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

Yuan, J.S.[Jun-Song], Liu, Z.C.[Zi-Cheng], Wu, Y.[Ying],
Discriminative Video Pattern Search for Efficient Action Detection,
PAMI(33), No. 9, September 2011, pp. 1728-1743.
IEEE DOI 1109
BibRef
Earlier:
Discriminative subvolume search for efficient action detection,
CVPR09(2442-2449).
IEEE DOI 0906
Actions as spatio-temporal patterns. Find re-occurrence of such patterns, with intra-pattern variation. Does not require human detection and tracking. BibRef

Cong, Y.[Yang], Yuan, J.S.[Jun-Song], Liu, J.[Ji],
Abnormal Event Detection in Crowded Scenes Using Sparse Representation,
PR(46), No. 7, July 2013, pp. 1851-1864.
Elsevier DOI 1303
BibRef
Earlier:
Sparse reconstruction cost for abnormal event detection,
CVPR11(3449-3456).
IEEE DOI 1106
Sparse representation; Abnormal event; Crowd analysis; Video surveillance See also Learning Actionlet Ensemble for 3D Human Action Recognition. BibRef

Zhu, X.B.[Xiao-Bin], Liu, J.[Jing], Wang, J.Q.[Jin-Qiao], Li, C.S.[Chang-Sheng], Lu, H.Q.[Han-Qing],
Sparse Representation for Robust Abnormality Detection in Crowded Scenes,
PR(47), No. 5, 2014, pp. 1791-1799.
Elsevier DOI 1402
Nonnegative matrix factorization 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

Chen, D.Y.[Duan-Yu], Huang, P.C.[Po-Chung],
Motion-based unusual event detection in human crowds,
JVCIR(22), No. 2, February 2011, pp. 178-186.
Elsevier DOI 1102
Human crowd analysis; Unusual event detection; Video surveillance; Optical flows; Unsupervised clustering; Force field model; Adjacency matrix; Spatial-temporal analysis 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

Moore, B.E.[Brian E.], Ali, S.[Saad], Mehran, R.[Ramin], Shah, M.[Mubarak],
Visual Crowd Surveillance Through a Hydrodynamics Lens,
CACM(54), No. 12, December 2011, pp. 64-73.
DOI Link 1112
People in high-density crowds appear to move with the flow of the crowd, like particles in a liquid. BibRef

Mehran, R.[Ramin], Moore, B.E.[Brian E.], Shah, M.[Mubarak],
A Streakline Representation of Flow in Crowded Scenes,
ECCV10(III: 439-452).
Springer DOI 1009
BibRef

Mehran, R.[Ramin], Oyama, A.[Alexis], Shah, M.[Mubarak],
Abnormal crowd behavior detection using social force model,
CVPR09(935-942).
IEEE DOI 0906
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

Sharif, M.H.[Md. Haidar], Djeraba, C.[Chabane],
An entropy approach for abnormal activities detection in video streams,
PR(45), No. 7, July 2012, pp. 2543-2561.
Elsevier DOI 1203
BibRef
Earlier:
PedVed: Pseudo Euclidian Distances for Video Events Detection,
ISVC09(II: 674-685).
Springer DOI 0911
BibRef
And:
A Simple Method for Eccentric Event Espial Using Mahalanobis Metric,
CIARP09(417-424).
Springer DOI 0911
Abnormality; Circular variance; Degree of randomness; Entropy E.g. escalator monitoring BibRef

Sharif, M.H.[M. Haidar], Ihaddadene, N.[Nacim], Djeraba, C.[Chabane],
Covariance Matrices for Crowd Behaviour Monitoring on the Escalator Exits,
ISVC08(II: 470-481).
Springer DOI 0812
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

Thida, M.[Myo], Eng, H.L.[How-Lung], Remagnino, P.[Paolo],
Laplacian Eigenmap With Temporal Constraints for Local Abnormality Detection in Crowded Scenes,
Cyber(43), No. 6, 2013, pp. 2147-2156.
IEEE DOI 1312
feature extraction BibRef

Thida, M.[Myo], Eng, H.L.[How-Lung], Dorothy, M.[Monekosso], Remagnino, P.[Paolo],
Learning Video Manifold for Segmenting Crowd Events and Abnormality Detection,
ACCV10(I: 439-449).
Springer DOI 1011
BibRef

Li, W.X.[Wei-Xin], Mahadevan, V.[Vijay], Vasconcelos, N.M.[Nuno M.],
Anomaly Detection and Localization in Crowded Scenes,
PAMI(36), No. 1, 2014, pp. 18-32.
IEEE DOI 1312
Uses: See also Biologically Inspired Object Tracking Using Center-Surround Saliency Mechanisms. And model of normal behavior. BibRef

Mahadevan, V.[Vijay], Li, W.X.[Wei-Xin], Bhalodia, V.[Viral], Vasconcelos, N.M.[Nuno M.],
Anomaly detection in crowded scenes,
CVPR10(1975-1981).
IEEE DOI Video of talk:
WWW Link. 1006
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
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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,
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IEEE DOI 1211
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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
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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.
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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.
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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.
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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

Xu, J., Denman, S., Sridharan, S., Fookes, C.,
An Efficient and Robust System for Multiperson Event Detection in Real-World Indoor Surveillance Scenes,
CirSysVideo(25), No. 6, June 2015, pp. 1063-1076.
IEEE DOI 1506
Cameras BibRef

Abbasnejad, I., Sridharan, S., Denman, S., Fookes, C., Lucey, S.,
Complex Event Detection Using Joint Max Margin and Semantic Features,
DICTA16(1-8)
IEEE DOI 1701
BibRef
Earlier:
Learning Temporal Alignment Uncertainty for Efficient Event Detection,
DICTA15(1-8)
IEEE DOI 1603
Adaptation models. image representation BibRef

Umakanthan, S.[Sabanadesan], Denman, S.[Simon], Fookes, C.[Clinton], Sridharan, S.[Sridha],
Supervised Latent Dirichlet Allocation Models for Efficient Activity Representation,
DICTA14(1-6)
IEEE DOI 1502
BibRef
Earlier:
Multiple Instance Dictionary Learning for Activity Representation,
ICPR14(1377-1382)
IEEE DOI 1412
BibRef
Earlier:
Semi-Binary Based Video Features for Activity Representation,
DICTA13(1-7)
IEEE DOI 1402
feature extraction BibRef

Xu, J.X.[Jing-Xin], Denman, S.[Simon], Fookes, C.[Clinton], Sridharan, S.[Sridha],
Unusual Scene Detection Using Distributed Behaviour Model and Sparse Representation,
AVSS12(48-53).
IEEE DOI 1211
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Earlier:
Unusual Event Detection in Crowded Scenes Using Bag of LBPs in Spatio-Temporal Patches,
DICTA11(549-554).
IEEE DOI 1205
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Ryan, D.[David], Denman, S.[Simon], Fookes, C.[Clinton], Sridharan, S.[Sridha],
Textures of optical flow for real-time anomaly detection in crowds,
AVSBS11(230-235).
IEEE DOI 1111
See also Crowd Counting Using Group Tracking and Local Features. 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
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Cheng, K.W.[Kai-Wen], Chen, Y.T.[Yie-Tarng], Fang, W.H.[Wen-Hsien],
Gaussian Process Regression-Based Video Anomaly Detection and Localization With Hierarchical Feature Representation,
IP(24), No. 12, December 2015, pp. 5288-5301.
IEEE DOI 1512
BibRef
Earlier:
Video anomaly detection and localization using hierarchical feature representation and Gaussian process regression,
CVPR15(2909-2917)
IEEE DOI 1510
Gaussian processes 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

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

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
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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

Gunduz, A.E.[Ayse Elvan], Ongun, C.[Cihan], Temizel, T.T.[Tugba Taskaya], Temizel, A.[Alptekin],
Density aware anomaly detection in crowded scenes,
IET-CV(10), No. 5, 2016, pp. 374-381.
DOI Link 1609
BibRef
Earlier: A1, A3, A4, Only:
Pedestrian zone anomaly detection by non-parametric temporal modelling,
AVSS14(131-135)
IEEE DOI 1411
BibRef
Earlier: A2, A4, A3, Only:
Local anomaly detection in crowded scenes using Finite-Time Lyapunov Exponent based clustering,
AVSS14(331-336)
IEEE DOI 1411
feature extraction. Clustering algorithms 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
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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

Ji, Q.G.[Qing-Ge], Chi, R.[Rui], Lu, Z.M.[Zhe-Ming],
Anomaly detection and localisation in the crowd scenes using a block-based social force model,
IET-IPR(12), No. 1, January 2018, pp. 133-137.
DOI Link 1712
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

Patil, N., Biswas, P.K.[Prabir Kumar],
Global abnormal events detection in crowded scenes using context location and motion-rich spatio-temporal volumes,
IET-IPR(12), No. 4, April 2018, pp. 596-604.
DOI Link 1804
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Kaltsa, V.[Vagia], Briassouli, A.[Alexia], Kompatsiaris, I.[Ioannis], Strintzis, M.G.[Michael G.],
Multiple Hierarchical Dirichlet Processes for anomaly detection in traffic,
CVIU(169), 2018, pp. 28-39.
Elsevier DOI 1804
BibRef
Earlier:
Swarm-based motion features for anomaly detection in crowds,
ICIP14(2353-2357)
IEEE DOI 1502
Anomaly detection, Traffic scenes, Surveillance 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

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

Ravanbakhsh, M., Nabi, M., Sangineto, E., Marcenaro, L., Regazzoni, C., Sebe, N.,
Abnormal event detection in videos using generative adversarial nets,
ICIP17(1577-1581)
IEEE DOI 1803
Gallium nitride, Generators, Image reconstruction, Optical imaging, Task analysis, Training, Videos, Generative Adversarial Networks, crowd behaviour analysis 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

Tomé, A.[Adrián], Salgado, L.[Luis],
Anomaly Detection in Crowded Scenarios Using Local and Global Gaussian Mixture Models,
ACIVS17(363-374).
Springer DOI 1712
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

Halbe, M.[Madhura], Vyas, V.[Vibha], Vaidya, Y.M.[Yogita M.],
Abnormal Crowd Behavior Detection Based on Combined Approach of Energy Model and Threshold,
PReMI17(187-195).
Springer DOI 1711
BibRef

Ionescu, R.T.[Radu Tudor], Smeureanu, S.[Sorina], Alexe, B.[Bogdan], Popescu, M.[Marius],
Unmasking the Abnormal Events in Video,
ICCV17(2914-2922)
IEEE DOI 1802
BibRef
Earlier: A2, A1, A4, A3:
Deep Appearance Features for Abnormal Behavior Detection in Video,
CIAP17(II:779-789).
Springer DOI 1711
feature extraction, 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

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

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

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

Mousavi, H.[Hossein], Nabi, M.[Moin], Galoogahi, H.K.[Hamed Kiani], Perina, A.[Alessandro], Murino, V.[Vittorio],
Abnormality Detection with Improved Histogram of Oriented Tracklets,
CIAP15(II:722-732).
Springer DOI 1511
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

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Real time anomaly detection in H.264 compressed videos,
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Anomalous Event Detection Using a Semi-Two Dimensional Hidden Markov Model,
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PETS13(45-52)
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Histograms of Optical Flow Orientation for Visual Abnormal Events Detection,
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object detection BibRef

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MSVALC11(136-143).
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HACI13(515-521)
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image classification BibRef

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ICCV15(1251-1259)
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Learning Latent Constituents for Recognition of Group Activities in Video,
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Springer DOI 1408
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Earlier:
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Earlier:
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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., Shi, J.,
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
Human Motion Understanding and Analysis .