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],
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Sparse Representation for Robust Abnormality Detection in Crowded
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PR(47), No. 5, 2014, pp. 1791-1799.
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1402
Nonnegative matrix factorization
BibRef
Chen, D.Y.[Duan-Yu],
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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
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
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BibRef
Mehran, R.[Ramin],
Moore, B.E.[Brian E.],
Shah, M.[Mubarak],
A Streakline Representation of Flow in Crowded Scenes,
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Springer DOI
1009
BibRef
Mehran, R.[Ramin],
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Shah, M.[Mubarak],
Abnormal crowd behavior detection using social force model,
CVPR09(935-942).
IEEE DOI
0906
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
Thida, M.[Myo],
Eng, H.L.[How-Lung],
Remagnino, P.[Paolo],
Laplacian Eigenmap With Temporal Constraints for Local Abnormality
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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],
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Bhalodia, V.[Viral],
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CVPR10(1975-1981).
IEEE DOI Video of talk:
WWW Link.
1006
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.
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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
BibRef
Earlier:
Unusual Event Detection in Crowded Scenes Using Bag of LBPs in
Spatio-Temporal Patches,
DICTA11(549-554).
IEEE DOI
1205
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
1111
See also Crowd Counting Using Group Tracking and Local Features.
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
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
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
BibRef
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
Chen, X.H.[Xiao-Han],
Lai, J.H.[Jian-Huang],
Detecting Abnormal Crowd Behaviors Based on the Div-Curl
Characteristics of Flow Fields,
PR(88), 2019, pp. 342-355.
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1901
Crowd state analysis, Physical characteristics, Temporal context of motion
BibRef
Afiq, A.A.,
Zakariya, M.A.,
Saad, M.N.,
Nurfarzana, A.A.,
Khir, M.H.M.,
Fadzil, A.F.,
Jale, A.,
Gunawan, W.,
Izuddin, Z.A.A.,
Faizari, M.,
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JVCIR(58), 2019, pp. 285-303.
Elsevier DOI
1901
Crowd analysis, Abnormal detection,
Gaussian Mixture Model (GMM), Hidden Markov Model (HMM),
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Tomé, A.[Adrián],
Salgado, L.[Luis],
Anomaly Detection in Crowded Scenarios Using Local and Global Gaussian
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ACIVS17(363-374).
Springer DOI
1712
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
Lee, D.G.[Dong-Gyu],
Suk, H.I.[Heung-Il],
Lee, S.W.[Seong-Whan],
Modeling crowd motions for abnormal activity detection,
AVSS14(325-330)
IEEE DOI
1411
Adaptive optics
BibRef
Zhang, T.[Teng],
Wiliem, A.,
Lovell, B.C.,
Region-Based Anomaly Localisation in Crowded Scenes via Trajectory
Analysis and Path Prediction,
DICTA13(1-7)
IEEE DOI
1402
feature extraction
BibRef
Hu, Y.[Yang],
Zhang, Y.[Yangmuzi],
Davis, L.S.[Larry S.],
Unsupervised Abnormal Crowd Activity Detection Using Semiparametric
Scan Statistic,
SISM13(767-774)
IEEE DOI
1309
BibRef
Zhu, X.B.[Xiao-Bin],
Liu, J.[Jing],
Wang, J.Q.[Jin-Qiao],
Fu, W.[Wei],
Lu, H.Q.[Han-Qing],
Weighted Interaction Force Estimation for Abnormality Detection in
Crowd Scenes,
ACCV12(III:507-518).
Springer DOI
1304
BibRef
Lu, T.[Tong],
Wu, L.[Liang],
Ma, X.L.[Xiao-Lin],
Shivakumara, P.[Palaiahnakote],
Tan, C.L.[Chew Lim],
Anomaly Detection through Spatio-temporal Context Modeling in Crowded
Scenes,
ICPR14(2203-2208)
IEEE DOI
1412
Context
BibRef
Ma, X.L.[Xiao-Lin],
Lu, T.[Tong],
Xu, F.M.[Fei-Ming],
Su, F.[Feng],
Anomaly detection with spatio-temporal context using depth images,
ICPR12(2590-2593).
WWW Link.
1302
BibRef
Yu, Y.H.[Yuan-Hao],
Lei, Z.[Zhen],
Yi, D.[Dong],
Li, S.Z.[Stan Z.],
Detecting individual in crowd with moving feature's structure
consistency,
ARTEMIS11(934-941).
IEEE DOI
1201
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
1201
BibRef
Krausz, B.[Barbara],
Bauckhage, C.[Christian],
Analyzing pedestrian behavior in crowds for automatic detection of
congestions,
MSVALC11(144-149).
IEEE DOI
1201
BibRef
And:
Automatic detection of dangerous motion behavior in human crowds,
AVSBS11(224-229).
IEEE DOI
1111
BibRef
Liao, H.H.[Hong-Hong],
Xiang, J.H.[Jin-Hai],
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
1109
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
1106
BibRef
Wu, S.D.[Shan-Dong],
Moore, B.E.[Brian E.],
Shah, M.[Mubarak],
Chaotic invariants of Lagrangian particle trajectories for anomaly
detection in crowded scenes,
CVPR10(2054-2060).
IEEE DOI
1006
BibRef
Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Human Motion Understanding and Analysis .