16.7.3.3 Detecting Anomalies, Abnormal Event, Abnormal Behavior, or Rare Events, Rare Behaviors

Scarth, G.B.[Gordon B.], Somorjai, R.L.,
Method and apparatus for detection of events or novelties over a change of state,
US_Patent6,064,770, May 16, 2000
WWW Link. BibRef 0005

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

Laur, P.A.[Pierre-Alain], Nock, R.[Richard], Symphor, J.E.[Jean-Emile], Poncelet, P.[Pascal],
Mining evolving data streams for frequent patterns,
PR(40), No. 2, February 2007, pp. 492-503.
Elsevier DOI 0611
BibRef
Earlier: A2, A1, A3, Only:
Statistical Borders for Incremental Mining,
ICPR06(III: 212-215).
IEEE DOI 0609
Data streams; Concentration inequalities; Precision; Recall; Accuracy. BibRef

Huang, Y.[Yan], Pei, J.[Jian], Xiong, H.[Hui],
Mining Co-Location Patterns with Rare Events from Spatial Data Sets,
GeoInfo(10), No. 3, September 2006, pp. 239-260.
Springer DOI 0703
BibRef

Boiman, O.[Oren], Irani, M.[Michal],
Detecting Irregularities in Images and in Video,
IJCV(74), No. 1, August 2007, pp. 17-31.
Springer DOI 0705
BibRef ICCV05(I: 462-469).
IEEE DOI 0510
Award, Marr Prize, HM. Irregular defined in context. BibRef

Adam, A.[Amit], Rivlin, E.[Ehud], Shimshoni, I.[Ilan], Reinitz, D.[Daviv],
Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors,
PAMI(30), No. 3, March 2008, pp. 555-560.
IEEE DOI 0801
Multiple local monitors which collect low-level statistics, each issues an alert which are integrated to the result. 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

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

Pruteanu-Malinici, I.[Iulian], Carin, L.[Lawrence],
Infinite Hidden Markov Models for Unusual-Event Detection in Video,
IP(17), No. 5, May 2008, pp. 811-822.
IEEE DOI 0804
BibRef
Earlier:
Infinite Hidden Markov Models and ISA Features for Unusual-Event Detection in Video,
ICIP07(V: 137-140).
IEEE DOI 0709
BibRef

Sudo, K.[Kyoko], Osawa, T.[Tatsuya], Wakabayashi, K.[Kaoru], Koike, H.[Hideki], Arakawa, K.[Kenichi],
Estimating Anomality of the Video Sequences for Surveillance Using 1-Class SVM,
IEICE(E91-D), No. 7, July 2008, pp. 1929-1936.
DOI Link 0807
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

Tziakos, I.[Ioannis], Cavallaro, A.[Andrea], Xu, L.Q.[Li-Qun],
Video event segmentation and visualisation in non-linear subspace,
PRL(30), No. 2, 15 January 2009, pp. 123-131,.
Elsevier DOI 0804
Unusual event detection; Dimensionality reduction; Laplacian eigenmaps BibRef

Singh, S., Tu, H., Donat, W., Pattipati, K., Willett, P.,
Anomaly Detection via Feature-Aided Tracking and Hidden Markov Models,
SMC-A(39), No. 1, January 2009, pp. 144-159.
IEEE DOI 0901
BibRef

Monachino, C.A.[Cheryl A.], Paradis, R.D.[Rosemary D.],
Scene analysis surveillance system,
US_Patent7,310,442, Dec 18, 2007
WWW Link. BibRef 0712

Silva, J.[Jorge], Willett, R.M.[Rebecca M.],
Hypergraph-Based Anomaly Detection of High-Dimensional Co-Occurrences,
PAMI(31), No. 3, March 2009, pp. 563-569.
IEEE DOI 0902
Small training set. Find anomalies. Applied to non-image tasks. BibRef

Rao, C.[Chinmay], Ray, A.[Asok], Sarkar, S.[Soumik], Yasar, M.[Murat],
Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns,
SIViP(3), No. 2, June 2009, pp. xx-yy.
Springer DOI 0903
Deviation from nominal behavior. PR method, not applied directly to images. BibRef

Dong, Q., Wu, Y., Hu, Z.,
Pointwise Motion Image (PMI): A Novel Motion Representation and Its Applications to Abnormality Detection and Behavior Recognition,
CirSysVideo(19), No. 3, March 2009, pp. 407-416.
IEEE DOI 0903
BibRef

Khalid, S.[Shehzad], Razzaq, S.[Shahid],
Frameworks for multivariate m-mediods based modeling and classification in Euclidean and general feature spaces,
PR(45), No. 3, March 2012, pp. 1092-1103.
Elsevier DOI 1111
Multivariate m-mediods; Classification; Anomaly detection; Data mining; Dynamic modeling BibRef

Loy, C.C.[Chen Change], Xiang, T.[Tao], Gong, S.G.[Shao-Gang],
Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding,
IJCV(90), No. 1, October 2010, pp. xx-yy.
Springer DOI 1007
BibRef
Earlier:
Modelling Activity Global Temporal Dependencies Using Time Delayed Probabilistic Graphical Model,
ICCV09(120-127).
IEEE DOI 0909
BibRef
And:
Modelling Multi-object Activity by Gaussian Processes,
BMVC09(xx-yy).
PDF File. 0909
BibRef
And:
Multi-camera activity correlation analysis,
CVPR09(1988-1995).
IEEE DOI 0906
BibRef
Earlier:
From local temporal correlation to global anomaly detection,
MLMotion08(xx-yy). 0810
BibRef

Loy, C.C.[Chen Change], Xiang, T.[Tao], Gong, S.G.[Shao-Gang],
Detecting and discriminating behavioural anomalies,
PR(44), No. 1, January 2011, pp. 117-132.
Elsevier DOI 1003
Anomaly detection; Dynamic Bayesian Networks; Visual surveillance; Behavior decomposition; Duration modelling BibRef

Zhu, X.T.[Xia-Tian], Loy, C.C.[Chen Change], Gong, S.G.[Shao-Gang],
Learning from Multiple Sources for Video Summarisation,
IJCV(117), No. 3, May 2016, pp. 247-268.
Springer DOI 1605
BibRef
Earlier: A1, A3, A2:
Comparing Visual Feature Coding for Learning Disjoint Camera Dependencies,
BMVC12(94).
DOI Link 1301
BibRef

Venkatesh, S., Konrad, J., Jodoin, P.M.,
Video Anomaly Identification,
SPMag(27), No. 5, 2010, pp. 18-33.
IEEE DOI 1003
BibRef

Moshtaghi, M.[Masud], Havens, T.C.[Timothy C.], Bezdek, J.C.[James C.], Park, L.[Laurence], Leckie, C.[Christopher], Rajasegarar, S.[Sutharshan], Keller, J.M.[James M.], Palaniswami, M.[Marimuthu],
Clustering ellipses for anomaly detection,
PR(44), No. 1, January 2011, pp. 55-69.
Elsevier DOI 1003
Cluster analysis; Elliptical anomalies in wireless sensor networks; Reordered dissimilarity images; Similarity of ellipsoids; Single linkage clustering; Visual assessment BibRef

Benezeth, Y.[Yannick], Jodoin, P.M.[Pierre-Marc], Saligrama, V.[Venkatesh],
Abnormality detection using low-level co-occurring events,
PRL(32), No. 3, 1 February 2011, pp. 423-431.
Elsevier DOI 1101
Video surveillance; Abnormality detection; Motion detection BibRef

Benezeth, Y., Jodoin, P.M., Saligrama, V., Rosenberger, C.,
Abnormal events detection based on spatio-temporal co-occurences,
CVPR09(2458-2465).
IEEE DOI 0906
BibRef

Ermis, E.B.[Erhan Baki], Saligrama, V.[Venkatesh], Jodoin, P.M.[Pierre-Marc], Konrad, J.[Janusz],
Motion segmentation and abnormal behavior detection via behavior clustering,
ICIP08(769-772).
IEEE DOI 0810
BibRef
And:
Abnormal behavior detection and behavior matching for networked cameras,
ICDSC08(1-10).
IEEE DOI 0809
BibRef

Jiang, F.[Fan], Yuan, J.S.[Jun-Song], Tsaftaris, S.A.[Sotirios A.], Katsaggelos, A.K.[Aggelos K.],
Anomalous video event detection using spatiotemporal context,
CVIU(115), No. 3, March 2011, pp. 323-333.
Elsevier DOI 1103
BibRef
Earlier:
Video anomaly detection in spatiotemporal context,
ICIP10(705-708).
IEEE DOI 1009
Video surveillance; Anomaly detection; Data mining; Clustering; Context BibRef

Tran, D.[Du], Yuan, J.S.[Jun-Song], Forsyth, D.A.,
Video Event Detection: From Subvolume Localization to Spatiotemporal Path Search,
PAMI(36), No. 2, February 2014, pp. 404-416.
IEEE DOI 1402
BibRef
Earlier: A1, A3, Only:
Optimal spatio-temporal path discovery for video event detection,
CVPR11(3321-3328).
IEEE DOI 1106
image motion analysis BibRef

Yan, X.[Xinchen], Yuan, J.S.[Jun-Song], Liang, H.[Hui],
Efficient Online Spatio-Temporal Filtering for Video Event Detection,
VECTaR14(769-785).
Springer DOI 1504
BibRef

Liu, C.[Chang], Wang, G.J.[Gui-Jin], Ning, W.X.[Wen-Xin], Lin, X.G.[Xing-Gang],
Drastic Anomaly Detection in Video Using Motion Direction Statistics,
IEICE(E94-D), No. 8, August 2011, pp. 1700-1707.
WWW Link. 1108
BibRef

Liu, C.[Chang], Wang, G.J.[Gui-Jin], Ning, W.X.[Wen-Xin], Lin, X.G.[Xing-Gang], Li, L.[Liang], Liu, Z.[Zhou],
Anomaly detection in surveillance video using motion direction statistics,
ICIP10(717-720).
IEEE DOI 1009
BibRef

Ntalampiras, S., Potamitis, I., Fakotakis, N.,
Probabilistic Novelty Detection for Acoustic Surveillance Under Real-World Conditions,
MultMed(13), No. 4, 2011, pp. 713-719.
IEEE DOI 1108
BibRef

Bertini, M.[Marco], del Bimbo, A.[Alberto], Seidenari, L.[Lorenzo],
Multi-scale and real-time non-parametric approach for anomaly detection and localization,
CVIU(116), No. 3, March 2012, pp. 320-329.
Elsevier DOI 1201
BibRef
Earlier: A3, A1, A2:
Dense spatio-temporal features for non-parametric anomaly detection and localization,
ARTEMIS10(27-32).
DOI Link 1111
Video surveillance; Anomaly detection; Space-time features 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

Popoola, O.P., Wang, K.,
Video-Based Abnormal Human Behavior Recognition: A Review,
SMC-C(42), No. 6, November 2012, pp. 865-878.
IEEE DOI 1210
Survey, Human Activity. 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

Varadarajan, J.[Jagannadan], Emonet, R.[Rémi], Odobez, J.M.[Jean-Marc],
A Sequential Topic Model for Mining Recurrent Activities from Long Term Video Logs,
IJCV(103), No. 1, May 2013, pp. 100-126.
Springer DOI 1305
BibRef

Emonet, R.[Remi], Varadarajan, J.[Jagannadan], Odobez, J.M.[Jean-Marc],
Temporal Analysis of Motif Mixtures Using Dirichlet Processes,
PAMI(36), No. 1, 2014, pp. 140-156.
IEEE DOI 1312
BibRef
Earlier:
Extracting and locating temporal motifs in video scenes using a hierarchical non parametric Bayesian model,
CVPR11(3233-3240).
IEEE DOI 1106
BibRef
Earlier: A2, A1, A3:
Probabilistic Latent Sequential Motifs: Discovering temporal activity patterns in video scenes,
BMVC10(xx-yy).
HTML Version. 1009
Bayesian modeling BibRef

Varadarajan, J.[Jagannadan], Odobez, J.M.[Jean-Marc],
Topic models for scene analysis and abnormality detection,
VS09(1338-1345).
IEEE DOI 0910
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

Lee, S.C.[Sung Chun], Nevatia, R.[Ram],
Hierarchical abnormal event detection by real time and semi-real time multi-tasking video surveillance system,
MVA(25), No. 1, January 2014, pp. 133-143.
Springer DOI 1402
BibRef

Mo, X.[Xuan], Monga, V., Bala, R., Fan, Z.G.[Zhi-Gang],
Adaptive Sparse Representations for Video Anomaly Detection,
CirSysVideo(24), No. 4, April 2014, pp. 631-645.
IEEE DOI 1405
greedy algorithms Transportation domain to identify unusual patterns such as traffic violations, accidents, unsafe driver behavior, street crime, and other suspicious activities. BibRef

Kim, J.[Jiman], Kim, D.J.[Dai-Jin],
Accurate Static Region Classification Using Multiple Cues for ARO Detection,
SPLetters(21), No. 8, August 2014, pp. 937-941.
IEEE DOI 1406
Conferences BibRef

Kim, J.[Jiman], Kang, B.N.[Bong-Nam], Wang, H.[Hai], Kim, D.J.[Dai-Jin],
Abnormal Object Detection Using Feedforward Model and Sequential Filters,
AVSS12(70-75).
IEEE DOI 1211
BibRef

Song, L., Jiang, F., Shi, Z., Molina, R., Katsaggelos, A.K.,
Toward Dynamic Scene Understanding by Hierarchical Motion Pattern Mining,
ITS(15), No. 3, June 2014, pp. 1273-1285.
IEEE DOI 1407
Hidden Markov models BibRef

Alvar, M.[Manuel], Torsello, A.[Andrea], Sanchez-Miralles, A.[Alvaro], Armingol, J.M.[José María],
Abnormal behavior detection using dominant sets,
MVA(25), No. 5, July 2014, pp. 1351-1368.
Springer DOI 1407
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

Susan, S.[Seba], Hanmandlu, M.[Madasu],
Unsupervised detection of nonlinearity in motion using weighted average of non-extensive entropies,
SIViP(9), No. 3, March 2015, pp. 511-525.
Springer DOI 1503
Motion for detecting abnormal motion in videos. BibRef

Kwon, J.[Junseok], Lee, K.M.[Kyoung Mu],
A Unified Framework for Event Summarization and Rare Event Detection from Multiple Views,
PAMI(37), No. 9, September 2015, pp. 1737-1750.
IEEE DOI 1508
BibRef
Earlier:
A unified framework for event summarization and rare event detection,
CVPR12(1266-1273).
IEEE DOI 1208
Cameras BibRef

Zhang, Z.[Zhong], Liu, S.[Shuang], Zhang, Z.W.[Zhi-Wei],
Consistent Sparse Representation for Abnormal Event Detection,
IEICE(E98-D), No. 10, October 2015, pp. 1866-1870.
WWW Link. 1511
BibRef

Jiang, M.[Meng], Cui, P.[Peng], Faloutsos, C.,
Suspicious Behavior Detection: Current Trends and Future Directions,
IEEE_Int_Sys(31), No. 1, January 2016, pp. 31-39.
IEEE DOI 1602
open systems 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

Epaillard, E.[Elise], Bouguila, N.[Nizar],
Proportional data modeling with hidden Markov models based on generalized Dirichlet and Beta-Liouville mixtures applied to anomaly detection in public areas,
PR(55), No. 1, 2016, pp. 125-136.
Elsevier DOI 1604
Hidden Markov models BibRef

Epaillard, E.[Elise], Bouguila, N.[Nizar],
Data-free metrics for Dirichlet and generalized Dirichlet mixture-based HMMs: A practical study,
PR(85), 2019, pp. 207-219.
Elsevier DOI 1810
Hidden Markov models, Similarity measure, Dirichlet, Generalized Dirichlet BibRef

Fortunati, S.[Stefano], Gini, F.[Fulvio], Greco, M.S.[Maria S.], Farina, A.[Alfonso], Graziano, A.[Antonio], Giompapa, S.[Sofia],
An improvement of the state-of-the-art covariance-based methods for statistical anomaly detection algorithms,
SIViP(10), No. 4, April 2016, pp. 687-694.
Springer DOI 1604
BibRef

Ben Abdallah, A.C.[Ahmed Chamseddine], Gouiffès, M.[Michèle], Lacassagne, L.[Lionel],
A modular system for global and local abnormal event detection and categorization in videos,
MVA(27), No. 4, May 2016, pp. 463-481.
Springer DOI 1605
BibRef

Choudhary, A.[Ayesha], Chaudhury, S.[Santanu],
Video analytics revisited,
IET-CV(10), No. 4, 2016, pp. 237-247.
DOI Link 1608
correlation theory BibRef

Choudhary, A.[Ayesha], Pal, M.[Manish], Banerjee, S.[Subhashis], Chaudhury, S.[Santanu],
Unusual Activity Analysis Using Video Epitomes and pLSA,
ICCVGIP08(390-397).
IEEE DOI 0812
BibRef

Blair, C.G., Robertson, N.M.,
Video Anomaly Detection in Real Time on a Power-Aware Heterogeneous Platform,
CirSysVideo(26), No. 11, November 2016, pp. 2109-2122.
IEEE DOI 1609
Algorithm design and analysis 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

Singh, D.[Dinesh], Mohan, C.K.[C. Krishna],
Graph formulation of video activities for abnormal activity recognition,
PR(65), No. 1, 2017, pp. 265-272.
Elsevier DOI 1702
Abnormal activity recognition BibRef

Colque, R.V.H.M., Caetano, C., de Andrade, M.T.L., Schwartz, W.R.,
Histograms of Optical Flow Orientation and Magnitude and Entropy to Detect Anomalous Events in Videos,
CirSysVideo(27), No. 3, March 2017, pp. 673-682.
IEEE DOI 1703
Computer vision BibRef

Yu, B., Liu, Y., Sun, Q.,
A Content-Adaptively Sparse Reconstruction Method for Abnormal Events Detection With Low-Rank Property,
SMCS(47), No. 4, April 2017, pp. 704-716.
IEEE DOI 1704
Dictionaries BibRef

Bensch, R.[Robert], Scherf, N.[Nico], Huisken, J.[Jan], Brox, T.[Thomas], Ronneberger, O.[Olaf],
Spatiotemporal Deformable Prototypes for Motion Anomaly Detection,
IJCV(122), No. 3, May 2017, pp. 502-523.
Springer DOI 1704
BibRef
Earlier: A1, A4, A5, Only: BMVC15(xx-yy).
DOI Link 1601
BibRef

Leyva, R.[Roberto], Sanchez, V.[Victor], Li, C.T.[Chang-Tsun],
Video Anomaly Detection With Compact Feature Sets for Online Performance,
IP(26), No. 7, July 2017, pp. 3463-3478.
IEEE DOI 1706
Cameras, Data mining, Feature extraction, Optical imaging, Training, Video anomaly detection, online processing, video surveillance BibRef

Martin, R.A.[R. Abraham], Blackburn, L.[Landen], Pulsipher, J.[Joshua], Franke, K.[Kevin], Hedengren, J.D.[John D.],
Potential Benefits of Combining Anomaly Detection with View Planning for UAV Infrastructure Modeling,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Chebi, H., Acheli, D., Kesraoui, M.,
Strategy of detecting abnormal behaviors by fuzzy logic,
ISCV17(1-5)
IEEE DOI 1710
abnormal behavior detection, automatic processing, fuzzy logic, surveillance cameras, video streaming, video surveillance, Cameras, Fuzzy logic, Image motion analysis, BibRef

Fuse, T., Kamiya, K.,
Statistical Anomaly Detection in Human Dynamics Monitoring Using a Hierarchical Dirichlet Process Hidden Markov Model,
ITS(18), No. 11, November 2017, pp. 3083-3092.
IEEE DOI 1711
BibRef
Earlier: A2, A1:
Statistical Anomaly Detection for Monitoring of Human Dynamics,
Seamless15(93-98).
DOI Link 1508
Bayes methods, Hidden Markov models, Monitoring, Sociology, Anomaly detection, hidden Markov models, human dynamics, BibRef

Li, S.F.[Shi-Feng], Yang, Y.Q.[Yu-Qiang], Liu, C.X.[Chun-Xiao],
Anomaly detection based on two global grid motion templates,
SP:IC(60), No. 1, 2018, pp. 6-12.
Elsevier DOI 1712
Anomaly detection BibRef

Li, S.F.[Shi-Feng], Liu, C.X.[Chun-Xiao], Yang, Y.Q.[Yu-Qiang],
Anomaly detection based on maximum a posteriori,
PRL(107), 2018, pp. 91-97.
Elsevier DOI 1805
Anomaly detection, MAP, Grid template, BibRef

Liu, K.W.[Kang-Wei], Wan, J.H.[Jian-Hua], Han, Z.Z.[Zhong-Zhi],
Abnormal event detection and localization using level set based on hybrid features,
SIViP(12), No. 2, February 2018, pp. 255-261.
Springer DOI 1802
five image descriptors, namely the color moments, the edge histogram descriptors, the color and edge directivity descriptors, the color layout descriptors, and the scalable color descriptors. BibRef

Torres, B.S.[Berthin S.], Pedrini, H.[Helio],
Detection of complex video events through visual rhythm,
VC(34), No. 2, February 2018, pp. 145-165.
Springer DOI 1802
Feature descriptors extracted from visual rhythms of video sequences in three computer vision problems: abnormal event detection, human action classification, and gesture recognition. BibRef

Xu, K.[Ke], Jiang, X.H.[Xing-Hao], Sun, T.F.[Tan-Feng],
Anomaly Detection Based on Stacked Sparse Coding With Intraframe Classification Strategy,
MultMed(20), No. 5, May 2018, pp. 1062-1074.
IEEE DOI 1805
Anomaly detection, Encoding, Feature extraction, Probabilistic logic, Support vector machines, Training, Videos, stacked sparse coding BibRef

Li, S.F.[Shi-Feng], Liu, C.[Chunxiao], Yang, Y.[Yuqiang],
Anomaly detection based on sparse coding with two kinds of dictionaries,
SIViP(12), No. 5, July 2018, pp. 983-989.
Springer DOI 1806
Dictionary based method. BibRef

Ratre, A.[Avinash], Pankajakshan, V.[Vinod],
Tucker tensor decomposition-based tracking and Gaussian mixture model for anomaly localisation and detection in surveillance videos,
IET-CV(12), No. 6, September 2018, pp. 933-940.
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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.
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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
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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

Hunt, X.J.[Xin J.], Willett, R.[Rebecca],
Online Data Thinning via Multi-Subspace Tracking,
PAMI(41), No. 5, May 2019, pp. 1173-1187.
IEEE DOI 1904
Find anomalies to limit data. Streaming media, Task analysis, Saliency detection, Sensors, Anomaly detection, Robustness, Clustering algorithms, saliency detection BibRef

Yang, C.[Chule], Yue, Y.F.[Yu-Feng], Zhang, J.[Jun], Wen, M.X.[Ming-Xing], Wang, D.W.[Dan-Wei],
Probabilistic Reasoning for Unique Role Recognition Based on the Fusion of Semantic-Interaction and Spatio-Temporal Features,
MultMed(21), No. 5, May 2019, pp. 1195-1208.
IEEE DOI 1905
Someone carrying items, or unique movements. image recognition, inference mechanisms, uncertainty handling, unique role recognition, spatio-temporal features, BibRef

Lin, C.[Chi], Lin, X.X.[Xu-Xin], Xie, Y.L.[Yi-Liang], Liang, Y.Y.[Yan-Yan],
Abnormal gesture recognition based on multi-model fusion strategy,
MVA(30), No. 5, July 2019, pp. 889-900.
Springer DOI 1907
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Zhang, J.[Jin], Wu, C.[Cheng], Wang, Y.M.[Yi-Ming], Wang, P.Y.[Ping-Ye],
Detection of abnormal behavior in narrow scene with perspective distortion,
MVA(30), No. 5, July 2019, pp. 987-998.
Springer DOI 1907
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Lu, C.[Cewu], Shi, J.P.[Jian-Ping], Wang, W.M.[Wei-Ming], Jia, J.Y.[Jia-Ya],
Fast Abnormal Event Detection,
IJCV(127), No. 8, August 2019, pp. 993-1011.
Springer DOI 1907
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Abnormal Event Detection at 150 FPS in MATLAB,
ICCV13(2720-2727)
IEEE DOI 1403
abnormal event detection BibRef

Bappy, J.H., Paul, S., Tuncel, E., Roy-Chowdhury, A.K.,
Exploiting Typicality for Selecting Informative and Anomalous Samples in Videos,
IP(28), No. 10, October 2019, pp. 5214-5226.
IEEE DOI 1909
Videos, Computational modeling, Anomaly detection, Entropy, Manuals, Labeling, Training, Activity recognition, typicality, anomaly and novelty detection 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

Cong, Y.[Yang], Fan, B.J.[Bao-Jie], Hou, D.D.[Dong-Dong], Fan, H.J.[Hui-Jie], Liu, K.Z.[Kai-Zhou], Luo, J.B.[Jie-Bo],
Novel event analysis for human-machine collaborative underwater exploration,
PR(96), 2019, pp. 106967.
Elsevier DOI 1909
Underwater, Underwater robot, Visual summarization, Visual saliency, Visual tracking, Robot vision, Video analysis, Deep sea BibRef

Jardim, E., Thomaz, L.A., da Silva, E.A.B., Netto, S.L.,
Domain-Transformable Sparse Representation for Anomaly Detection in Moving-Camera Videos,
IP(29), No. , 2020, pp. 1329-1343.
IEEE DOI 1911
Cameras, Videos, Optimization, Data models, Matrix decomposition, Analytical models, Principal component analysis, l?-optimization BibRef

Phiboonbanakit, T.[Thananut], Huynh, V.N.[Van-Nam], Horanont, T.[Teerayut], Supnithi, T.[Thepchai],
Unsupervised hybrid anomaly detection model for logistics fleet management systems,
IET-ITS(13), No. 11, November 2019, pp. 1636-1648.
DOI Link 1911
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Kwon, J.[Junseok],
Rare-Event Detection by Quasi-Wang-Landau Monte Carlo Sampling with Approximate Bayesian Computation,
JMIV(61), No. 9, November 2019, pp. 1258-1275.
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Shi, Y., Xu, M., Zhao, R., Fu, H., Wu, T., Cao, N.,
Interactive Context-Aware Anomaly Detection Guided by User Feedback,
HMS(49), No. 6, December 2019, pp. 550-559.
IEEE DOI 1912
Anomaly detection, Complexity theory, Monitoring, Algorithm design and analysis, Anomaly detection, interaction techniques BibRef

Tang, Y.[Yao], Zhao, L.[Lin], Zhang, S.S.[Shan-Shan], Gong, C.[Chen], Li, G.Y.[Guang-Yu], Yang, J.[Jian],
Integrating prediction and reconstruction for anomaly detection,
PRL(129), 2020, pp. 123-130.
Elsevier DOI 2001
Anomaly detection, Reconstruction, Future frame prediction 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.
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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

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

Lin, Y.G.[Yi-Gang],
Automatic recognition of image of abnormal situation in scenic spots based on Internet of things,
IVC(96), 2020, pp. 103908.
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Internet of things, Scenic spots, Abnormal situations, Image recognition BibRef

Liu, Y.[Yeqi], Yu, H.[Huihui], Gong, C.Y.[Chuan-Yang], Chen, Y.Y.[Ying-Yi],
A real time expert system for anomaly detection of aerators based on computer vision and surveillance cameras,
JVCIR(68), 2020, pp. 102767.
Elsevier DOI 2005
Computer vision, Surveillance camera, Anomaly detection, Optical flow, Object region detection, Application BibRef

Chen, D.Y.[Dong-Yue], Wang, P.T.[Peng-Tao], Yue, L.Y.[Ling-Yi], Zhang, Y.X.[Yu-Xin], Jia, T.[Tong],
Anomaly detection in surveillance video based on bidirectional prediction,
IVC(98), 2020, pp. 103915.
Elsevier DOI 2006
Anomaly detection, Bidirectional prediction, Sliding window, U-Net BibRef

Zhang, X.F.[Xin-Feng], Yang, S.[Su], Zhang, J.L.[Jiu-Long], Zhang, W.S.[Wei-Shan],
Video anomaly detection and localization using motion-field shape description and homogeneity testing,
PR(105), 2020, pp. 107394.
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Abnormal activity, Anomaly detection, Anomaly localization, Shape description, -NN similarity-based outlier detection BibRef

Balasundaram, A., Chellappan, C.,
An intelligent video analytics model for abnormal event detection in online surveillance video,
RealTimeIP(17), No. 4, August 2020, pp. 915-930.
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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

Wu, P.[Peng], Liu, J.[Jing], Li, M.M.[Ming-Ming], Sun, Y.[Yujia], Shen, F.[Fang],
Fast sparse coding networks for anomaly detection in videos,
PR(107), 2020, pp. 107515.
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Anomaly detection, Encoding-decoding networks, Sparse coding networks, Spatial-temporal information, Video representation 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,
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Fault detection, Anomaly detection, Smart manufacturing, Smart industry, Interpretable machine learning, Autoencoder, Anomaly discriminator BibRef

Guo, J., Zheng, P., Huang, J.,
Efficient Privacy-Preserving Anomaly Detection and Localization in Bitstream Video,
CirSysVideo(30), No. 9, September 2020, pp. 3268-3281.
IEEE DOI 2009
Anomaly detection, Encryption, Cloud computing, Feature extraction, Servers, Signal processing in the encrypted domain, cloud computing 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

Wang, Z.G.[Zhi-Guo], Yang, Z.L.[Zhong-Liang], Zhang, Y.J.[Yu-Jin],
A promotion method for generation error-based video anomaly detection,
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Anomaly detection, Block-level, Generation error, Surveillance video BibRef

Zavrtanik, V.[Vitjan], Kristan, M.[Matej], Skocaj, D.[Danijel],
Reconstruction by inpainting for visual anomaly detection,
PR(112), 2021, pp. 107706.
Elsevier DOI 2102
Anomaly detection, Video anomaly detection, Inpainting, CNN BibRef

Asad, M.[Mujtaba], Yang, J.[Jie], Tu, E.[Enmei], Chen, L.M.[Li-Ming], He, X.J.[Xiang-Jian],
Anomaly3D: Video anomaly detection based on 3D-normality clusters,
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Elsevier DOI 2103
Spatiotemporal latent features, 3D-CAE, Anomaly detection, Video analysis, Autonomous video surveillance BibRef

Öztürk, H.I.[Halil Ibrahim], Can, A.B.[Ahmet Burak],
Adnet: Temporal Anomaly Detection in Surveillance Videos,
MLCSA20(88-101).
Springer DOI 2103
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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

Zhang, L., Zhao, J., Li, W.,
Online and Unsupervised Anomaly Detection for Streaming Data Using an Array of Sliding Windows and PDDs,
Cyber(51), No. 4, April 2021, pp. 2284-2289.
IEEE DOI 2103
Anomaly detection, Arrays, Estimation, Kernel, Data models, Bandwidth, Anomaly detection, concept drift, streaming data BibRef

Bergmann, P.[Paul], Batzner, K.[Kilian], Fauser, M.[Michael], Sattlegger, D.[David], Steger, C.[Carsten],
The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection,
IJCV(129), No. 4, April 2021, pp. 1038-1059.
Springer DOI 2104
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MVTec AD: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection,
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Degardin, B.[Bruno], Proença, H.[Hugo],
Iterative weak/self-supervised classification framework for abnormal events detection,
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Elsevier DOI 2104
Visual surveillance, Abnormal events detection, Weakly supervised learning BibRef

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Towards a data-driven adaptive anomaly detection system for human activity,
PRL(145), 2021, pp. 200-207.
Elsevier DOI 2104
Anomaly detection, Activities of daily living, Similarity measure, Forgetting factor, Ensemble model BibRef

Zhou, K.[Kai], Hui, B.[Bei], Wang, J.F.[Jun-Feng], Wang, C.Y.[Chun-Yu], Wu, T.T.[Ting-Ting],
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IVC(108), 2021, pp. 104120.
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Abnormal behavior, Attention, LSTM, Variable pooling BibRef

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Padim: A Patch Distribution Modeling Framework for Anomaly Detection and Localization,
IML20(475-489).
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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).
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Park, J.[Jaeyoo], Kim, J.[Junha], Han, B.H.[Bo-Hyung],
Learning to Adapt to Unseen Abnormal Activities Under Weak Supervision,
ACCV20(V:514-529).
Springer DOI 2103
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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).
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Mantini, P.[Pranav], Li, Z.[Zhenggang], Shah, K.S.[K. Shishir],
A Day on Campus - An Anomaly Detection Dataset for Events in a Single Camera,
ACCV20(VI:619-635).
Springer DOI 2103
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Yi, J.[Jihun], Yoon, S.[Sungroh],
Patch SVDD: Patch-level Svdd for Anomaly Detection and Segmentation,
ACCV20(VI:375-390).
Springer DOI 2103
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Zhang, C., Li, G., Su, L., Zhang, W., Huang, Q.,
Video Anomaly Detection Using Open Data Filter and Domain Adaptation,
VCIP20(395-398)
IEEE DOI 2102
Training, Training data, Anomaly detection, Feature extraction, Data models, Testing, Adaptation models, anomaly detection, domain adaptation BibRef

Ma, T., Wang, Y., Shao, J., Zhang, B., Doermann, D.,
Orthogonal Features Fusion Network for Anomaly Detection,
VCIP20(33-37)
IEEE DOI 2102
Training, Optical fiber networks, Generators, Convolution, Optical imaging, Anomaly detection, Feature extraction, off-cnn BibRef

Venkataramanan, S.[Shashanka], Peng, K.C.[Kuan-Chuan], Singh, R.V.[Rajat Vikram], Mahalanobis, A.[Abhijit],
Attention Guided Anomaly Localization in Images,
ECCV20(XVII:485-503).
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Inspection, surveillance. 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
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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

Sun, L., Chen, Y., Luo, W., Wu, H., Zhang, C.,
Discriminative Clip Mining for Video Anomaly Detection,
ICIP20(2121-2125)
IEEE DOI 2011
Anomaly detection, Feature extraction, Testing, Training, Task analysis, Indexes, Surveillance, anomaly detection, contrastive pattern BibRef

Lu, Y.[Yiwei], Yu, F.[Frank], Reddy, M.K.K.[Mahesh Kumar Krishna], Wang, Y.[Yang],
Few-shot Scene-adaptive Anomaly Detection,
ECCV20(V:125-141).
Springer DOI 2011
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Roady, R., Hayes, T.L., Vaidya, H., Kanan, C.,
Stream-51: Streaming Classification and Novelty Detection from Videos,
CLVision20(925-934)
IEEE DOI 2008
Videos, Streaming media, Training, Task analysis, Protocols, Real-time systems, Object detection BibRef

Epstein, D., Chen, B., Vondrick, C.,
Oops! Predicting Unintentional Action in Video,
CVPR20(916-926)
IEEE DOI 2008
Task analysis, Visualization, Computational modeling, Analytical models, Benchmark testing, Training, Standards BibRef

Doshi, K., Yilmaz, Y.,
Continual Learning for Anomaly Detection in Surveillance Videos,
CLVision20(1025-1034)
IEEE DOI 2008
Videos, Feature extraction, Anomaly detection, Training, Neural networks, Surveillance, Computer vision 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

Markovitz, A., Sharir, G., Friedman, I., Zelnik-Manor, L., Avidan, S.,
Graph Embedded Pose Clustering for Anomaly Detection,
CVPR20(10536-10544)
IEEE DOI 2008
Anomaly detection, Predictive models, Lighting, Training, Data models, Clustering algorithms, Benchmark testing 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

Kilickaya, M., Smeulders, A.,
Diagnosing Rarity in Human-object Interaction Detection,
VL3W20(3956-3960)
IEEE DOI 2008
Detectors, Tin, Clutter, Sensitivity, Benchmark testing, Object detection, Training BibRef

Doshi, K., Yilmaz, Y.,
Any-Shot Sequential Anomaly Detection in Surveillance Videos,
VL3W20(4037-4042)
IEEE DOI 2008
Training, Videos, Feature extraction, Anomaly detection, Neural networks, Data models, Integrated optics 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

Ramachandra, B., Jones, M.J.,
Street Scene: A new dataset and evaluation protocol for video anomaly detection,
WACV20(2558-2567)
IEEE DOI 2006
Anomaly detection, Training, Cameras, Testing, Legged locomotion, Public transportation, Surveillance 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

Wang, J., Cherian, A.,
GODS: Generalized One-Class Discriminative Subspaces for Anomaly Detection,
ICCV19(8200-8210)
IEEE DOI 2004
computational geometry, concave programming, conjugate gradient methods, convex programming, Manifolds 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
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Ionescu, R.T.[Radu Tudor], Khan, F.S.[Fahad Shahbaz], Georgescu, M.I.[Mariana-Iuliana], Shao, L.[Ling],
Object-Centric Auto-Encoders and Dummy Anomalies for Abnormal Event Detection in Video,
CVPR19(7834-7843).
IEEE DOI 2002
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Zhong, J.X.[Jia-Xing], Li, N.N.[Nan-Nan], Kong, W.J.[Wei-Jie], Liu, S.[Shan], Li, T.H.[Thomas H.], Li, G.[Ge],
Graph Convolutional Label Noise Cleaner: Train a Plug-And-Play Action Classifier for Anomaly Detection,
CVPR19(1237-1246).
IEEE DOI 2002
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Wei, H.[Hao], Li, K.[Kai], Li, H.[Haichang], Lyu, Y.[Yifan], Hu, X.H.[Xiao-Hui],
Detecting Video Anomaly with a Stacked Convolutional LSTM Framework,
CVS19(330-342).
Springer DOI 1912
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Shadaydeh, M.[Maha], Denzler, J.[Joachim], García, Y.G.[Yanira Guanche], Mahecha, M.[Miguel],
Time-Frequency Causal Inference Uncovers Anomalous Events in Environmental Systems,
GCPR19(499-512).
Springer DOI 1911
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Trifunov, V.T.[Violeta Teodora], Shadaydeh, M.[Maha], Runge, J.[Jakob], Eyring, V.[Veronika], Reichstein, M.[Markus], Denzler, J.[Joachim],
Nonlinear Causal Link Estimation Under Hidden Confounding with an Application to Time Series Anomaly Detection,
GCPR19(261-273).
Springer DOI 1911
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Yin, Z., Chen, X., Huang, K.,
An Effective Adversarial Training Based Spatial-Temporal Network for Abnormal Behavior Detection,
ICIP19(4085-4089)
IEEE DOI 1910
abnormal behavior detection, adversarial training, spatial-temporal BibRef

Sabokrou, M.[Mohammad], Pourreza, M.[Masoud], Fayyaz, M.[Mohsen], Entezari, R.[Rahim], Fathy, M.[Mahmood], Gall, J.[Jürgen], Adeli, E.[Ehsan],
AVID: Adversarial Visual Irregularity Detection,
ACCV18(VI:488-505).
Springer DOI 1906
detection of irregularities. 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
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Yan, M., Jiang, X., Yuan, J.,
3D Convolutional Generative Adversarial Networks for Detecting Temporal Irregularities in Videos,
ICPR18(2522-2527)
IEEE DOI 1812
Videos, Generative adversarial networks, Generators, Convolution, Training 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

Jin, D., Zhu, S., Wu, S., Jing, X.,
Sparse Representation and Weighted Clustering Based Abnormal Behavior Detection,
ICPR18(1574-1579)
IEEE DOI 1812
Optical flow, Dictionaries, Histograms, Feature extraction, Image reconstruction, Containers, Acceleration, weighted clustering BibRef

Sultani, W., Chen, C., Shah, M.,
Real-World Anomaly Detection in Surveillance Videos,
CVPR18(6479-6488)
IEEE DOI 1812
Videos, Anomaly detection, Surveillance, Training, Hidden Markov models, Computer vision, Cameras 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

Liu, W., Luo, W., Lian, D., Gao, S.,
Future Frame Prediction for Anomaly Detection - A New Baseline,
CVPR18(6536-6545)
IEEE DOI 1812
Videos, Anomaly detection, Image reconstruction, Feature extraction, Training data, Optical imaging, Training BibRef

Wang, C., Zhang, Y., Liu, C.,
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks,
ICPR18(1121-1126)
IEEE DOI 1812
Generators, Anomaly detection, Generative adversarial networks, Training, Linear programming, Computational modeling BibRef

Wang, L., Zhou, F., Li, Z., Zuo, W., Tan, H.,
Abnormal Event Detection in Videos Using Hybrid Spatio-Temporal Autoencoder,

IEEE DOI 1809
Decoding, Videos, Public transportation, Anomaly detection, Feature extraction, Encoding, Data models, Autoencoder, LSTM, Abnormality Detection BibRef

Mosca, N.[Nicola], Renò, V.[Vito], Marani, R.[Roberto], Nitti, M.[Massimiliano], Martino, F.[Fabio], d'Orazio, T.[Tiziana], Stella, E.[Ettore],
Anomalous Human Behavior Detection Using a Network of RGB-D Sensors,
UHA3DS16(3-14).
Springer DOI 1806
BibRef

Qi, D.[Di], Arfin, J.[Joshua], Zhang, M.X.[Meng-Xue], Mathew, T.[Tushar], Pless, R.[Robert], Juba, B.[Brendan],
Anomaly Explanation Using Metadata,
WACV18(1916-1924)
IEEE DOI 1806
When is data atypical. meta data, security of data, anomalous data, anomaly detection, anomaly explanation, data set, data source, identified anomalies, Webcams 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

Tian, J.[Jing], Chen, L.[Li],
Abnormal motion detection in video using statistics of spatiotemporal local kinematics pattern,
ICIP17(2065-2068)
IEEE DOI 1803
Biomedical measurement, Feature extraction, Histograms, Kinematics, Motion detection, Muscles, Spatiotemporal phenomena, motion classification BibRef

Palomino, N.M.[Neptalí Menejes], Chávez, G.C.[Guillermo Cámara],
Abnormal Event Detection in Video Using Motion and Appearance Information,
CIARP17(382-390).
Springer DOI 1802
BibRef

Prado-Romero, M.A.[Mario Alfonso], Doerr, C.[Christian], Gago-Alonso, A.[Andrés],
Discovering Bitcoin Mixing Using Anomaly Detection,
CIARP17(534-541).
Springer DOI 1802
BibRef

Masoudirad, S.M., Hadadnia, J.,
Anomaly detection in video using two-part sparse dictionary in 170 FPS,
IPRIA17(133-139)
IEEE DOI 1712
feature extraction, image motion analysis, object detection, pedestrians, sensitivity analysis, video coding, Sparse Coding BibRef

Turchini, F.[Francesco], Seidenari, L.[Lorenzo], del Bimbo, A.[Alberto],
Convex Polytope Ensembles for Spatio-Temporal Anomaly Detection,
CIAP17(I:174-184).
Springer DOI 1711
Improve surveillance monitoring. BibRef

Vignesh, K., Yadav, G., Sethi, A.,
Abnormal Event Detection on BMTT-PETS 2017 Surveillance Challenge,
PETS17(2161-2168)
IEEE DOI 1709
Cameras, Feature extraction, Histograms, Support vector machines, Surveillance, Tracking, Videos BibRef

Abuolaim, A.A.[Abdullah A.], Leow, W.K.[Wee Kheng], Varadarajan, J.[Jagannadan], Ahuja, N.[Narendra],
On the Essence of Unsupervised Detection of Anomalous Motion in Surveillance Videos,
CAIP17(I: 160-171).
Springer DOI 1708
BibRef

Munawar, A., Vinayavekhin, P., Magistris, G.D.,
Spatio-Temporal Anomaly Detection for Industrial Robots through Prediction in Unsupervised Feature Space,
WACV17(1017-1025)
IEEE DOI 1609
Clustering algorithms, Feature extraction, Image color analysis, Service robots, Surveillance, Visualization BibRef

Bao, T.L.[Tian-Long], Ding, C.H.[Chun-Hui], Karmoshi, S.[Saleem], Zhu, M.[Ming],
Video Anomaly Detection Based on Adaptive Multiple Auto-Encoders,
ISVC16(II: 83-91).
Springer DOI 1701
BibRef

del Giorno, A.[Allison], Bagnell, J.A.[J. Andrew], Hebert, M.[Martial],
A Discriminative Framework for Anomaly Detection in Large Videos,
ECCV16(V: 334-349).
Springer DOI 1611
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Zhu, Z.P.[Zi-Ping], Wang, J.J.[Jing-Jing], Yu, N.H.[Neng-Hai],
Anomaly detection via 3D-HOF and fast double sparse representation,
ICIP16(286-290)
IEEE DOI 1610
Cameras BibRef

Zhao, Y., Zhou, L., Fu, K.[Keren], Yang, J.[Jie],
Abnormal event detection using spatio-temporal feature and nonnegative locality-constrained linear coding,
ICIP16(3354-3358)
IEEE DOI 1610
Computer vision BibRef

Sarkar, R., Vaccari, A., Acton, S.T.,
SSPARED: Saliency and sparse code analysis for rare event detection in video,
IVMSP16(1-5)
IEEE DOI 1608
Cameras BibRef

Ren, H.M.[Hua-Min], Pan, H., Olsen, S.I.[Søren Ingvor], Jensen, M.B., Moeslund, T.B.[Thomas B.],
An in-depth study of sparse codes on abnormality detection,
AVSS16(66-72)
IEEE DOI 1611
Approximation algorithms 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
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Wen, H.[Hui], Ge, S.M.[Shi-Ming], Chen, S.[Shuixian], 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

Mousavi, H.[Hossein], Nabi, M.[Moin], Galoogahi, H.K.[Hamed Kiani], Perina, A.[Alessandro], Murino, V.[Vittorio],
Abnormality Detection with Improved Histogram of Oriented Tracklets,
CIAP15(II:722-732).
Springer DOI 1511
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Lung, F.B.[Fam Boon], Jaward, M.H.[Mohamed Hisham], Parkkinen, J.[Jussi],
Spatio-temporal descriptor for abnormal human activity detection,
MVA15(471-474)
IEEE DOI 1507
Computational efficiency BibRef

Pathak, D.[Deepak], Sharang, A.[Abhijit], Mukerjee, A.[Amitabha],
Anomaly Localization in Topic-Based Analysis of Surveillance Videos,
WACV15(389-395)
IEEE DOI 1503
Computational modeling BibRef

Mo, X.[Xuan], Monga, V.[Vishal], Bala, R.[Raja],
Simultaneous sparsity model for multi-perspective video anomaly detection,
ICIP14(2314-2318)
IEEE DOI 1502
Encoding BibRef

Li, N.N.[Nan-Nan], Guo, H.W.[Hui-Wen], Xu, D.[Dan], Wu, X.Y.[Xin-Yu],
Multi-Scale Analysis of Contextual Information Within Spatio-Temporal Video Volumes for Anomaly Detection,
ICIP14(2363-2367)
IEEE DOI 1502
Cameras BibRef

Ben Hadf, S.[Saima], Bobin, J.[Jerome], Woiselle, A.[Arnaud],
Blind source separation based anomaly detection in multi-spectral images,
ICIP14(5147-5151)
IEEE DOI 1502
Blind source separation BibRef

Yun, K.[Kimin], Kim, J.[Jiyun], Kim, S.W.[Soo Wan], Jeong, H.[Hawook], Choi, J.Y.[Jin Young],
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ISVC14(I: 698-707).
Springer DOI 1501
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Ren, H.M.[Hua-Min], Moeslund, T.B.[Thomas B.],
Abnormal event detection using local sparse representation,
AVSS14(125-130)
IEEE DOI 1411
Dictionaries BibRef

Biswas, S.[Sovan], Babu, R.V.[R. Venkatesh],
Sparse representation based anomaly detection with enhanced local dictionaries,
ICIP14(5532-5536)
IEEE DOI 1502
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Earlier:
Real time anomaly detection in H.264 compressed videos,
NCVPRIPG13(1-4)
IEEE DOI 1408
Computational modeling. data compression BibRef

Zhang, T., Liu, L., Wiliem, A., Lovell, B.C.,
Is alice chasing or being chased?: Determining subject and object of activities in videos,
WACV16(1-7)
IEEE DOI 1606
Adaptation models 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)
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image motion analysis BibRef

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Standoff video analysis for the detection of security anomalies in vehicles,
AIPR10(1-8).
IEEE DOI 1010
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Hierarchical activity discovery within spatio-temporal context for video anomaly detection,
ICIP13(3597-3601)
IEEE DOI 1402
Visual surveillance BibRef

Wang, C.[Can], Liu, H.[Hong],
Unusual events detection based on multi-dictionary sparse representation using kinect,
ICIP13(2968-2972)
IEEE DOI 1402
Anomaly Detection; Kinect; Sparse Representation BibRef

Yuan, F.[Fei], Tang, C.[Chu], Tian, S.[Shu], Hao, H.W.[Hong-Wei],
A Framework for Quick and Accurate Access of Interesting Visual Events in Surveillance Videos,
ISVC13(II:168-177).
Springer DOI 1311
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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)
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Earlier:
Anomalous Event Detection Using a Semi-Two Dimensional Hidden Markov Model,
DICTA12(1-7).
IEEE DOI 1303
Gaussian processes BibRef

Lin, C.C.[Chung-Ching], Pankanti, S., Smith, J.,
Accurate coverage summarization of UAV videos,
AIPR14(1-5)
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Event summarys to determine whether to look at them. aerospace computing BibRef

Trinh, H.[Hoang], Li, J.[Jun], Miyazawa, S.[Sachiko], Moreno, J.[Juan], Pankanti, S.[Sharath],
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ICPR12(2226-2229).
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Tao, Y.[Yisi], Chen, Y.Z.[Yuan-Zhe], Lin, W.Y.[Wei-Yao], Han, X.T.[Xin-Tong], Li, H.X.[Hong-Xiang], Lu, Z.[Zheng],
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PETS13(45-52)
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Histograms of Optical Flow Orientation for Visual Abnormal Events Detection,
AVSS12(13-18).
IEEE DOI 1211
object detection BibRef

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Video anomaly detection based on local statistical aggregates,
CVPR12(2112-2119).
IEEE DOI 1208
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Less Is More: Video Trimming for Action Recognition,
HACI13(515-521)
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image classification BibRef

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

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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],
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Hommes, S., State, R., Zinnen, A., Engel, T.,
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Rolland, P., Krebs, W.K., Burger, A.,
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AVSBS11(325-330).
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Chockalingam, T.[Thiyagarajan], Emonet, R.[Remi], Odobez, J.M.[Jean-Marc],
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Multi-camera open space human activity discovery for anomaly detection,
AVSBS11(218-223).
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Jouneau, E.[Erwan], Carincotte, C.[Cyril],
Particle-based tracking model for automatic anomaly detection,
ICIP11(513-516).
IEEE DOI 1201
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Earlier:
Mono versus Multi-view tracking-based model for automatic scene activity modeling and anomaly detection,
AVSBS11(95-100).
IEEE DOI 1111
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Markov random fields for abnormal behavior detection on highways,
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Panoramic Background Generation and Abnormal Behavior Detection in PTZ Camera Networks,
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Holzer, P.[Peter], Pinz, A.[Axel],
Mobile Surveillance by 3D-Outlier Analysis,
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Aghazadeh, O.[Omid], Sullivan, J.[Josephine], Carlsson, S.[Stefan],
Novelty detection from an ego-centric perspective,
CVPR11(3297-3304).
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Cui, X.[Xinyi], Liu, Q.S.[Qing-Shan], Gao, M.C.[Ming-Chen], Metaxas, D.N.[Dimitris N.],
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Li, L.J.[Li-Jia], Zhu, J.[Jun], Su, H.[Hao], Xing, E.P.[Eric P.], Fei-Fei, L.[Li],
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Zhao, B.[Bin], Fei-Fei, L.[Li], Xing, E.P.[Eric P.],
Online detection of unusual events in videos via dynamic sparse coding,
CVPR11(3313-3320).
IEEE DOI 1106
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Al-Khateeb, H.[Hussein], Petrou, M.[Maria],
An extended fuzzy SOM for anomalous behaviour detection,
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IEEE DOI 1106
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Identifying Surprising Events in Videos Using Bayesian Topic Models,
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Chang, C.W.[Chueh-Wei], Yang, T.H.[Ti-Hua], Tsao, Y.Y.[Yu-Yu],
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Local Abnormality Detection in Video Using Subspace Learning,
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Anomalous Behaviour Detection Using Spatiotemporal Oriented Energies, Subset Inclusion Histogram Comparison and Event-Driven Processing,
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ACIVS09(389-399).
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Who's Counting? Real-Time Blackjack Monitoring for Card Counting Detection,
CVS09(354-363).
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Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates,
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Abnormal Behavior Recognition Using Self-Adaptive Hidden Markov Models,
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Real-Time Adaptive Camera Tamper Detection for Video Surveillance,
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Zhou, H.N.[Han-Ning], Kimber, D.[Don],
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ICPR06(III: 1161-1166).
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Yu, E.[Elden], Aggarwal, J.K.,
Human action recognition with extremities as semantic posture representation,
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Yu, E.[Elden], Aggarwal, J.K.,
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Detection of Fence Climbing from Monocular Video,
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extended star-skeleton representation, stable contacts are formed by stationary extreme points. BibRef

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AVSS12(1-6).
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A Principled Approach to Detecting Surprising Events in Video,
CVPR05(I: 631-637).
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Zhong, H.[Hua], Shi, J.B.[Jian-Bo], Visontai, M.,
Detecting unusual activity in video,
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Dee, H.M., Hogg, D.C.,
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Earlier:
Detecting inexplicable behaviour,
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Chan, M.T.[Michael T.], Hoogs, A.J.[Anthony J.], Bhotika, R.[Rahul], Perera, A.[Amitha], Schmiederer, J.[John], Doretto, G.[Gianfranco],
Joint Recognition of Complex Events and Track Matching,
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Chan, M.T.[Michael T.], Hoogs, A.J.[Anthony J.], Sun, Z.H.[Zhao-Hui], Schmiederer, J.[John], Bhotika, R.[Rahul], Doretto, G.[Gianfranco],
Event Recognition with Fragmented Object Tracks,
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Chan, M.T.[Michael T.], Hoogs, A.J.[Anthony J.], Schmiederer, J.[John], Petersen, M.,
Detecting rare events in video using semantic primitives with HMM,
ICPR04(IV: 150-154).
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Apply analysis of stereotypical patterns of motion. BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Deep Learning for Detecting Anomalies .