection using parallelepiped spatio-temporal region,
IET-CV(13), No. 1, February 2019, pp. 23-30.
DOI Link 1902
BibRef

dos Santos, F.P.[Fernando P.], Ribeiro, L.S.F.[Leonardo S.F.], Ponti, M.A.[Moacir A.],
Generalization of feature embeddings transferred from different video anomaly detection domains,
JVCIR(60), 2019, pp. 407-416.
Elsevier DOI 1903
Video, Transfer learning, Feature generalization, Anomaly detection BibRef

Barz, B.[Björn], Rodner, E.[Erik], Garcia, Y.G.[Yanira Guanche], Denzler, J.[Joachim],
Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection,
PAMI(41), No. 5, May 2019, pp. 1088-1101.
IEEE DOI 1904
Fraud, climate, healthcare monitoring. Anomaly detection, Data models, Meteorology, Task analysis, Tensile stress, Tools, Medical services, Anomaly detection, unsupervised machine learning BibRef

Torres, D.M.[Duber Martinez], Correa, H.L.[Humberto Loaiza], Bravo, E.C.[Eduardo Caicedo],
Online learning of contexts for detecting suspicious behaviors in surveillance videos,
IVC(89), 2019, pp. 197-210.
Elsevier DOI 1909
Incremental learning, Online learning, Context, Suspicious behavior, Surveillance BibRef

Dotti, D.[Dario], Popa, M.[Mirela], Asteriadis, S.[Stylianos],
A hierarchical autoencoder learning model for path prediction and abnormality detection,
PRL(130), 2020, pp. 216-224.
Elsevier DOI 2002
Motion features, Autoencoder, Hierarchical learning, Behavior understanding, Abnormality detection, Path prediction BibRef

Yan, M.J.[Meng-Jia], Meng, J.J.[Jing-Jing], Zhou, C.[Chunluan], Tu, Z.G.[Zhi-Gang], Tan, Y.P.[Yap-Peng], Yuan, J.S.[Jun-Song],
Detecting spatiotemporal irregularities in videos via a 3D convolutional autoencoder,
JVCIR(67), 2020, pp. 102747.
Elsevier DOI 2004
Spatiotemporal irregularity detection, Autoencoder, 3D convolution, Anomaly detection, Unsupervised learning, Real-time BibRef

Song, H., Sun, C., Wu, X., Chen, M., Jia, Y.,
Learning Normal Patterns via Adversarial Attention-Based Autoencoder for Abnormal Event Detection in Videos,
MultMed(22), No. 8, August 2020, pp. 2138-2148.
IEEE DOI 2007
Videos, Decoding, Event detection, Generative adversarial networks, Image reconstruction, generative adversarial network BibRef

Alfeo, A.L.[Antonio L.], Cimino, M.G.C.A.[Mario G.C.A.], Manco, G.[Giuseppe], Ritacco, E.[Ettore], Vaglini, G.[Gigliola],
Using an autoencoder in the design of an anomaly detector for smart manufacturing,
PRL(136), 2020, pp. 272-278.
Elsevier DOI 2008
Fault detection, Anomaly detection, Smart manufacturing, Smart industry, Interpretable machine learning, Autoencoder, Anomaly discriminator BibRef

Zaheer, M.Z.[Muhammad Zaigham], Mahmood, A.[Arif], Shin, H.[Hochul], Lee, S.I.[Seung-Ik],
A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels,
SPLetters(27), 2020, pp. 1705-1709.
IEEE DOI 1806
Videos, Training, Feature extraction, Anomaly detection, Event detection, Noise measurement, Surveillance, weakly supervised learning BibRef

Fan, Y.X.[Ya-Xiang], Wen, G.J.[Gong-Jian], Li, D.R.[De-Ren], Qiu, S.H.[Shao-Hua], Levine, M.D.[Martin D.], Xiao, F.[Fei],
Video anomaly detection and localization via Gaussian Mixture Fully Convolutional Variational Autoencoder,
CVIU(195), 2020, pp. 102920.
Elsevier DOI 2005
Anomaly detection, Video surveillance, Variational autoencoder, Gaussian mixture model, Dynamic flow, Two-stream network BibRef

Wu, P.[Peng], Liu, J.[Jing],
Learning Causal Temporal Relation and Feature Discrimination for Anomaly Detection,
IP(30), 2021, pp. 3513-3527.
IEEE DOI 2103
Anomaly detection, Feature extraction, Convolution, Training, Task analysis, Dispersion, Benchmark testing, Anomaly detection, weak supervision BibRef

Degardin, B.[Bruno], Proença, H.[Hugo],
Iterative weak/self-supervised classification framework for abnormal events detection,
PRL(145), 2021, pp. 50-57.
Elsevier DOI 2104
Visual surveillance, Abnormal events detection, Weakly supervised learning BibRef

Degardin, B.[Bruno], Lopes, V.[Vasco], Proença, H.[Hugo],
ATOM: Self-supervised human action recognition using atomic motion representation learning,
IVC(137), 2023, pp. 104750.
Elsevier DOI 2309
Atomic dynamics, Self-supervised learning, Graph convolutional networks, Human pose, Human behavior understanding BibRef

Wang, J.Z.[Jian-Zhu], Huang, W.[Wei], Wang, S.C.[Sheng-Chun], Dai, P.[Peng], Li, Q.Y.[Qing-Yong],
LRGAN: Visual anomaly detection using GAN with locality-preferred recoding,
JVCIR(79), 2021, pp. 103201.
Elsevier DOI 2109
Visual anomaly detection, GAN, Locality, Recoding BibRef

Hao, Y.[Yi], Li, J.[Jie], Wang, N.N.[Nan-Nan], Wang, X.Y.[Xiao-Yu], Gao, X.B.[Xin-Bo],
Spatiotemporal consistency-enhanced network for video anomaly detection,
PR(121), 2022, pp. 108232.
Elsevier DOI 2109
Anomaly detection, Unsupervised learning, Spatiotemporal consistency BibRef

Sun, C.[Che], Jia, Y.D.[Yun-De], Song, H.[Hao], Wu, Y.W.[Yu-Wei],
Adversarial 3D Convolutional Auto-Encoder for Abnormal Event Detection in Videos,
MultMed(23), 2021, pp. 3292-3305.
IEEE DOI 2109
Videos, Event detection, Noise reduction, Correlation, Decoding, Generators, abnormal event detection BibRef

Yu, S.H.[Sheng-Hao], Wang, C.[Chong], Mao, Q.M.[Qiao-Mei], Li, Y.Q.[Yu-Qi], Wu, J.F.[Jia-Fei],
Cross-Epoch Learning for Weakly Supervised Anomaly Detection in Surveillance Videos,
SPLetters(28), 2021, pp. 2137-2141.
IEEE DOI 2112
Videos, Feature extraction, Training, Anomaly detection, Task analysis, Surveillance, video understanding BibRef

Gong, Y.L.[Yi-Ling], Luo, S.[Sihui], Wang, C.[Chong], Zheng, Y.J.[Yu-Jie],
Feature Differentiation Reconstruction Network for Weakly-Supervised Video Anomaly Detection,
SPLetters(30), 2023, pp. 1462-1466.
IEEE DOI 2310
BibRef

Chen, D.Y.[Dong-Yue], Yue, L.Y.[Ling-Yi], Chang, X.Y.[Xing-Ya], Xu, M.[Ming], Jia, T.[Tong],
NM-GAN: Noise-modulated generative adversarial network for video anomaly detection,
PR(116), 2021, pp. 107969.
Elsevier DOI 2106
Video anomaly detection, Generative adversarial network, Noise modulation, Reconstruction error map, Generalization ability BibRef

Li, B.[Bo], Leroux, S.[Sam], Simoens, P.[Pieter],
Decoupled appearance and motion learning for efficient anomaly detection in surveillance video,
CVIU(210), 2021, pp. 103249.
Elsevier DOI 2109
Anomaly detection, Surveillance video, Unsupervised learning BibRef

Zhang, Y.[Yu], Nie, X.S.[Xiu-Shan], He, R.D.[Run-Dong], Chen, M.[Meng], Yin, Y.L.[Yi-Long],
Normality Learning in Multispace for Video Anomaly Detection,
CirSysVideo(31), No. 9, September 2021, pp. 3694-3706.
IEEE DOI 2109
Anomaly detection, Training, Generative adversarial networks, Semantics, Predictive models, Neural networks, generative adversarial network BibRef

Wang, Z.G.[Zhi-Guo], Zhang, Y.J.[Yu-Jin], Wang, G.J.[Gui-Jin], Xie, P.W.[Peng-Wei],
Main-Auxiliary Aggregation Strategy for Video Anomaly Detection,
SPLetters(28), 2021, pp. 1794-1798.
IEEE DOI 2109
Detectors, Germanium, Anomaly detection, Focusing, Aggregates, Training, Fuses, Anomaly detection, aggregation, ensemble learning, main-auxiliary BibRef

Du, S.Y.[Shuai-Yuan], Hong, C.Y.[Chao-Yi], Chen, Y.P.[Yin-Peng], Cao, Z.G.[Zhi-Guo], Zhang, Z.M.[Zi-Ming],
Class-attribute inconsistency learning for novelty detection,
PR(126), 2022, pp. 108582.
Elsevier DOI 2204
Novelty detection, Class-attribute inconsistency, Class-level similarity, Attribute-level similarity BibRef

Shao, W.[Wen], Kawakami, R.[Rei], Naemura, T.[Takeshi],
Anomaly Detection Using Spatio-Temporal Context Learned by Video Clip Sorting,
IEICE(E105-D), No. 5, May 2022, pp. 1094-1102.
WWW Link. 2205
BibRef

Li, D.H.[Dao-Heng], Nie, X.S.[Xiu-Shan], Li, X.F.[Xiao-Feng], Zhang, Y.[Yu], Yin, Y.L.[Yi-Long],
Context-related video anomaly detection via generative adversarial network,
PRL(156), 2022, pp. 183-189.
Elsevier DOI 2205
Video anomaly detection, Context, Bidirectional prediction, Generative adversarial network BibRef

Fan, Z.[Zheyi], Yi, S.H.[Shu-Han], Wu, D.[Di], Song, Y.[Yu], Cui, M.J.[Meng-Jie], Liu, Z.W.[Zhi-Wen],
Video anomaly detection using CycleGan based on skeleton features,
JVCIR(85), 2022, pp. 103508.
Elsevier DOI 2205
Anomaly detection, Cycle-consistent adversarial networks, Pose estimation, Dynamic skeleton feature, Reconstruction error BibRef

Navarro, J.[Jorge], Martín de Diego, I.[Isaac], Fernández, R.R.[Rubén R.], Moguerza, J.M.[Javier M.],
Triangle-based outlier detection,
PRL(156), 2022, pp. 152-159.
Elsevier DOI 2205
Unsupervised learning, Anomaly detection, Information fusion, Dissimilarity matrices, Triangle inequality BibRef

Zhang, Z.[Zhi], Zhong, S.H.[Sheng-Hua], Fares, A.[Ahmed], Liu, Y.[Yan],
Detecting abnormality with separated foreground and background: Mutual Generative Adversarial Networks for video abnormal event detection,
CVIU(219), 2022, pp. 103416.
Elsevier DOI 2205
Foreground-background separation mutual generative adversarial network, Video-level abnormal event detection BibRef

Avola, D.[Danilo], Cannistraci, I.[Irene], Cascio, M.[Marco], Cinque, L.[Luigi], Diko, A.[Anxhelo], Fagioli, A.[Alessio], Foresti, G.L.[Gian Luca], Lanzino, R.[Romeo], Mancini, M.[Maurizio], Mecca, A.[Alessio], Pannone, D.[Daniele],
A Novel GAN-Based Anomaly Detection and Localization Method for Aerial Video Surveillance at Low Altitude,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Yi, S.H.[Shu-Han], Fan, Z.[Zheyi], Wu, D.[Di],
Batch feature standardization network with triplet loss for weakly-supervised video anomaly detection,
IVC(120), 2022, pp. 104397.
Elsevier DOI 2204
Video anomaly detection, Batch feature standardization, Triplet loss, Feature processing BibRef

Slavic, G.[Giulia], Baydoun, M.[Mohamad], Campo, D.[Damian], Marcenaro, L.[Lucio], Regazzoni, C.[Carlo],
Multilevel Anomaly Detection Through Variational Autoencoders and Bayesian Models for Self-Aware Embodied Agents,
MultMed(24), 2022, pp. 1399-1414.
IEEE DOI 2204
Feature extraction, Anomaly detection, Predictive models, Image reconstruction, Self-aware, Probabilistic logic, Data models, variational autoencoder BibRef

Zhou, D.[Di], Chen, W.G.[Wei-Gang], Guo, C.S.[Chun-Sheng], Zhang, M.[Mark],
Batch quadratic programming network with maximum entropy constraint for anomaly detection,
IET-CV(16), No. 3, 2022, pp. 230-240.
DOI Link 2204
information theory, pattern clustering, quadratic programming BibRef

Maziarka, L.[Lukasz], Smieja, M.[Marek], Sendera, M.[Marcin], Struski, L.[Lukasz], Tabor, J.[Jacek], Spurek, P.[Przemyslaw],
OneFlow: One-Class Flow for Anomaly Detection Based on a Minimal Volume Region,
PAMI(44), No. 11, November 2022, pp. 8508-8519.
IEEE DOI 2210
Anomaly detection, Support vector machines, Solid modeling, Neural networks, Data models, Training, Anomaly detection, normalizing flows BibRef

Georgescu, M.I.[Mariana Iuliana], Ionescu, R.T.[Radu Tudor], Khan, F.S.[Fahad Shahbaz], Popescu, M.[Marius], Shah, M.[Mubarak],
A Background-Agnostic Framework With Adversarial Training for Abnormal Event Detection in Video,
PAMI(44), No. 9, September 2022, pp. 4505-4523.
IEEE DOI 2208
Training, Event detection, Image reconstruction, Anomaly detection, Feature extraction, Public transportation, Detectors, security and surveillance BibRef

Barbalau, A.[Antonio], Ionescu, R.T.[Radu Tudor], Georgescu, M.I.[Mariana-Iuliana], Dueholm, J.[Jacob], Ramachandra, B.[Bharathkumar], Nasrollahi, K.[Kamal], Khan, F.S.[Fahad Shahbaz], Moeslund, T.B.[Thomas B.], Shah, M.[Mubarak],
SSMTL++: Revisiting self-supervised multi-task learning for video anomaly detection,
CVIU(229), 2023, pp. 103656.
Elsevier DOI 2303
Anomaly detection, Self-supervised learning, Multi-task learning, Neural networks, Transformers BibRef

Georgescu, M.I.[Mariana-Iuliana], Barbalau, A.[Antonio], Ionescu, R.T.[Radu Tudor], Khan, F.S.[Fahad Shahbaz], Popescu, M.[Marius], Shah, M.[Mubarak],
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning,
CVPR21(12737-12747)
IEEE DOI 2111
Training, Event detection, Benchmark testing, Predictive models BibRef

Luo, W.X.[Wei-Xin], Liu, W.[Wen], Lian, D.Z.[Dong-Ze], Gao, S.H.[Sheng-Hua],
Future Frame Prediction Network for Video Anomaly Detection,
PAMI(44), No. 11, November 2022, pp. 7505-7520.
IEEE DOI 2210
BibRef
Earlier: A2, A1, A3, A4:
Future Frame Prediction for Anomaly Detection - A New Baseline,
CVPR18(6536-6545)
IEEE DOI 1812
Optical losses, Adaptation models, Visualization, Sensitivity, Uncertainty, Toy manufacturing industry, Training data, meta learning. Videos, Anomaly detection, Image reconstruction, Feature extraction, Optical imaging, Training BibRef

Wei, D.L.[Dong-Lai], Liu, Y.[Yang], Zhu, X.G.[Xiao-Guang], Liu, J.[Jing], Zeng, X.H.[Xin-Hua],
MSAF: Multimodal Supervise-Attention Enhanced Fusion for Video Anomaly Detection,
SPLetters(29), 2022, pp. 2178-2182.
IEEE DOI 2212
Feature extraction, Anomaly detection, Benchmark testing, Training, Task analysis, Visualization, Surveillance, weakly supervised learning BibRef

Zhang, C.[Chen], Li, G.R.[Guo-Rong], Xu, Q.Q.[Qian-Qian], Zhang, X.F.[Xin-Feng], Su, L.[Li], Huang, Q.M.[Qing-Ming],
Weakly Supervised Anomaly Detection in Videos Considering the Openness of Events,
ITS(23), No. 11, November 2022, pp. 21687-21699.
IEEE DOI 2212
Anomaly detection, Videos, Open data, Data models, Training, Feature extraction, Predictive models, Anomaly detection, meta-learning BibRef

Zhong, Y.H.[Yuan-Hong], Chen, X.[Xia], Hu, Y.T.[Yong-Ting], Tang, P.L.[Pan-Liang], Ren, F.[Fan],
Bidirectional Spatio-Temporal Feature Learning With Multiscale Evaluation for Video Anomaly Detection,
CirSysVideo(32), No. 12, December 2022, pp. 8285-8296.
IEEE DOI 2212
Anomaly detection, Feature extraction, Representation learning, Deep learning, Video sequences, Spatial temporal resolution, video anomaly detection BibRef

Liu, Y.[Yang], Guo, Z.L.[Zheng-Liang], Liu, J.[Jing], Li, C.F.[Cheng-Fang], Song, L.[Liang],
OSIN: Object-Centric Scene Inference Network for Unsupervised Video Anomaly Detection,
SPLetters(30), 2023, pp. 359-363.
IEEE DOI 2305
Feature extraction, Task analysis, Prototypes, Predictive models, Detectors, Computational modeling, Anomaly detection, video anomaly detection BibRef

Li, G.[Gang], He, P.[Ping], Li, H.[Huibin], Zhang, F.[Fan],
Adversarial composite prediction of normal video dynamics for anomaly detection,
CVIU(232), 2023, pp. 103686.
Elsevier DOI 2305
Unsupervised video anomaly detection, Normal video dynamics, Adversarial composite prediction, Foreground motion metric learning BibRef

He, P.[Ping], Zhang, F.[Fan], Li, G.[Gang], Li, H.[Huibin],
Adversarial and focused training of abnormal videos for weakly-supervised anomaly detection,
PR(147), 2024, pp. 110119.
Elsevier DOI Code:
WWW Link. 2312
Inter-video data imbalance problem, Weakly-supervised video anomaly detection, Focused training BibRef

Zhou, Q.H.[Qi-Hang], He, S.B.[Shi-Bo], Liu, H.Y.[Hao-Yu], Chen, T.[Tao], Chen, J.M.[Ji-Ming],
Pull & Push: Leveraging Differential Knowledge Distillation for Efficient Unsupervised Anomaly Detection and Localization,
CirSysVideo(33), No. 5, May 2023, pp. 2176-2189.
IEEE DOI 2305
Anomaly detection, Feature extraction, Training, Image reconstruction, Artificial neural networks, Task analysis, anomaly segmentation BibRef

Amin, J.[Javaria], Anjum, M.A.[Muhammad Almas], Ibrar, K.[Kainat], Sharif, M.[Muhammad], Kadry, S.[Seifedine], Crespo, R.G.[Ruben González],
Detection of anomaly in surveillance videos using quantum convolutional neural networks,
IVC(135), 2023, pp. 104710.
Elsevier DOI 2306
Anomalous, UNI-crime, Videos, Surveillance, Robbery, Quantum BibRef

Madan, N.[Neelu], Ristea, N.C.[Nicolae-Catalin], Ionescu, R.T.[Radu Tudor], Nasrollahi, K.[Kamal], Khan, F.S.[Fahad Shahbaz], Moeslund, T.B.[Thomas B.], Shah, M.[Mubarak],
Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection,
PAMI(46), No. 1, January 2024, pp. 525-542.
IEEE DOI 2312
BibRef
Earlier:
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection,
CVPR22(13566-13576)
IEEE DOI 2210
Convolutional codes, Training, Convolution, Machine vision, Performance gain, Information filters, Vision applications and systems BibRef

Feng, Y.B.[Yang-Bo], Gao, J.Y.[Jun-Yu], Yang, S.C.[Shi-Cai], Xu, C.S.[Chang-Sheng],
Spatial-Temporal Exclusive Capsule Network for Open Set Action Recognition,
MultMed(25), 2023, pp. 9464-9478.
IEEE DOI 2312
BibRef

Guo, C.[Chongye], Wang, H.B.[Hong-Bo], Xia, Y.J.[Ying-Jie], Feng, G.R.[Guo-Rui],
Learning Appearance-Motion Synergy via Memory-Guided Event Prediction for Video Anomaly Detection,
CirSysVideo(34), No. 3, March 2024, pp. 1519-1531.
IEEE DOI 2403
Behavioral sciences, Anomaly detection, Feature extraction, Correlation, Task analysis, Visualization, Prototypes, event prediction BibRef

Shi, H.Y.[Hao-Yue], Wang, L.[Le], Zhou, S.P.[San-Ping], Hua, G.[Gang], Tang, W.[Wei],
Abnormal Ratios Guided Multi-Phase Self-Training for Weakly-Supervised Video Anomaly Detection,
MultMed(26), 2024, pp. 5575-5587.
IEEE DOI 2404
Anomaly detection, Training, Annotations, Labeling, Road accidents, Feature extraction, Adaptation models, Anomaly detection, multiple instance learning BibRef

Wu, P.[Peng], Liu, J.[Jing], He, X.[Xiangteng], Peng, Y.X.[Yu-Xin], Wang, P.[Peng], Zhang, Y.N.[Yan-Ning],
Toward Video Anomaly Retrieval From Video Anomaly Detection: New Benchmarks and Model,
IP(33), 2024, pp. 2213-2225.
IEEE DOI Code:
WWW Link. 2404
Reactive power, Task analysis, Accidents, Automobiles, Anomaly detection, Training, Benchmark testing, cross-modal retrieval BibRef

Aslam, N.[Nazia], Kolekar, M.H.[Maheshkumar H.],
TransGANomaly: Transformer based Generative Adversarial Network for Video Anomaly Detection,
JVCIR(100), 2024, pp. 104108.
Elsevier DOI 2405
Anomaly detection, Video vision transformer, Adversarial training, Generative adversarial network BibRef

Li, D.H.[Dao-Heng], Nie, X.S.[Xiu-Shan], Gong, R.[Rui], Lin, X.M.[Xi-Ming], Yu, H.[Hui],
Multi-Branch GAN-Based Abnormal Events Detection via Context Learning in Surveillance Videos,
CirSysVideo(34), No. 5, May 2024, pp. 3439-3450.
IEEE DOI 2405
Videos, Anomaly detection, Generators, Training, Feature extraction, Generative adversarial networks, Task analysis, pseudo-anomaly module BibRef

Yao, H.M.[Hai-Ming], Yu, W.Y.[Wen-Yong], Luo, W.[Wei], Qiang, Z.F.[Zhen-Feng], Luo, D.H.[Dong-Hao], Zhang, X.T.[Xiao-Tian],
Learning Global-Local Correspondence With Semantic Bottleneck for Logical Anomaly Detection,
CirSysVideo(34), No. 5, May 2024, pp. 3589-3605.
IEEE DOI 2405
Feature extraction, Semantics, Estimation, Visualization, Anomaly detection, Training, Image reconstruction, vision transformer BibRef

Yang, Z.[Zhen], Guo, Y.F.[Yuan-Fang], Wang, J.[Junfu], Huang, D.[Di], Bao, X.[Xiuguo], Wang, Y.H.[Yun-Hong],
Towards Video Anomaly Detection in the Real World: A Binarization Embedded Weakly-Supervised Network,
CirSysVideo(34), No. 5, May 2024, pp. 4135-4140.
IEEE DOI 2405
Feature extraction, Anomaly detection, Training, Surveillance, Memory management, Correlation, Convolution, binarized graph convolutional network BibRef


Li, Y.T.[Yi-Ting], David, A.G.[Adam Goodge], Liu, F.[Fayao], Foo, C.S.[Chuan-Sheng],
PromptAD: Zero-shot Anomaly Detection using Text Prompts,
WACV24(1082-1091)
IEEE DOI 2404
Training, Visualization, Natural languages, Semantics, Self-supervised learning, Benchmark testing, Algorithms, Vision + language and/or other modalities BibRef

Tan, W.J.[Wei-Jun], Yao, Q.[Qi], Liu, J.F.[Jing-Feng],
Overlooked Video Classification in Weakly Supervised Video Anomaly Detection,
RWSurvil24(212-220)
IEEE DOI Code:
WWW Link. 2404
Training, Codes, Performance gain, Classification algorithms, Anomaly detection BibRef

AlMarri, S.[Salem], Zaheer, M.Z.[Muhammad Zaigham], Nandakumar, K.[Karthik],
A Multi-Head Approach with Shuffled Segments for Weakly-Supervised Video Anomaly Detection,
RWSurvil24(132-142)
IEEE DOI 2404
Training, Location awareness, Stochastic processes, Self-supervised learning, Noise measurement, Task analysis BibRef

Majhi, S.[Snehashis], Dai, R.[Rui], Kong, Q.[Quan], Garattoni, L.[Lorenzo], Francesca, G.[Gianpiero], Brémond, F.[François],
OE-CTST: Outlier-Embedded Cross Temporal Scale Transformer for Weakly-supervised Video Anomaly Detection,
WACV24(8559-8568)
IEEE DOI 2404
Correlation, Transformers, Feature extraction, Encoding, Anomaly detection, Applications, Social good, Applications, Autonomous Driving BibRef

Karim, H.[Hamza], Doshi, K.[Keval], Yilmaz, Y.[Yasin],
Real-Time Weakly Supervised Video Anomaly Detection,
WACV24(6834-6842)
IEEE DOI 2404
Training, Measurement, Visualization, Refining, Pipelines, Streaming media, Feature extraction, Algorithms BibRef

Al-Lahham, A.[Anas], Tastan, N.[Nurbek], Zaheer, M.Z.[Muhammad Zaigham], Nandakumar, K.[Karthik],
A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection,
WACV24(6779-6788)
IEEE DOI 2404
Training, Annotations, Surveillance, Detectors, Generators, Anomaly detection, Algorithms, Video recognition and understanding BibRef

Duka, E.[Enea], Kukleva, A.[Anna], Schiele, B.[Bernt],
Leveraging Self-supervised Training for Unintentional Action Recognition,
SelfLearn22(69-85).
Springer DOI 2304
BibRef

Park, S.[Seongheon], Kim, H.[Hanjae], Kim, M.[Minsu], Kim, D.[Dahye], Sohn, K.H.[Kwang-Hoon],
Normality Guided Multiple Instance Learning for Weakly Supervised Video Anomaly Detection,
WACV23(2664-2673)
IEEE DOI 2302
Training, Prototypes, Streaming media, Predictive models, Video surveillance, Real-time systems, Safety, Robotics BibRef

Doshi, K.[Keval], Yilmaz, Y.[Yasin],
Towards Interpretable Video Anomaly Detection,
WACV23(2654-2663)
IEEE DOI 2302
BibRef
Earlier:
Any-Shot Sequential Anomaly Detection in Surveillance Videos,
VL3W20(4037-4042)
IEEE DOI 2008
Surveillance, Transfer learning, Pipelines, Training data, Detectors, Benchmark testing, Reliability theory. Training, Videos, Feature extraction, Anomaly detection, Neural networks, Data models, Integrated optics BibRef

Khazaie, V.R.[Vahid Reza], Wong, A.[Anthony], Jewell, J.T.[John Taylor], Mohsenzadeh, Y.[Yalda],
Anomaly Detection with Adversarially Learned Perturbations of Latent Space,
CRV22(183-189)
IEEE DOI 2301
Training, Deep learning, Perturbation methods, Neural networks, Feature extraction, Convolutional neural networks, Task analysis, Autoencoder BibRef

Kirchheim, K.[Konstantin], Filax, M.[Marco], Ortmeier, F.[Frank],
Multi-Class Hypersphere Anomaly Detection,
ICPR22(2636-2642)
IEEE DOI 2212
Learning systems, Training, Codes, Performance gain, Benchmark testing, Linear programming, Classification algorithms BibRef

Lee, J.[Jooyeon], Nam, W.J.[Woo-Jeoung], Lee, S.W.[Seong-Whan],
Multi-Contextual Predictions with Vision Transformer for Video Anomaly Detection,
ICPR22(1012-1018)
IEEE DOI 2212
Training, Measurement, Predictive models, Streaming media, Transformers, Task analysis BibRef

Li, Y.[Youyu], Song, X.N.[Xiao-Ning], Xu, T.Y.[Tian-Yang], Feng, Z.H.[Zhen-Hua],
Memory-Token Transformer for Unsupervised Video Anomaly Detection,
ICPR22(3325-3332)
IEEE DOI 2212
Convolution, Semantics, Video sequences, Memory modules, Benchmark testing, Transformers BibRef

Li, J.Z.[Jing-Ze], Lian, Z.C.[Zhi-Chao], Li, M.[Min],
A Novel Contrastive Learning Framework for Self-Supervised Anomaly Detection,
ICIP22(3366-3370)
IEEE DOI 2211
Location awareness, Indexes, Image reconstruction, Anomaly detection, Index Terms, local regions reconstitution BibRef

Zhuang, Z.S.[Zi-Song], Zhang, J.H.[Jun-Hang], Xiao, L.[Luwei], Ma, T.L.[Tian-Long], He, L.[Liang],
PGTNet: Prototype Guided Transfer Network for Few-Shot Anomaly Localization,
ICIP22(2321-2325)
IEEE DOI 2211
Location awareness, Measurement, Clustering methods, Prototypes, Task analysis, Anomaly detection, Anomaly Detection, Metric Learning BibRef

Watanabe, Y.[Yudai], Okabe, M.[Makoto], Harada, Y.[Yasunori], Kashima, N.[Naoji],
Real-World Video Anomaly Detection by Extracting Salient Features,
ICIP22(891-895)
IEEE DOI 2211
Supervised learning, Neural networks, Focusing, Feature extraction, Anomaly detection, Videos, Weakly supervised anomaly detection BibRef

Li, G.Q.[Guo-Qiu], Cai, G.X.[Guan-Xiong], Zeng, X.Y.[Xing-Yu], Zhao, R.[Rui],
Scale-Aware Spatio-Temporal Relation Learning for Video Anomaly Detection,
ECCV22(IV:333-350).
Springer DOI 2211
BibRef

Wang, G.[Gaoang], Zhan, Y.B.[Yi-Bing], Wang, X.C.[Xin-Chao], Song, M.L.[Ming-Li], Nahrstedt, K.[Klara],
Hierarchical Semi-supervised Contrastive Learning for Contamination-Resistant Anomaly Detection,
ECCV22(XXV:110-128).
Springer DOI 2211
BibRef

Mumcu, F.[Furkan], Doshi, K.[Keval], Yilmaz, Y.[Yasin],
Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems,
ArtOfRobust22(205-212)
IEEE DOI 2210
Computational modeling, Wireless networks, Video surveillance, Adversarial machine learning, Synchronization, Servers BibRef

Zaheer, M.Z.[M. Zaigham], Mahmood, A.[Arif], Khan, M.H.[M. Haris], Segu, M.[Mattia], Yu, F.[Fisher], Lee, S.I.[Seung-Ik],
Generative Cooperative Learning for Unsupervised Video Anomaly Detection,
CVPR22(14724-14734)
IEEE DOI 2210
Training, Costs, Annotations, Machine vision, Manuals, Generators, Self- semi- meta- Vision applications and systems BibRef

Deng, H.Q.[Han-Qiu], Li, X.Y.[Xing-Yu],
Anomaly Detection via Reverse Distillation from One-Class Embedding,
CVPR22(9727-9736)
IEEE DOI 2210
Location awareness, Perturbation methods, Computational modeling, Pattern recognition, Image restoration, Transfer/low-shot/long-tail learning BibRef

Ding, C.[Choubo], Pang, G.S.[Guan-Song], Shen, C.H.[Chun-Hua],
Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection*,
CVPR22(7378-7388)
IEEE DOI 2210
Training, Representation learning, Machine vision, Computational modeling, Inspection, Vision applications and systems BibRef

Acsintoae, A.[Andra], Florescu, A.[Andrei], Georgescu, M.I.[Mariana-Iuliana], Mare, T.[Tudor], Sumedrea, P.[Paul], Ionescu, R.T.[Radu Tudor], Khan, F.S.[Fahad Shahbaz], Shah, M.[Mubarak],
UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection,
CVPR22(20111-20121)
IEEE DOI 2210
Training, Learning systems, Event detection, Benchmark testing, Performance gain, Data models, Action and event recognition, Video analysis and understanding BibRef

Sevyeri, L.R.[Laya Rafiee], Fevens, T.[Thomas],
AD-CGAN: Contrastive Generative Adversarial Network for Anomaly Detection,
CIAP22(I:322-334).
Springer DOI 2205
BibRef

Bao, W.T.[Wen-Tao], Yu, Q.[Qi], Kong, Y.[Yu],
Evidential Deep Learning for Open Set Action Recognition,
ICCV21(13329-13338)
IEEE DOI 2203
Deep learning, Training, Uncertainty, Image recognition, Computational modeling, Training data, Video analysis and understanding BibRef

Liu, G.L.[Guo-Liang], Lan, S.Y.[Shi-Yong], Zhang, T.[Ting], Huang, W.K.[Wei-Kang], Wang, W.W.[Wen-Wu],
SAGAN: Skip-Attention GAN for Anomaly Detection,
ICIP21(2468-2472)
IEEE DOI 2201
Image processing, Generative adversarial networks, Generators, Anomaly detection, Depth-wise Separable Convolutions BibRef

Astrid, M.[Marcella], Zaheer, M.Z.[Muhammad Zaigham], Lee, S.I.[Seung-Ik],
Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection,
RSLCV21(207-214)
IEEE DOI 2112
Training, Analytical models, Synthesizers, Computational modeling, Anomaly detection BibRef

Madan, N.[Neelu], Farkhondeh, A.[Arya], Nasrollahi, K.[Kamal], Escalera, S.[Sergio], Moeslund, T.B.[Thomas B.],
Temporal Cues from Socially Unacceptable Trajectories for Anomaly Detection,
DYAD21(2150-2158)
IEEE DOI 2112
Tracking, Fuses, Surveillance, Object detection, Skeleton, Trajectory BibRef

Carrara, F.[Fabio], Amato, G.[Giuseppe], Brombin, L.[Luca], Falchi, F.[Fabrizio], Gennaro, C.[Claudio],
Combining GANs and AutoEncoders for efficient anomaly detection,
ICPR21(3939-3946)
IEEE DOI 2105
Visualization, Benchmark testing, Decoding, Proposals, Iterative methods, Task analysis, Anomaly detection BibRef

Collin, A.S.[Anne-Sophie], de Vleeschouwer, C.[Christophe],
Improved anomaly detection by training an autoencoder with skip connections on images corrupted with Stain-shaped noise,
ICPR21(7915-7922)
IEEE DOI 2105
Training, Location awareness, Uncertainty, Monte Carlo methods, Pattern recognition, Image reconstruction, Anomaly detection BibRef

Rippel, O.[Oliver], Mertens, P.[Patrick], Merhof, D.[Dorit],
Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection,
ICPR21(6726-6733)
IEEE DOI 2105
Training, Fitting, Transfer learning, Receivers, Feature extraction, Data models BibRef

Perera, P.[Pramuditha], Patel, V.M.[Vishal M.],
A Joint Representation Learning and Feature Modeling Approach for One-class Recognition,
ICPR21(6600-6607)
IEEE DOI 2105
Target recognition, Redundancy, Force, Decision making, Pattern recognition, Task analysis, Anomaly detection BibRef

Wu, J.C.[Jhih-Ciang], Chen, D.J.[Ding-Jie], Fuh, C.S.[Chiou-Shann], Liu, T.L.[Tyng-Luh],
Learning Unsupervised Metaformer for Anomaly Detection,
ICCV21(4349-4358)
IEEE DOI 2203
Location awareness, Adaptation models, Image resolution, Performance gain, Inspection, Transformers, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Tian, Y.[Yu], Pang, G.S.[Guan-Song], Chen, Y.H.[Yuan-Hong], Singh, R.[Rajvinder], Verjans, J.W.[Johan W.], Carneiro, G.[Gustavo],
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning,
ICCV21(4955-4966)
IEEE DOI 2203
Adaptation models, Computational modeling, Benchmark testing, Feature extraction, Data models, Robustness, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Sheynin, S.[Shelly], Benaim, S.[Sagie], Wolf, L.B.[Lior B.],
A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection,
ICCV21(8475-8484)
IEEE DOI 2203
Training, Computational modeling, Generators, Adversarial machine learning, Task analysis, Anomaly detection, Neural generative models BibRef

Szymanowicz, S.[Stanislaw], Charles, J.[James], Cipolla, R.[Roberto],
Discrete neural representations for explainable anomaly detection,
WACV22(1506-1514)
IEEE DOI 2202
Training, Upper bound, Computational modeling, Benchmark testing, Task analysis, Privacy and Ethics in Vision Anomaly Detection BibRef

Doshi, K.[Keval], Yilmaz, Y.[Yasin],
A Modular and Unified Framework for Detecting and Localizing Video Anomalies,
WACV22(3007-3016)
IEEE DOI 2202
Performance evaluation, Location awareness, Event detection, Transfer learning, Benchmark testing, Feature extraction, Security/Surveillance Scene Understanding BibRef

Tsai, C.C.[Chin-Chia], Wu, T.H.[Tsung-Hsuan], Lai, S.H.[Shang-Hong],
Multi-Scale Patch-Based Representation Learning for Image Anomaly Detection and Segmentation,
WACV22(3065-3073)
IEEE DOI 2202
Representation learning, Training, Image segmentation, Image representation, Benchmark testing, Semi- and Un- supervised Learning BibRef

Gudovskiy, D.[Denis], Ishizaka, S.[Shun], Kozuka, K.[Kazuki],
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows,
WACV22(1819-1828)
IEEE DOI 2202
Location awareness, Analytical models, Computational modeling, Feature extraction, Real-time systems, Data models, Semi- and Un- supervised Learning BibRef

Slavic, G.[Giulia], Alemaw, A.S.[Abrham Shiferaw], Marcenaro, L.[Lucio], Regazzoni, C.[Carlo],
Learning of Linear Video Prediction Models In A Multi-Modal Framework for Anomaly Detection,
ICIP21(1569-1573)
IEEE DOI 2201
Maximum likelihood detection, Nonlinear filters, Predictive models, Markov processes, Particle filters, Dynamic Bayesian Networks BibRef

Purwanto, D.[Didik], Chen, Y.T.[Yie-Tarng], Fang, W.H.[Wen-Hsien],
Dance with Self-Attention: A New Look of Conditional Random Fields on Anomaly Detection in Videos,
ICCV21(173-183)
IEEE DOI 2203
Correlation, Feature extraction, Convolutional neural networks, Anomaly detection, Recognition and classification, Scene analysis and understanding BibRef

Leroux, S.[Sam], Li, B.[Bo], Simoens, P.[Pieter],
Multi-branch Neural Networks for Video Anomaly Detection in Adverse Lighting and Weather Conditions,
WACV22(3027-3035)
IEEE DOI 2202
Rain, Surveillance, Brightness, Urban areas, Lighting, Cameras, Security/Surveillance Datasets, Semi- and Un- supervised Learning BibRef

Lv, H.[Hui], Chen, C.[Chen], Cui, Z.[Zhen], Xu, C.Y.[Chun-Yan], Li, Y.[Yong], Yang, J.[Jian],
Learning Normal Dynamics in Videos with Meta Prototype Network,
CVPR21(15420-15429)
IEEE DOI 2111
Training, Video sequences, Memory management, Prototypes, Benchmark testing, Streaming media, Real-time systems BibRef

Li, C.L.[Chun-Liang], Sohn, K.[Kihyuk], Yoon, J.S.[Jin-Sung], Pfister, T.[Tomas],
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization,
CVPR21(9659-9669)
IEEE DOI 2111
Location awareness, Training, Buildings, Transfer learning, Training data, Detectors BibRef

Reiss, T.[Tal], Cohen, N.[Niv], Bergman, L.[Liron], Hoshen, Y.[Yedid],
PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation,
CVPR21(2805-2813)
IEEE DOI 2111
Transfer learning, Supervised learning, Performance gain, Feature extraction, Pattern recognition BibRef

Ouyang, Y.Q.[Yu-Qi], Sanchez, V.[Victor],
Video Anomaly Detection by Estimating Likelihood of Representations,
ICPR21(8984-8991)
IEEE DOI 2105
Noise reduction, Neural networks, Estimation, Detectors, Probabilistic logic, Performance analysis, Pattern recognition, Gaussian Mixture Model BibRef

Frikha, A.[Ahmed], Krompaß, D.[Denis], Tresp, V.[Volker],
ARCADe: A Rapid Continual Anomaly Detector,
ICPR21(10449-10456)
IEEE DOI 2105
Training, Solid modeling, Neural networks, Detectors, Pattern recognition, Task analysis, Anomaly detection BibRef

Lin, S.[Shuheng], Yang, H.[Hua],
Dual-Mode iterative denoiser: Tackling the weak label for anomaly detection,
ICPR21(6742-6749)
IEEE DOI 2105
Training, Convolution, Noise reduction, Neural networks, Training data, Predictive models, Pattern recognition, GCN BibRef

Roy, P.R.[Pankaj Raj], Bilodeau, G.A.[Guillaume-Alexandre], Seoud, L.[Lama],
Local Anomaly Detection in Videos Using Object-centric Adversarial Learning,
HCAU20(219-234).
Springer DOI 2103
BibRef

Lübbering, M.[Max], Gebauer, M.[Michael], Ramamurthy, R.[Rajkumar], Sifa, R.[Rafet], Bauckhage, C.[Christian],
Supervised Autoencoder Variants for End to End Anomaly Detection,
DLPR20(566-581).
Springer DOI 2103
BibRef

Zaheer, M.Z.[Muhammad Zaigham], Mahmood, A.[Arif], Astrid, M.[Marcella], Lee, S.I.[Seung-Ik],
Claws: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection,
ECCV20(XXII:358-376).
Springer DOI 2011
BibRef

Zahid, Y., Tahir, M.A., Durrani, M.N.,
Ensemble Learning Using Bagging And Inception-V3 For Anomaly Detection In Surveillance Videos,
ICIP20(588-592)
IEEE DOI 2011
Feature extraction, Videos, Bagging, Anomaly detection, Neural networks, Training, Support vector machines, Bagging Ensemble BibRef

Lee, W.Y., Wang, Y.C.F.,
Learning Disentangled Feature Representations For Anomaly Detection,
ICIP20(2156-2160)
IEEE DOI 2011
Anomaly detection, Semantics, Visualization, Image reconstruction, Training, Estimation, Feature extraction, Feature disentanglement, generative model BibRef

Doshi, K.[Keval], Yilmaz, Y.[Yasin],
Rethinking Video Anomaly Detection - A Continual Learning Approach,
WACV22(3036-3045)
IEEE DOI 2202
BibRef
Earlier:
Continual Learning for Anomaly Detection in Surveillance Videos,
CLVision20(1025-1034)
IEEE DOI 2008
Performance evaluation, Detectors, Benchmark testing, Task analysis, Anomaly detection, Standards, Scene Understanding. Videos, Feature extraction, Training, Neural networks, Surveillance. BibRef

Zaheer, M.Z.[Muhammad Zaigham], Lee, J.H.[Jin-Ha], Astrid, M.[Marcella], Lee, S.I.[Seung-Ik],
Old Is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm,
CVPR20(14171-14181)
IEEE DOI 2008
Training, Anomaly detection, Generators, Image reconstruction, Robustness, Stability analysis BibRef

Sun, X., Yang, Z., Zhang, C., Ling, K., Peng, G.,
Conditional Gaussian Distribution Learning for Open Set Recognition,
CVPR20(13477-13486)
IEEE DOI 2008
Feature extraction, Training, Task analysis, Testing, Probabilistic logic, Decoding, Anomaly detection BibRef

Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.,
Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings,
CVPR20(4182-4191)
IEEE DOI 2008
Anomaly detection, Training, Feature extraction, Image segmentation, Training data, Machine learning, Uncertainty BibRef

Ramachandra, B., Jones, M.J., Vatsavai, R.R.[R. Raju],
Learning a distance function with a Siamese network to localize anomalies in videos,
WACV20(2587-2596)
IEEE DOI 2006
Videos, Training, Anomaly detection, Testing, Image reconstruction, Task analysis, Computational modeling BibRef

Gauerhof, L., Gu, N.,
Reverse Variational Autoencoder for Visual Attribute Manipulation and Anomaly Detection,
WACV20(2103-2112)
IEEE DOI 2006
Generators, Image reconstruction, Training, Data models, Visualization, Image generation BibRef

Yu, R.C.[Rui-Chi], Wang, H.C.[Hong-Cheng], Li, A.[Ang], Zheng, J.X.[Jing-Xiao], Morariu, V.[Vlad], Davis, L.S.[Larry S.],
Layout-Induced Video Representation for Recognizing Agent-in-Place Actions,
ICCV19(1262-1272)
IEEE DOI 2004
who is doing what, where. feature extraction, image representation, learning (artificial intelligence), neural nets, Aggregates BibRef

Nguyen, T.N., Meunier, J.,
Anomaly Detection in Video Sequence With Appearance-Motion Correspondence,
ICCV19(1273-1283)
IEEE DOI 2004
convolutional neural nets, image motion analysis, image sequences, learning (artificial intelligence), Surveillance BibRef

Hamaguchi, R.[Ryuhei], Sakurada, K.[Ken], Nakamura, R.[Ryosuke],
Rare Event Detection Using Disentangled Representation Learning,
CVPR19(9319-9327).
IEEE DOI 2002
BibRef

Sun, X., Zhu, S., Wu, S., Jing, X.,
Weak Supervised Learning Based Abnormal Behavior Detection,
ICPR18(1580-1585)
IEEE DOI 1812
Video sequences, Feature extraction, Encoding, Supervised learning, Data mining, Brakes, Hidden Markov models, Corresponding Classifier BibRef

Akcay, S.[Samet], Atapour-Abarghouei, A.[Amir], Breckon, T.P.[Toby P.],
GANomaly: Semi-supervised Anomaly Detection via Adversarial Training,
ACCV18(III:622-637).
Springer DOI 1906
BibRef

Sabokrou, M., Khalooei, M., Fathy, M., Adeli, E.,
Adversarially Learned One-Class Classifier for Novelty Detection,
CVPR18(3379-3388)
IEEE DOI 1812
Image reconstruction, Training, Anomaly detection, Videos, Task analysis, Testing BibRef

Vandersteegen, M., van Beeck, K., Goedemé, T.,
Super accurate low latency object detection on a surveillance UAV,
MVA19(1-6)
DOI Link 1911
autonomous aerial vehicles, learning (artificial intelligence), object detection, object tracking, robot vision, flying heights, Optimization BibRef

Wang, L.[Lin], Zhou, F.Q.[Fu-Qiang], Li, Z.X.[Zuo-Xin], Zuo, W.X.[Wang-Xia], Tan, H.S.[Hai-Shu],
Abnormal Event Detection in Videos Using Hybrid Spatio-Temporal Autoencoder,
ICIP18(2276-2280).
IEEE DOI 1809
Decoding, Videos, Public transportation, Anomaly detection, Feature extraction, Encoding, Data models, Autoencoder, LSTM, Abnormality Detection BibRef

Ren, H.M.[Hua-Min], Liu, W.F.[Wei-Feng], Olsen, S.I.[Søren Ingvor], Escalera, S.[Sergio], Moeslund, T.B.[Thomas B.],
Unsupervised Behavior-Specific Dictionary Learning for Abnormal Event Detection,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Wen, H.[Hui], Ge, S.M.[Shi-Ming], Chen, S.X.[Shui-Xian], Wang, H.T.[Hong-Tao], Sun, L.M.[Li-Min],
Abnormal event detection via adaptive cascade dictionary learning,
ICIP15(847-851)
IEEE DOI 1512
BibRef

Yun, K.[Kimin], Kim, J.[Jiyun], Kim, S.W.[Soo Wan], Jeong, H.[Hawook], Choi, J.Y.[Jin Young],
Learning with Adaptive Rate for Online Detection of Unusual Appearance,
ISVC14(I: 698-707).
Springer DOI 1501
BibRef

Sandhan, T., Srivastava, T., Sethi, A., Choi, J.Y.[Jin Young],
Unsupervised learning approach for abnormal event detection in surveillance video by revealing infrequent patterns,
IVCNZ13(494-499)
IEEE DOI 1402
image motion analysis BibRef

Nallaivarothayan, H., Ryan, D., Denman, S.[Simon], Sridharan, S.[Sridha], Fookes, C.[Clinton],
An Evaluation of Different Features and Learning Models for Anomalous Event Detection,
DICTA13(1-8)
IEEE DOI 1402
BibRef
Earlier:
Anomalous Event Detection Using a Semi-Two Dimensional Hidden Markov Model,
DICTA12(1-7).
IEEE DOI 1303
Gaussian processes BibRef

Antic, B.[Borislav], Ommer, B.[Björn],
Per-Sample Kernel Adaptation for Visual Recognition and Grouping,
ICCV15(1251-1259)
IEEE DOI 1602
BibRef
Earlier:
Learning Latent Constituents for Recognition of Group Activities in Video,
ECCV14(I: 33-47).
Springer DOI 1408
BibRef
Earlier:
Video parsing for abnormality detection,
ICCV11(2415-2422).
IEEE DOI 1201
Image recognition BibRef

Schuster, R.[Rene], Schulter, S.[Samuel], Poier, G.[Georg], Hirzer, M.[Martin], Birchbauer, J.[Josef], Roth, P.M.[Peter M.], Bischof, H.[Horst], Winter, M.[Martin], Schallauer, P.[Peter],
Multi-cue learning and visualization of unusual events,
VS11(1933-1940).
IEEE DOI 1201
BibRef

Birchbauer, J.[Josef], Schulter, S.[Samuel], Schuster, R.[Rene], Poier, G.[Georg], Winter, M.[Martin], Schallauer, P.[Peter], Roth, P.M.[Peter M.], Bischof, H.[Horst],
OUTLIER: Online learning and visualization of unusual events,
AVSBS11(533-534).
IEEE DOI 1111
AVSS 2011 demo session. BibRef

Tziakos, I., Cavallaro, A., Xu, L.Q.[Li-Qun],
Local Abnormality Detection in Video Using Subspace Learning,
AVSS10(519-525).
IEEE DOI 1009
BibRef

Roberts, R.[Richard], Potthast, C.[Christian], Dellaert, F.[Frank],
Learning general optical flow subspaces for egomotion estimation and detection of motion anomalies,
CVPR09(57-64).
IEEE DOI 0906
BibRef

Basharat, A.[Arslan], Gritai, A.[Alexei], Shah, M.[Mubarak],
Learning object motion patterns for anomaly detection and improved object detection,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Wang, D.[Dong], Li, J.M.[Jian-Min], Zhang, B.[Bo],
Relay Boost Fusion for Learning Rare Concepts in Multimedia,
CIVR06(271-280).
Springer DOI 0607
BibRef

Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Deep Learning for Detecting Anomalies .


Last update:Jun 17, 2024 at 21:38:11