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

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

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

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

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

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

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

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

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

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.
DOI Link 1808
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
BibRef

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
BibRef

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
BibRef
Earlier: A1, A2, A4, Only:
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

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), 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
BibRef

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.
WWW Link. 1911
BibRef

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

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.
Elsevier DOI 2005
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.
Elsevier DOI 2006
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.
WWW Link. 2007
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.
Elsevier DOI 2008
Anomaly detection, Encoding-decoding networks, Sparse coding networks, Spatial-temporal information, Video representation 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

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,
PRL(140), 2020, pp. 88-94.
Elsevier DOI 2012
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,
JVCIR(75), 2021, pp. 103047.
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
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
BibRef
Earlier: A1, A3, A4, A5, Only:
MVTec AD: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection,
CVPR19(9584-9592).
IEEE DOI 2002
BibRef

Yahaya, S.W.[Salisu Wada], Lotfi, A.[Ahmad], Mahmud, M.[Mufti],
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],
A study on attention-based LSTM for abnormal behavior recognition with variable pooling,
IVC(108), 2021, pp. 104120.
Elsevier DOI 2104
Abnormal behavior, Attention, LSTM, Variable pooling BibRef

Lv, H.[Hui], Zhou, C.W.[Chuan-Wei], Cui, Z.[Zhen], Xu, C.Y.[Chun-Yan], Li, Y.[Yong], Yang, J.[Jian],
Localizing Anomalies From Weakly-Labeled Videos,
IP(30), 2021, pp. 4505-4515.
IEEE DOI 2105
Videos, Anomaly detection, Location awareness, Semantics, Detectors, Training, Benchmark testing, Anomaly detection, traffic anomaly dataset 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

Sreenivasan, S.C.[Sreeram C.], Bhashyam, S.[Srikrishna],
Sequential Nonparametric Detection of Anomalous Data Streams,
SPLetters(28), 2021, pp. 932-936.
IEEE DOI 2106
Kernel, Frequency selective surfaces, Error probability, Testing, Search problems, Measurement, Limiting, Anomaly detection, outlier detection BibRef

Kavousi-Fard, A.[Abdollah], Dabbaghjamanesh, M.[Morteza], Jin, T.[Tao], Su, W.[Wencong], Roustaei, M.[Mahmoud],
An Evolutionary Deep Learning-Based Anomaly Detection Model for Securing Vehicles,
ITS(22), No. 7, July 2021, pp. 4478-4486.
IEEE DOI 2107
Generative adversarial networks, Generators, Protocols, Automobiles, Anomaly detection, Deep learning, Cyberattack, firefly algorithm BibRef

Wan, S.H.[Shao-Hua], Xu, X.L.[Xiao-Long], Wang, T.[Tian], Gu, Z.H.[Zong-Hua],
An Intelligent Video Analysis Method for Abnormal Event Detection in Intelligent Transportation Systems,
ITS(22), No. 7, July 2021, pp. 4487-4495.
IEEE DOI 2107
Streaming media, Semantics, Cameras, Natural languages, Image segmentation, Intelligent transportation systems, Safety, question-answering 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

Wu, J.C.[Jhih-Ciang], Lu, S.[Sherman], Fuh, C.S.[Chiou-Shann], Liu, T.L.[Tyng-Luh],
One-class anomaly detection via novelty normalization,
CVIU(210), 2021, pp. 103226.
Elsevier DOI 2109
Deep learning, Anomaly detection, Unsupervised learning, Convolutional neural network BibRef

Bahrami, M.[Maedeh], Pourahmadi, M.[Majid], Vafaei, A.[Abbas], Shayesteh, M.R.[Mohammad Reza],
A comparative study between single and multi-frame anomaly detection and localization in recorded video streams,
JVCIR(79), 2021, pp. 103232.
Elsevier DOI 2109
Anomaly detection, Deep learning, Convolutional autoencoder, Image reconstruction BibRef

Zhang, Y.[Yu], Nie, X.[Xiushan], He, R.[Rundong], 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.[Yujin], 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

Roy, P.R.[Pankaj Raj], Bilodeau, G.A.[Guillaume-Alexandre], Seoud, L.[Lama],
Predicting Next Local Appearance for Video Anomaly Detection,
MVA21(1-5)
DOI Link 2109
Training, Benchmark testing, Anomaly detection, Videos BibRef

Jain, Y.[Yashswi], Sharma, A.K.[Ashvini Kumar], Velmurugan, R.[Rajbabu], Banerjee, B.[Biplab],
PoseCVAE: Anomalous Human Activity Detection,
ICPR21(2927-2934)
IEEE DOI 2105
Training, Stochastic processes, Training data, Coherence, Trajectory, Pattern recognition, Decoding, Stochastic Generative Models, Pose Trajectory BibRef

Orrù, G.[Giulia], Ghiani, D.[Davide], Pintor, M.[Maura], Marcialis, G.L.[Gian Luca], Roli, F.[Fabio],
Detecting Anomalies from Video-Sequences: a Novel Descriptor,
ICPR21(4642-4649)
IEEE DOI 2105
Measurement units, Dynamics, Benchmark testing, Pattern recognition, Anomaly detection BibRef

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

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

Leveni, F.[Filippo], Magri, L.[Luca], Boracchi, G.[Giacomo], Alippi, C.[Cesare],
PIF: Anomaly detection via preference embedding,
ICPR21(8077-8084)
IEEE DOI 2105
Pattern recognition, Anomaly detection BibRef

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

Ivanovska, M.[Marija], Perš, J.[Janez], Tabernik, D.[Domen], Skocaj, D.[Danijel],
Evaluation of Anomaly Detection Algorithms for the Real-World Applications,
ICPR21(6196-6203)
IEEE DOI 2105
Measurement, Training, Satellites, Computational modeling, Manuals, Rendering (computer graphics) BibRef

Montulet, R.[Rico], Briassouli, A.[Alexia],
Densely Annotated Photorealistic Virtual Dataset Generation for Abnormal Event Detection,
MLCSA20(5-19).
Springer DOI 2103
BibRef

Defard, T.[Thomas], Setkov, A.[Aleksandr], Loesch, A.[Angelique], Audigier, R.[Romaric],
Padim: A Patch Distribution Modeling Framework for Anomaly Detection and Localization,
IML20(475-489).
Springer DOI 2103
BibRef

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
BibRef

Yi, J.[Jihun], Yoon, S.[Sungroh],
Patch SVDD: Patch-level Svdd for Anomaly Detection and Segmentation,
ACCV20(VI:375-390).
Springer DOI 2103
BibRef

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).
Springer DOI 2011
Inspection, surveillance. 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
BibRef

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

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

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.,
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

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

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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

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

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

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

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
BibRef

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

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
BibRef

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

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
BibRef
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

Srivastava, S.[Satyam], Delp, E.J.[Edward J.],
Standoff video analysis for the detection of security anomalies in vehicles,
AIPR10(1-8).
IEEE DOI 1010
BibRef

Xu, D.[Dan], Wu, X.Y.[Xin-Yu], Song, D.Z.[De-Zhen], Li, N.N.[Nan-Nan], Chen, Y.L.[Yen-Lun],
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
BibRef

Lin, C.C.[Chung-Ching], Pankanti, S., Smith, J.,
Accurate coverage summarization of UAV videos,
AIPR14(1-5)
IEEE DOI 1504
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],
Efficient UAV video event summarization,
ICPR12(2226-2229).
WWW Link. 1302
BibRef

Nguyen, T.V.[Tien Vu], Phung, D.Q.[Dinh Q.], Rana, S.[Santu], Pham, D.S.[Duc Son], Venkatesh, S.[Svetha],
Multi-modal abnormality detection in video with unknown data segmentation,
ICPR12(1322-1325).
WWW Link. 1302
BibRef

Feng, J.[Jie], Zhang, C.[Chao], Hao, P.W.[Peng-Wei],
Online anomaly detection in videos by clustering dynamic exemplars,
ICIP12(3097-3100).
IEEE DOI 1302
BibRef

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],
A patch-based framework for detecting abnormal activities with a PTZ camera,
VCIP12(1-6).
IEEE DOI 1302
BibRef

Wang, T.[Tian], Snoussi, H.[Hichem],
Histograms of optical flow orientation for abnormal events detection,
PETS13(45-52)
IEEE DOI 1411
BibRef
Earlier:
Histograms of Optical Flow Orientation for Visual Abnormal Events Detection,
AVSS12(13-18).
IEEE DOI 1211
object detection BibRef

Ito, Y.[Yuichi], Kitani, K.M.[Kris M.], Bagnell, J.A.[James A.], Hebert, M.[Martial],
Detecting Interesting Events Using Unsupervised Density Ratio Estimation,
ARTEMIS12(III: 151-161).
Springer DOI 1210
BibRef

Saligrama, V.[Venkatesh], Chen, Z.[Zhu],
Video anomaly detection based on local statistical aggregates,
CVPR12(2112-2119).
IEEE DOI 1208
BibRef

Antic, B.[Borislav], Milbich, T., Ommer, B.[Bjorn],
Less Is More: Video Trimming for Action Recognition,
HACI13(515-521)
IEEE DOI 1403
image classification BibRef

Hommes, S., State, R., Zinnen, A., Engel, T.,
Detection of abnormal behaviour in a surveillance environment using control charts,
AVSBS11(113-118).
IEEE DOI 1111
BibRef

Chang, H.J.[Hyung Jin], Kim, J.[Jiyun], Cho, J.C.[Jung-Chan], Oh, S.H.[Song-Hwai], Yi, K.[Kwang], Choi, J.Y.[Jin Young],
Action Chart: A Representation for Efficient Recognition of Complex Activity,
BMVC13(xx-yy).
DOI Link 1402
BibRef

Rolland, P., Krebs, W.K., Burger, A.,
Naturalistic data sets for image and behavior analysis: 'normal' versus 'anomalous' events,
AVSBS11(325-330).
IEEE DOI 1111
BibRef

Chockalingam, T.[Thiyagarajan], Emonet, R.[Remi], Odobez, J.M.[Jean-Marc],
Localized anomaly detection via hierarchical integrated activity discovery,
AVSS13(51-56)
IEEE DOI 1311
Cameras BibRef

Emonet, R.[Rémi], Varadarajan, J.[Jagannadan], Odobez, J.M.[Jean-Marc],
Multi-camera open space human activity discovery for anomaly detection,
AVSBS11(218-223).
IEEE DOI 1111
BibRef

Jouneau, E.[Erwan], Carincotte, C.[Cyril],
Particle-based tracking model for automatic anomaly detection,
ICIP11(513-516).
IEEE DOI 1201
BibRef
Earlier:
Mono versus Multi-view tracking-based model for automatic scene activity modeling and anomaly detection,
AVSBS11(95-100).
IEEE DOI 1111
BibRef

Bouttefroy, P.L.M., Beghdadi, A., Bouzerdoum, A., Phung, S.L.,
Markov random fields for abnormal behavior detection on highways,
EUVIP10(149-154).
IEEE DOI 1110
BibRef

Cho, S.H.[Sang-Hyun], Kang, H.B.[Hang-Bong],
Panoramic Background Generation and Abnormal Behavior Detection in PTZ Camera Networks,
ISVC11(I: 748-757).
Springer DOI 1109
BibRef

Holzer, P.[Peter], Pinz, A.[Axel],
Mobile Surveillance by 3D-Outlier Analysis,
VS10(195-204).
Springer DOI 1109
BibRef

Aghazadeh, O.[Omid], Sullivan, J.[Josephine], Carlsson, S.[Stefan],
Novelty detection from an ego-centric perspective,
CVPR11(3297-3304).
IEEE DOI 1106
Chest mounted camera while doing routine tasks, compare to previous sequences. BibRef

Cui, X.[Xinyi], Liu, Q.S.[Qing-Shan], Gao, M.C.[Ming-Chen], Metaxas, D.N.[Dimitris N.],
Abnormal detection using interaction energy potentials,
CVPR11(3161-3167).
IEEE DOI 1106
BibRef

Li, L.J.[Li-Jia], Zhu, J.[Jun], Su, H.[Hao], Xing, E.P.[Eric P.], Fei-Fei, L.[Li],
Multi-Level Structured Image Coding on High-Dimensional Image Representation,
ACCV12(II:147-161).
Springer DOI 1304
BibRef

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
BibRef

Al-Khateeb, H.[Hussein], Petrou, M.[Maria],
An extended fuzzy SOM for anomalous behaviour detection,
CVCG11(31-36).
IEEE DOI 1106
BibRef

Hendel, A.[Avishai], Weinshall, D.[Daphna], Peleg, S.[Shmuel],
Identifying Surprising Events in Videos Using Bayesian Topic Models,
ACCV10(III: 448-459).
Springer DOI 1011
BibRef

Barr, J.R.[Jeremiah R.], Bowyer, K.W.[Kevin W.], Flynn, P.J.[Patrick J.],
Detecting questionable observers using face track clustering,
WACV11(182-189).
IEEE DOI 1101
Who appears too often. Tracking and recognizing. BibRef

Petrás, I.[István], Beleznai, C.[Csaba], Dedeoglu, Y.[Yigithan], Pardàs, M.[Montse], Kovács, L.[Levente], Szlávik, Z.[Zoltán], Havasi, L.[László], Szirányi, T.[Tamás], Töreyin, B.U.[B. Ugur], Güdükbay, U.[Ugur], Çetin, A.E.[A. Enis], Canton-Ferrer, C.[Cristian],
Flexible test-bed for unusual behavior detection,
CIVR07(105-108).
DOI Link 0707
BibRef

Chang, C.W.[Chueh-Wei], Yang, T.H.[Ti-Hua], Tsao, Y.Y.[Yu-Yu],
Abnormal Spatial Event Detection and Video Content Searching in a Multi-Camera Surveillance System,
MVA09(170-).
PDF File. 0905
BibRef

Shi, Y.H.[Ying-Huan], Gao, Y.[Yang], Wang, R.[Ruili],
Real-Time Abnormal Event Detection in Complicated Scenes,
ICPR10(3653-3656).
IEEE DOI 1008
BibRef

Yuen, J.[Jenny], Torralba, A.B.[Antonio B.],
A Data-Driven Approach for Event Prediction,
ECCV10(II: 707-720).
Springer DOI 1009
To find unusual events in large collection of short videos. BibRef

Zaharescu, A.[Andrei], Wildes, R.P.[Richard P.],
Spatiotemporal Salience via Centre-Surround Comparison of Visual Spacetime Orientations,
ACCV12(III:533-546).
Springer DOI 1304
BibRef
Earlier:
Anomalous Behaviour Detection Using Spatiotemporal Oriented Energies, Subset Inclusion Histogram Comparison and Event-Driven Processing,
ECCV10(I: 563-576).
Springer DOI 1009
BibRef

Breitenstein, M.D.[Michael D.], Grabner, H.[Helmut], Van Gool, L.J.[Luc J.],
Hunting Nessie: Real-time abnormality detection from webcams,
VS09(1243-1250).
IEEE DOI 0910
BibRef

Li, J.[Jian], Gong, S.G.[Shao-Gang], Xiang, T.[Tao],
On-the-fly global activity prediction and anomaly detection,
VS09(1330-1337).
IEEE DOI 0910
BibRef

Nater, F.[Fabian], Grabner, H.[Helmut], Jaeggli, T.[Tobias], Van Gool, L.J.[Luc J.],
Tracker trees for unusual event detection,
VS09(1113-1120).
IEEE DOI 0910
BibRef

Matilainen, M.[Matti], Barnard, M.[Mark], Silvén, O.[Olli],
Unusual Activity Recognition in Noisy Environments,
ACIVS09(389-399).
Springer DOI 0909
BibRef

Zutis, K.[Krists], Hoey, J.[Jesse],
Who's Counting? Real-Time Blackjack Monitoring for Card Counting Detection,
CVS09(354-363).
Springer DOI 0910
Detect anomalous playing patterns. BibRef

Ivanov, I.[Ivan], DuFaux, F.[Frederic], Ha, T.M.[Thien M.], Ebrahimi, T.[Touradj],
Towards Generic Detection of Unusual Events in Video Surveillance,
AVSBS09(61-66).
IEEE DOI 0909
BibRef

Kim, J.[Jaechul], Grauman, K.[Kristen],
Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates,
CVPR09(2921-2928).
IEEE DOI 0906
BibRef

Yu, T.H.[Tsz-Ho], Moon, Y.S.[Yiu-Sang],
Unsupervised Abnormal Behavior Detection for Real-Time Surveillance Using Observed History,
MVA09(166-).
PDF File. 0905
BibRef
And:
Unsupervised Real-Time Unusual Behavior Detection for Biometric-Assisted Visual Surveillance,
ICB09(1019-1029).
Springer DOI 0906
BibRef

Yin, J.[Jun], Meng, Y.[Yan],
Abnormal Behavior Recognition Using Self-Adaptive Hidden Markov Models,
ICIAR09(337-346).
Springer DOI 0907
BibRef

Reif, M.[Matthias], Goldstein, M.[Markus], Stahl, A.[Armin], Breuel, T.M.[Thomas M.],
Anomaly detection by combining decision trees and parametric densities,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Iwai, Y.[Yoshio],
A Framework for Suspicious Action Detection with Mixture Distributions of Action Primitives,
PSIVT09(519-530).
Springer DOI 0901
BibRef

Zhou, J.Q.[Jun-Qiang], Ntafos, S.[Simeon], Prabhakaran, B.[Balakrishnan],
Fault Detection Framework for Video Surveillance Systems,
AVSBS08(219-226).
IEEE DOI 0809
BibRef

Zou, X.T.[Xiao-Tao], Bhanu, B.[Bir],
Anomalous activity classification in the distributed camera network,
ICIP08(781-784).
IEEE DOI 0810
BibRef

Goshorn, R.[Rachel], Goshorn, D.[Deborah], Goshorn, J.[Joshua], Goshorn, L.[Lawrence],
Abnormal behavior-detection using sequential syntactical classification in a network of clustered cameras,
ICDSC08(1-10).
IEEE DOI 0809
BibRef

Goshorn, R.[Rachel], Goshorn, J.[Joshua], Goshorn, D.[Deborah], Aghajan, H.,
Architecture for Cluster-Based Automated Surveillance Network for Detecting and Tracking Multiple Persons,
ICDSC07(219-226).
IEEE DOI 0709
BibRef

Zelniker, E.E.[Emanuel E.], Gong, S.G.[Shao-Gang], Xiang, T.[Tao],
Global Abnormal Behaviour Detection Using a Network of CCTV Cameras,
VS08(xx-yy). 0810
BibRef

Cardinaux, F.[Fabien], Brownsell, S.[Simon], Hawley, M.[Mark], Bradley, D.[David],
Modelling of Behavioural Patterns for Abnormality Detection in the Context of Lifestyle Reassurance,
CIARP08(243-25).
Springer DOI 0809
BibRef

Russell, D.M.[David M.], Gong, S.G.[Shao-Gang],
Multi-layered Decomposition of Recurrent Scenes,
ECCV08(III: 574-587).
Springer DOI 0810
BibRef
Earlier:
Exploiting Periodicity in Recurrent Scenes,
BMVC08(xx-yy).
PDF File. 0809
E.g. road intersections. BibRef

Dickinson, P.[Patrick], Hunter, A.[Andrew],
Using Inactivity to Detect Unusual behavior,
Motion08(1-6).
IEEE DOI 0801
BibRef

Pritch, Y.[Yael], Rav-Acha, A.[Alex], Gutman, A.[Avital], Peleg, S.[Shmuel],
Webcam Synopsis: Peeking Around the World,
ICCV07(1-8).
IEEE DOI 0710
A short version that contains only those parts where something happens. Generate the action based description. BibRef

Reulke, R.[Ralf], Meysel, F.[Frederik], Bauer, S.[Sascha],
Situation Analysis and Atypical Event Detection with Multiple Cameras and Multi-Object Tracking,
RobVis08(234-247).
Springer DOI 0802
BibRef

Saglam, A.[Ali], Temizel, A.[Alptekin],
Real-Time Adaptive Camera Tamper Detection for Video Surveillance,
AVSBS09(430-435).
IEEE DOI 0909
BibRef

Aksay, A.[Anil], Temizel, A.[Alptekin], Cetin, A.E.[A. Enis],
Camera Tamper Detection Using Wavelet Analysis for Video Surveillance,
AVSBS07(558-562).
IEEE DOI 0709
BibRef

Izo, T.[Tomas], Grimson, W.E.L.[W. Eric L.],
Unsupervised Modeling of Object Tracks for Fast Anomaly Detection,
ICIP07(IV: 529-532).
IEEE DOI 0709
BibRef

Irani, M.[Michal],
Seeing the Invisible and Predicting the Unexpected,
IbPRIA07(I: 7-8).
Springer DOI 0706
BibRef

Salas, J.[Joaquin], Jimenez-Hernandez, H.[Hugo], Gonzalez-Barbosa, J.J.[Jose-Joel], Hurtado-Ramos, J.B.[Juan B.], Canchola, S.[Sandra],
A Double Layer Background Model to Detect Unusual Events,
ACIVS07(406-416).
Springer DOI 0708
BibRef

Cui, P.[Peng], Sun, L.F.[Li-Feng], Liu, Z.Q.[Zhi-Qiang], Yang, S.Q.[Shi-Qiang],
A Sequential Monte Carlo Approach to Anomaly Detection in Tracking Visual Events,
VS07(1-8).
IEEE DOI 0706
BibRef

O'Callaghan, R.[Robert], Haga, T.[Tetsuji],
Robust Change-Detection by Normalised Gradient-Correlation,
VS07(1-8).
IEEE DOI 0706
BibRef

Lin, D.T.[Daw-Tung], Liu, M.J.[Ming-Ju],
Face Occlusion Detection for Automated Teller Machine Surveillance,
PSIVT06(641-651).
Springer DOI 0612
BibRef

Branzan Albu, A.[Alexandra], Beugeling, T.[Trevor], Virji-Babul, N.[Naznin], Beach, C.[Cheryl],
Analysis of Irregularities in Human Actions with Volumetric Motion History Images,
Motion07(16-16).
IEEE DOI 0702
BibRef

Gaucel, J.M.[Jean-Michel], Guillaume, M.[Mireille], Bourennane, S.[Salah],
Non Orthogonal Component Analysis: Application to Anomaly Detection,
ACIVS06(1198-1209).
Springer DOI 0609
BibRef

Au, C.E.[Carmen E.], Skaff, S.[Sandra], Clark, J.J.[James J.],
Anomaly Detection for Video Surveillance Applications,
ICPR06(IV: 888-891).
IEEE DOI 0609
BibRef

Zhou, H.N.[Han-Ning], Kimber, D.[Don],
Unusual Event Detection via Multi-camera Video Mining,
ICPR06(III: 1161-1166).
IEEE DOI 0609
BibRef

Yu, E.[Elden], Aggarwal, J.K.,
Human action recognition with extremities as semantic posture representation,
SLAM09(1-8).
IEEE DOI 0906
BibRef

Yu, E.[Elden], Aggarwal, J.K.,
Detection of stable contacts for human motion analysis,
VSSN06(87-94).
WWW Link. 0701
BibRef
And:
Detection of Fence Climbing from Monocular Video,
ICPR06(I: 375-378).
IEEE DOI 0609
extended star-skeleton representation, stable contacts are formed by stationary extreme points. BibRef

Voorhies, R.C.[Randolph C.], Elazary, L.[Lior], Itti, L.[Laurent],
Neuromorphic Bayesian Surprise for Far-Range Event Detection,
AVSS12(1-6).
IEEE DOI 1211
BibRef

Itti, L.[Laurent], Baldi, P.[Pierre],
A Principled Approach to Detecting Surprising Events in Video,
CVPR05(I: 631-637).
IEEE DOI 0507
BibRef

Zhong, H.[Hua], Shi, J.B.[Jian-Bo], Visontai, M.,
Detecting unusual activity in video,
CVPR04(II: 819-826).
IEEE DOI 0408
BibRef

Dee, H.M., Hogg, D.C.,
On the feasibility of using a cognitive model to filter surveillance data,
AVSBS05(34-39).
IEEE DOI 0602
BibRef
Earlier:
Detecting inexplicable behaviour,
BMVC04(xx-yy).
HTML Version. 0508
BibRef

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,
CVPR06(II: 1615-1622).
IEEE DOI 0606
BibRef

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,
ICPR06(I: 412-416).
IEEE DOI 0609
BibRef

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).
IEEE DOI 0409
BibRef

Zhong, H.[Hua], Shi, J.B.[Jian-Bo],
Finding (Un)Usual Events in Video,
CMU-RI-TR-03-05, May, 2003.
HTML Version. 0306
BibRef

Mori, H., Ishiguro, H., Kotani, S., Yasutomi, S., Chino, Y.,
A mobile robot strategy applied to Harunobu-4,
ICPR88(I: 525-530).
IEEE DOI 8811
Apply analysis of stereotypical patterns of motion. BibRef

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