16.7.3.2 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 Version. 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.
WWW Version. 0609
trajectory clustering; On-line clustering; Behaviour analysis BibRef

Piciarelli, C., Micheloni, C., Foresti, G.L.,
Kernel-based unsupervised trajectory clusters discovery,
VS08(xx-yy). 0810
BibRef

Piciarelli, C.[Claudio], Foresti, G.L.[Gian Luca],
Anomalous trajectory detection using support vector machines,
AVSBS07(153-158).
IEEE DOI Link 0709
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 Link 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 Link 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 Link 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.
WWW Version. 0611
Data streams; Concentration inequalities; Precision; Recall; Accuracy BibRef

Nock, R.[Richard], Laur, P.A.[Pierre-Alain], Symphor, J.E.[Jean-Emile],
Statistical Borders for Incremental Mining,
ICPR06(III: 212-215).
IEEE DOI Link 0609
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 Link 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 Link 0705
BibRef ICCV05(I: 462-469).
IEEE DOI Link 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 Link 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 Link 0803
BibRef
Earlier:
Optimal Dynamic Graphs for Video Content Analysis,
BMVC06(I:177).
PDF Version. 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., Gong, S.G.[Shao-Gang], Xiang, T.[Tao],
Global Behaviour Inference using Probabilistic Latent Semantic Analysis,
BMVC08(xx-yy).
PDF Version. 0809
BibRef

Xiang, T.[Tao], Gong, S.G.[Shao-Gang],
Incremental and adaptive abnormal behaviour detection,
CVIU(111), No. 1, July 2008, pp. 59-73.
WWW Version. 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

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 Link 0804
BibRef
Earlier:
Infinite Hidden Markov Models and ISA Features for Unusual-Event Detection in Video,
ICIP07(V: 137-140).
IEEE DOI Link 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.
WWW Version. 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 Link 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 Link
WWW Version. 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 Link 0901
BibRef

Monachino, C.A.[Cheryl A.], Paradis, R.D.[Rosemary D.],
Scene analysis surveillance system,
US_Patent7,310,442, Dec 18, 2007
WWW Version. 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 Link 0902
Small training set. Find anomalies. Applied to non-image tasks. 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 Link 0811
BibRef
And:
Anomalous trajectory patterns detection,
ICPR08(1-4).
IEEE DOI Link 0812
BibRef
And:
Support vector machines for robust trajectory clustering,
ICIP08(2540-2543).
IEEE DOI Link 0810
BibRef
Earlier:
An Autonomous Surveillance Vehicle for People Tracking,
CIAP05(1140-1147).
Springer DOI Link 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 Link 1107
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 Link 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 Link 0903
BibRef
Earlier:
Abnormal Event Detection from Surveillance Video by Dynamic Hierarchical Clustering,
ICIP07(V: 145-148).
IEEE DOI Link 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 Link 1109
BibRef
Earlier:
Discriminative subvolume search for efficient action detection,
CVPR09(2442-2449).
IEEE DOI Link 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],
Sparse reconstruction cost for abnormal event detection,
CVPR11(3449-3456).
IEEE DOI Link 1106
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 Link 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 Link
WWW Version. 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 Link
WWW Version. 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 Link
WWW Version. 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 Link 1007
BibRef
Earlier:
Modelling Activity Global Temporal Dependencies Using Time Delayed Probabilistic Graphical Model,
ICCV09(120-127).
IEEE DOI Link 0909
BibRef
And:
Modelling Multi-object Activity by Gaussian Processes,
BMVC09(xx-yy).
PDF Version. 0909
BibRef
And:
Multi-camera activity correlation analysis,
CVPR09(1988-1995).
IEEE DOI Link 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 Link
WWW Version. 1003
Anomaly detection; Dynamic Bayesian Networks; Visual surveillance; Behavior decomposition; Duration modelling BibRef

Venkatesh, S., Konrad, J., Jodoin, P.M.,
Video Anomaly Identification,
SPMag(27), No. 5, 2010, pp. 18-33.
IEEE DOI Link 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 Link
WWW Version. 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 Link
WWW Version. 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 Link 0906
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 Link
WWW Version. 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 Link
WWW Version. 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 Link
WWW Version. 1103
BibRef
Earlier:
Video anomaly detection in spatiotemporal context,
ICIP10(705-708).
IEEE DOI Link 1009
Video surveillance; Anomaly detection; Data mining; Clustering; Context BibRef

Tran, D.[Du], Yuan, J.S.[Jun-Song],
Optimal spatio-temporal path discovery for video event detection,
CVPR11(3321-3328).
IEEE DOI Link 1106
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 Version. 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 Link 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 Link 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.
WWW Version. 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 Link 1009
BibRef

Mehran, R.[Ramin], Oyama, A.[Alexis], Shah, M.[Mubarak],
Abnormal crowd behavior detection using social force model,
CVPR09(935-942).
IEEE DOI Link 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 Link
WWW Version. 1201
BibRef
Earlier: A3, A1, A2:
Dense spatio-temporal features for non-parametric anomaly detection and localization,
ARTEMIS10(27-32).
WWW Version. 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 Link
WWW Version. 1201
BibRef
Earlier:
An Update-Describe Approach for Human Action Recognition in Surveillance Video,
DICTA10(270-275).
IEEE DOI Link 1012
BibRef
Earlier:
A Context-Based Approach for Detecting Suspicious Behaviours,
DICTA09(146-153).
IEEE DOI Link 0912
BibRef
Earlier:
Detecting Uncommon Trajectories,
DICTA08(398-404).
IEEE DOI Link 0812
Suspicious behaviour; Context; Surveillance system; Security BibRef

Raghavendra, R., Del Bue, A.[Alessio], Cristani, M.[Marco], Murino, V.[Vittorio],
Optimizing interaction force for global anomaly detection in crowded scenes,
MSVALC11(136-143).
IEEE DOI Link 1201
BibRef

Antic, B.[Borislav], Ommer, B.[Bjorn],
Video parsing for abnormality detection,
ICCV11(2415-2422).
IEEE DOI Link 1201
BibRef

Mueller, M.[Martin], Karasev, P.[Peter], Kolesov, I.[Ivan], Tannenbaum, A.[Allen],
A video analytics framework for amorphous and unstructured anomaly detection,
ICIP11(2945-2948).
IEEE DOI Link 1201
BibRef

Schuster, R.[Rene], Schulter, S.[Samuel], Poier, G.[Georg], Hirzer, M.[Martin], Birchbauer, J.[Josef], Roth, P.M.[Peter M.], Bischof, H.[Horst], Winter, M.[Martin], Schallauer, P.[Peter],
Multi-cue learning and visualization of unusual events,
VS11(1933-1940).
IEEE DOI Link 1201
BibRef

Birchbauer, J.[Josef], Schulter, S.[Samuel], Schuster, R.[Rene], Poier, G.[Georg], Winter, M.[Martin], Schallauer, P.[Peter], Roth, P.M.[Peter M.], Bischof, H.[Horst],
OUTLIER: Online learning and visualization of unusual events,
AVSBS11(533-534).
IEEE DOI Link 1111
AVSS 2011 demo session. BibRef

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

Jeong, H.[Hawook], Chang, H.J.[Hyung Jin], Choi, J.Y.[Jin Young],
Modeling of moving object trajectory by spatio-temporal learning for abnormal behavior detection,
AVSBS11(119-123).
IEEE DOI Link 1111
BibRef

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

Emonet, R., Varadarajan, J., Odobez, J.,
Multi-camera open space human activity discovery for anomaly detection,
AVSBS11(218-223).
IEEE DOI Link 1111
BibRef

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

Krausz, B.[Barbara], Bauckhage, C.[Christian],
Analyzing pedestrian behavior in crowds for automatic detection of congestions,
MSVALC11(144-149).
IEEE DOI Link 1201
BibRef
And:
Automatic detection of dangerous motion behavior in human crowds,
AVSBS11(224-229).
IEEE DOI Link 1111
BibRef

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 Link 1111
See also Crowd Counting Using Group Tracking and Local Features. BibRef

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

Riche, N.[Nicolas], Mancas, M.[Matei], Gosselin, B.[Bernard], Dutoit, T.[Thierry],
3D Saliency for Abnormal Motion Selection: The Role of the Depth Map,
CVS11(143-152).
Springer DOI Link 1109
BibRef

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

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

Liao, H.H.[Hong-Hong], Xiang, J.[Jinhai], Sun, W.P.[Wei-Ping], Feng, Q.[Qing], Dai, J.H.[Jiang-Hua],
An Abnormal Event Recognition in Crowd Scene,
ICIG11(731-736).
IEEE DOI Link 1109
BibRef

Li, C.[Ce], Han, Z.J.[Zhen-Jun], Ye, Q.X.[Qi-Xiang], Jiao, J.B.[Jian-Bin],
Abnormal Behavior Detection via Sparse Reconstruction Analysis of Trajectory,
ICIG11(807-810).
IEEE DOI Link 1109
BibRef

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

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

Zhao, B.[Bin], Fei-Fei, L.[Li], Xing, E.P.[Eric P.],
Online detection of unusual events in videos via dynamic sparse coding,
CVPR11(3313-3320).
IEEE DOI Link 1106
BibRef

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

Reddy, V.[Vikas], Sanderson, C.[Conrad], Lovell, B.C.[Brian C.],
Improved anomaly detection in crowded scenes via cell-based analysis of foreground speed, size and texture,
MLVMA11(55-61).
IEEE DOI Link 1106
BibRef

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

Loy, C.C.[Chen Change], Xiang, T.[Tao], Gong, S.G.[Shao-Gang],
Stream-Based Active Unusual Event Detection,
ACCV10(I: 161-175).
Springer DOI Link 1011
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 Link 1011
BibRef

Thida, M.[Myo], Remagnino, P.[Paolo], Eng, H.L.[How-Lung],
A particle swarm optimization approach for multi-objects tracking in crowded scene,
VS09(1209-1215).
IEEE DOI Link 0910
BibRef

Thida, M.[Myo], Eng, H.L.[How-Lung], Chew, B.F.[Boon Fong],
Automatic Analysis of Fish Behaviors and Abnormality Detection,
MVA09(278-).
PDF Version. 0905
BibRef

Chew, B.F.[Boon Fong], Eng, H.L.[How-Lung], Thida, M.[Myo],
Vision-Based Real-Time Monitoring on the Behavior of Fish School,
MVA09(90-).
PDF Version. 0905
Not walking, clusters of fish. BibRef

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

Pan, J.[Jiyan], Fan, Q.F.[Quan-Fu], Pankanti, S.[Sharath], Trinh, H.[Hoang], Gabbur, P.[Prasad], Miyazawa, S.[Sachiko],
Soft margin keyframe comparison: Enhancing precision of fraud detection in retail surveillance,
WACV11(549-556).
IEEE DOI Link 1101
BibRef

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

Park, U.S.[Un-Sang], Otto, C.A., Pankanti, S.,
Cart Auditor: A Compliance and Training Tool for Cashiers at Checkout,
PSIVT10(151-155).
IEEE DOI Link 1011
BibRef

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

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

Tziakos, I., Cavallaro, A., Xu, L.Q.[Li-Qun],
Local Abnormality Detection in Video Using Subspace Learning,
AVSS10(519-525).
IEEE DOI Link 1009
BibRef

Espinosa-Isidrón, D.L.[Dustin L.], García-Reyes, E.B.[Edel B.],
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Chan, M.T.[Michael T.], Hoogs, A.J.[Anthony J.], Bhotika, R.[Rahul], Perera, A.[Amitha], Schmiederer, J.[John], Doretto, G.[Gianfranco],
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Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Human Motion Understanding and Analysis .