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
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PR(47), No. 5, 2014, pp. 1791-1799.
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1402
Nonnegative matrix factorization
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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
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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],
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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],
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Dorothy, M.[Monekosso],
Remagnino, P.[Paolo],
Learning Video Manifold for Segmenting Crowd Events and Abnormality
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ACCV10(I: 439-449).
Springer DOI
1011
BibRef
Li, W.X.[Wei-Xin],
Mahadevan, V.[Vijay],
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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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IEEE DOI Video of talk:
WWW Link.
1006
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Xu, J.,
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An Efficient and Robust System for Multiperson Event Detection in
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1506
Cameras
See also Two Stream LSTM: A Deep Fusion Framework for Human Action Recognition.
BibRef
Abbasnejad, I.,
Sridharan, S.[Sridha],
Denman, S.[Simon],
Fookes, C.[Clinton],
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
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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
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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.
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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
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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
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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.
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1901
Crowd analysis, Abnormal detection,
Gaussian Mixture Model (GMM), Hidden Markov Model (HMM),
Spatio-Temporal Technique (STT)
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Xu, Y.P.[Yuan-Ping],
Lu, L.[Li],
Xu, Z.J.[Zhi-Jie],
He, J.[Jia],
Zhou, J.L.[Ji-Liu],
Zhang, C.L.[Chao-Long],
Dual-channel CNN for efficient abnormal behavior identification through
crowd feature engineering,
MVA(30), No. 5, July 2019, pp. 945-958.
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1907
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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
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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
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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 .