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
Tao, C.C.[Chen-Chen],
Wang, C.[Chong],
Lin, S.[Sunqi],
Cai, S.[Suhang],
Li, D.[Di],
Qian, J.B.[Jiang-Bo],
Feature Reconstruction With Disruption for Unsupervised Video Anomaly
Detection,
MultMed(26), 2024, pp. 10160-10173.
IEEE DOI
2410
Training, Benchmark testing, Transformers, Feature extraction,
Robustness, Anomaly detection, Cross attention,
unsupervised video anomaly detection
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
Croitoru, F.A.[Florinel-Alin],
Ristea, N.C.[Nicolae-Catalin],
Dscalescu, D.[Dana],
Ionescu, R.T.[Radu Tudor],
Khan, F.S.[Fahad Shahbaz],
Shah, M.[Mubarak],
Lightning fast video anomaly detection via multi-scale adversarial
distillation,
CVIU(247), 2024, pp. 104074.
Elsevier DOI Code:
WWW Link.
2408
Abnormal event detection, Anomaly detection,
Knowledge distillation, Neural networks, Transformers
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
Yu, S.B.[Shou-Bin],
Zhao, Z.Y.[Zhong-Yin],
Fang, H.[Haoshu],
Deng, A.D.[An-Dong],
Su, H.S.[Hai-Sheng],
Wang, D.L.[Dong-Liang],
Gan, W.H.[Wei-Hao],
Lu, C.[Cewu],
Wu, W.[Wei],
Regularity Learning via Explicit Distribution Modeling for Skeletal
Video Anomaly Detection,
CirSysVideo(34), No. 8, August 2024, pp. 6661-6673.
IEEE DOI
2408
Transformers, Optical flow, Anomaly detection, Training,
Task analysis, Trajectory, Feature extraction,
regularity learning
BibRef
Sun, S.Y.[Sheng-Yang],
Gong, X.J.[Xiao-Jin],
Event-driven weakly supervised video anomaly detection,
IVC(149), 2024, pp. 105169.
Elsevier DOI
2408
Video anomaly detection, Weakly supervised, Transformer, Event-driven
BibRef
Pu, Y.J.[Yu-Jiang],
Wu, X.Y.[Xiao-Yu],
Yang, L.[Lulu],
Wang, S.J.[Sheng-Jin],
Learning Prompt-Enhanced Context Features for Weakly-Supervised Video
Anomaly Detection,
IP(33), 2024, pp. 4923-4936.
IEEE DOI Code:
WWW Link.
2409
Anomaly detection, Visualization, Semantics, Feature extraction,
Knowledge based systems, Convolution, Computational modeling,
weak supervision
BibRef
Park, C.[Chaewon],
Kim, D.[Donghyeong],
Cho, M.[MyeongAh],
Kim, M.[Minjung],
Lee, M.[Minseok],
Park, S.[Seungwook],
Lee, S.Y.[Sang-Youn],
Fast video anomaly detection via context-aware shortcut exploration
and abnormal feature distance learning,
PR(157), 2025, pp. 110877.
Elsevier DOI
2409
Video anomaly detection, Surveillance system,
Distance learning, Self-supervised learning, Autoencoder
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
Al-lahham, A.[Anas],
Zaheer, M.Z.[Muhammad Zaigham],
Tastan, N.[Nurbek],
Nandakumar, K.[Karthik],
Collaborative Learning of Anomalies with Privacy (CLAP) for
Unsupervised Video Anomaly Detection: A New Baseline,
CVPR24(12416-12425)
IEEE DOI Code:
WWW Link.
2410
Training, Privacy, Data privacy, Protocols, Federated learning,
Surveillance, Collaboration
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,
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,
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,
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
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,
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,
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,
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 .