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

Piciarelli, C.[Claudio], Foresti, G.L.[Gian Luca],
On-line trajectory clustering for anomalous events detection,
PRL(27), No. 15, November 2006, pp. 1835-1842.
Elsevier DOI 0609
trajectory clustering; On-line clustering; Behaviour analysis BibRef

Piciarelli, C.[Claudio], Micheloni, C.[Christian], Foresti, G.L.[Gian Luca],
Trajectory-Based Anomalous Event Detection,
CirSysVideo(18), No. 11, November 2008, pp. 1544-1554.
IEEE DOI 0811
BibRef
And:
Anomalous trajectory patterns detection,
ICPR08(1-4).
IEEE DOI 0812
BibRef
And:
Support vector machines for robust trajectory clustering,
ICIP08(2540-2543).
IEEE DOI 0810
BibRef
And:
Kernel-based unsupervised trajectory clusters discovery,
VS08(xx-yy). 0810
BibRef
Earlier:
An Autonomous Surveillance Vehicle for People Tracking,
CIAP05(1140-1147).
Springer DOI 0509
See also Detecting moving people in video streams. BibRef

Piciarelli, C.[Claudio], Foresti, G.L.[Gian Luca],
Surveillance-Oriented Event Detection in Video Streams,
IEEE_Int_Sys(26), No. 3, May-June 2011, pp. 32-41.
IEEE DOI 1107
BibRef
Earlier:
Anomalous trajectory detection using support vector machines,
AVSBS07(153-158).
IEEE DOI 0709
Use Explicit event analysis or Anomaly detection. BibRef

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

Hu, W.M.[Wei-Ming], Xiao, X.J.[Xue-Juan], Fu, Z.Y.[Zhou-Yu], Xie, D., Tan, T.N.[Tie-Niu], Maybank, S.J.[Steve J.],
A System for Learning Statistical Motion Patterns,
PAMI(28), No. 9, September 2006, pp. 1450-1464.
IEEE DOI 0608
Learn patterns for anomaly detection and prediction of behaviors. Track, then learn patterns of trajectories. Detect anomalies. Some comparisons with others: See also Learning the Distribution of Object Trajectories for Event Recognition. See also Learning Spatio-temporal Patterns for Predicting Object Behaviour. See also Learning Semantic Scene Models From Observing Activity in Visual Surveillance. See also Multi feature path modeling for video surveillance. (these do not use probability distributions on the motion patterns) See also Learning Patterns of Activity Using Real-Time Tracking. See also Application of the Self-Organizing Map to Trajectory Classification. See also Utilizing Learned Motion Patterns to Robustly Track Persons. 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

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

Jiang, F.[Fan], Wu, Y.[Ying], Katsaggelos, A.K.[Aggelos K.],
A Dynamic Hierarchical Clustering Method for Trajectory-Based Unusual Video Event Detection,
IP(18), No. 4, April 2009, pp. 907-913.
IEEE DOI 0903
BibRef
Earlier:
Abnormal Event Detection from Surveillance Video by Dynamic Hierarchical Clustering,
ICIP07(V: 145-148).
IEEE DOI 0709
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],
Motion-based behaviour learning, profiling and classification in the presence of anomalies,
PR(43), No. 1, January 2010, pp. 173-186,.
Elsevier DOI 0909
Object trajectory; Dimensionality reduction; Trajectory modelling; Trajectory clustering; Event mining; Anomaly detection; Motion recognition BibRef

Khalid, S.[Shehzad],
Activity classification and anomaly detection using m-mediods based modelling of motion patterns,
PR(43), No. 10, October 2010, pp. 3636-3647.
Elsevier DOI 1007
Object trajectory; Dimensionality reduction; Trajectory modelling; Event mining; Anomaly detection; Motion recognition 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

Tung, F.[Frederick], Zelek, J.S.[John S.], Clausi, D.A.[David A.],
Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance,
IVC(29), No. 4, March 2011, pp. 230-240.
Elsevier DOI 1102
Video surveillance; Behaviour understanding; Trajectory analysis; Anomaly detection 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

Wiliem, A.[Arnold], Madasu, V.[Vamsi], Boles, W.[Wageeh], Yarlagadda, P.[Prasad],
A suspicious behaviour detection using a context space model for smart surveillance systems,
CVIU(116), No. 2, February 2012, pp. 194-209.
Elsevier DOI 1201
BibRef
Earlier:
An Update-Describe Approach for Human Action Recognition in Surveillance Video,
DICTA10(270-275).
IEEE DOI 1012
BibRef
Earlier:
A Context-Based Approach for Detecting Suspicious Behaviours,
DICTA09(146-153).
IEEE DOI 0912
BibRef
Earlier:
Detecting Uncommon Trajectories,
DICTA08(398-404).
IEEE DOI 0812
Suspicious behaviour; Context; Surveillance system; Security 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

Chen, C., Zhang, D., Castro, P.S., Li, N., Sun, L., Li, S., Wang, Z.,
iBOAT: Isolation-Based Online Anomalous Trajectory Detection,
ITS(14), No. 2, 2013, pp. 806-818.
IEEE DOI Global Positioning System; Roads; Trajectory; Anomalous trajectory detection 1307
BibRef

Yang, W.Q.[Wan-Qi], Gao, Y.[Yang], Cao, L.B.[Long-Bing],
TRASMIL: A local anomaly detection framework based on trajectory segmentation and multi-instance learning,
CVIU(117), No. 10, 2013, pp. 1273-1286.
Elsevier DOI 1309
Local anomaly detection 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

Laxhammar, R., Falkman, G.,
Online Learning and Sequential Anomaly Detection in Trajectories,
PAMI(36), No. 6, June 2014, pp. 1158-1173.
IEEE DOI 1406
Algorithm design and analysis 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

Kang, K.[Kai], Liu, W.B.[Wei-Bin], Xing, W.W.[Wei-Wei],
Motion Pattern Study and Analysis from Video Monitoring Trajectory,
IEICE(E97-D), No. 6, June 2014, pp. 1574-1582.
WWW Link. 1407
Abnormality detection. BibRef

Wan, Y.[Yiwen], Yang, T.I.[Tze-I], Keathly, D., Buckles, B.,
Dynamic scene modelling and anomaly detection based on trajectory analysis,
IET-ITS(8), No. 6, September 2014, pp. 526-533.
DOI Link 1411
pattern clustering BibRef

Huang, D.Y.[Deng-Yuan], Chen, C.H.[Chao-Ho], Chen, T.Y.[Tsong-Yi], Hu, W.C.[Wu-Chih], Chen, B.C.[Bo-Cin],
Rapid detection of camera tampering and abnormal disturbance for video surveillance system,
JVCIR(25), No. 8, 2014, pp. 1865-1877.
Elsevier DOI 1411
Camera tampering BibRef

Huang, D.Y.[Deng-Yuan], Chen, C.H.[Chao-Ho], Chen, T.Y.[Tsong-Yi], Hu, W.C.[Wu-Chih], Feng, K.W.[Kai-Wei],
Vehicle detection and inter-vehicle distance estimation using single-lens video camera on urban/suburb roads,
JVCIR(46), No. 1, 2017, pp. 250-259.
Elsevier DOI 1706
Vehicle, detection BibRef

Hu, W.C.[Wu-Chih], Chen, C.H.[Chao-Ho], Chen, T.Y.[Tsong-Yi], Huang, D.Y.[Deng-Yuan], Wu, Z.C.[Zong-Che],
Moving object detection and tracking from video captured by moving camera,
JVCIR(30), No. 1, 2015, pp. 164-180.
Elsevier DOI 1507
Object detection BibRef

Talha, A.M.[Ayesha M.], Junejo, I.N.[Imran N.],
Dynamic scene understanding using temporal association rules,
IVC(32), No. 12, 2014, pp. 1102-1116.
Elsevier DOI 1412
Scene understanding. Spatio-temporal abnormalities in event analysis. 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.
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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

Ahmadi, P., Tabandeh, M., Gholampour, I.,
Abnormal event detection and localisation in traffic videos based on group sparse topical coding,
IET-IP(10), No. 3, 2016, pp. 235-246.
DOI Link 1603
feature extraction 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

Hu, X., Hu, S., Huang, Y., Zhang, H., Wu, H.,
Video anomaly detection using deep incremental slow feature analysis network,
IET-CV(10), No. 4, 2016, pp. 258-265.
DOI Link 1608
video signal processing 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

Xu, D.[Dan], Yan, Y.[Yan], Ricci, E.[Elisa], Sebe, N.[Nicu],
Detecting anomalous events in videos by learning deep representations of appearance and motion,
CVIU(156), No. 1, 2017, pp. 117-127.
Elsevier DOI 1702
Video surveillance BibRef

Xu, D.[Dan], Song, J.K.[Jing-Kuan], Alameda-Pineda, X., Ricci, E.[Elisa], Sebe, N.[Nicu],
Multi-Paced Dictionary Learning for cross-domain retrieval and recognition,
ICPR16(3228-3233)
IEEE DOI 1705
Dictionaries, Image reconstruction, Learning systems, Optimization, Silicon, Training, Training, data BibRef

Xu, D.[Dan], Ricci, E.[Elisa], Yan, Y.[Yan], Song, J.K.[Jing-Kuan], Sebe, N.[Nicu],
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Kumar, D.[Dheeraj], Bezdek, J.C.[James C.], Rajasegarar, S.[Sutharshan], Leckie, C.[Christopher], Palaniswami, M.[Marimuthu],
A visual-numeric approach to clustering and anomaly detection for trajectory data,
VC(33), No. 3, March 2017, pp. 265-281.
Springer DOI 1702
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

Cosar, S., Donatiello, G., Bogorny, V., Garate, C., Alvares, L.O., Brémond, F.,
Toward Abnormal Trajectory and Event Detection in Video Surveillance,
CirSysVideo(27), No. 3, March 2017, pp. 683-695.
IEEE DOI 1703
Acceleration 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
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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

Ribeiro, M.[Manassés], Lazzaretti, A.E.[André Eugênio], Lopes, H.S.[Heitor Silvério],
A study of deep convolutional auto-encoders for anomaly detection in videos,
PRL(105), 2018, pp. 13-22.
Elsevier DOI 1804
Deep learning, Convolutional auto-encoder, Anomaly detection, Object recognition, Feature extraction 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

Amraee, S.[Somaieh], Vafaei, A.[Abbas], Jamshidi, K.[Kamal], Adibi, P.[Peyman],
Abnormal event detection in crowded scenes using one-class SVM,
SIViP(12), No. 6, September 2018, pp. 1115-1123.
Springer DOI
WWW Link. 1808
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

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

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

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

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

Shin, H., Turchi, D., He, S., Tsourdos, A.,
Behavior Monitoring Using Learning Techniques and Regular-Expressions-Based Pattern Matching,
ITS(20), No. 4, April 2019, pp. 1289-1302.
IEEE DOI 1904
Monitoring, Pattern matching, Trajectory, Anomaly detection, Target tracking, Dictionaries, Europe, Monitoring, pattern matching, dictionary learning 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

Ceroni, A.[Andrea], Ma, C.Y.[Chen-Yang], Ewerth, R.[Ralph],
Mining exoticism from visual content with fusion-based deep neural networks,
MultInfoRetr(8), No. 1, March 2019, pp. 19-33.
Springer DOI 1906
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

Xu, Z.[Zhenzhou], Cui, G.[Ge], Zhong, M.[Ming], Wang, X.[Xin],
Anomalous Urban Mobility Pattern Detection Based on GPS Trajectories and POI Data,
IJGI(8), No. 7, 2019, pp. xx-yy.
DOI Link 1908
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

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

Roy, P., Bilodeau, G.,
Adversarially Learned Abnormal Trajectory Classifier,
CRV19(65-72)
IEEE DOI 1908
Trajectory, Generative adversarial networks, Training, Data models, Generators, Image reconstruction, Generative adversarial networks 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

Ma, C.[Cong], Miao, Z.J.[Zhen-Jiang], Li, M.[Min], Song, S.[Shaoyue], Yang, M.H.[Ming-Hsuan],
Detecting Anomalous Trajectories via Recurrent Neural Networks,
ACCV18(IV:370-382).
Springer DOI 1906
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

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

Van Beeck, K., Van Engeland, K., Vennekens, J., Goedemé, T.,
Abnormal behavior detection in LWIR surveillance of railway platforms,
AVSS17(1-6)
IEEE DOI 1806
feature extraction, image classification, learning (artificial intelligence), pedestrians, railway safety, Videos BibRef

Lile, C., Yiqun, L.,
Anomaly detection in thermal images using deep neural networks,
ICIP17(2299-2303)
IEEE DOI 1803
Anomaly detection, Bars, Computer architecture, IP networks, Land surface temperature, Predictive models, Training, thermal image 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

Hinami, R., Mei, T., Satoh, S.,
Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge,
ICCV17(3639-3647)
IEEE DOI 1802
convolution, image representation, learning (artificial intelligence), neural nets, Visualization 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

Ionescu, R.T.[Radu Tudor], Smeureanu, S.[Sorina], Alexe, B.[Bogdan], Popescu, M.[Marius],
Detecting Abnormal Events in Video Using Narrowed Normality Clusters,
WACV19(1951-1960)
IEEE DOI 1904
BibRef
Earlier: A1, A2, A4, A3:
Unmasking the Abnormal Events in Video,
ICCV17(2914-2922)
IEEE DOI 1802
BibRef
Earlier: A2, A1, A3, A4:
Deep Appearance Features for Abnormal Behavior Detection in Video,
CIAP17(II:779-789).
Springer DOI 1711
feature extraction, learning (artificial intelligence), neural nets, pattern clustering, support vector machines, Dictionaries. image sequences, object detection, video signal processing, Training data 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

Lawson, W., Bekele, E., Sullivan, K.,
Finding Anomalies with Generative Adversarial Networks for a Patrolbot,
DeepLearnRV17(484-485)
IEEE DOI 1709
Anomaly detection, Cameras, Gallium nitride, Image reconstruction, Robots, Training 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

Varadarajan, J.[Jagannadan], Subramanian, R., Ahuja, N., Moulin, P., Odobez, J.M.[Jean-Marc],
Active Online Anomaly Detection Using Dirichlet Process Mixture Model and Gaussian Process Classification,
WACV17(615-623)
IEEE DOI 1609
Gaussian processes, Junctions, Labeling, Mixture models, Surveillance, Trajectory, Videos BibRef

Maiorano, F., Petrosino, A.,
Granular trajectory based anomaly detection for surveillance,
ICPR16(2066-2072)
IEEE DOI 1705
Real-time systems, Rough sets, Surveillance, Training, Trajectory, Granular Computation, Online Anomaly Detection, Outlier Detection, Rough Sets, Surveillance 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

Ghrab, N.B.[Najla Bouarada], Fendri, E.[Emna], Hammami, M.[Mohamed],
Abnormal Events Detection Based on Trajectory Clustering,
CGiV16(301-306)
IEEE DOI 1608
BibRef
And:
Clustering-Based Abnormal Event Detection: Experimental Comparison for Similarity Measures' Efficiency,
ICIAR16(367-374).
Springer DOI 1608
feature extraction BibRef

Xu, H.T.[Hong-Teng], Zhou, Y.[Yang], Lin, W.Y.[Wei-Yao], Zha, H.Y.[Hong-Yuan],
Unsupervised Trajectory Clustering via Adaptive Multi-kernel-Based Shrinkage,
ICCV15(4328-4336)
IEEE DOI 1602
Clustering algorithms BibRef

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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
Detecting Anomalies, Abnormal Behavior In Crowds .