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],
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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
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Nonnegative matrix factorization
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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
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People in high-density crowds appear to move with the flow of the
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Shah, M.[Mubarak],
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0906
BibRef
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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
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Ihaddadene, N.[Nacim],
Djeraba, C.[Chabane],
Covariance Matrices for Crowd Behaviour Monitoring on the Escalator
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ISVC08(II: 470-481).
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Thida, M.[Myo],
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feature extraction
BibRef
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1011
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1312
Uses:
See also Biologically Inspired Object Tracking Using Center-Surround Saliency Mechanisms. And model of normal behavior.
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1506
Cameras
See also Two Stream LSTM: A Deep Fusion Framework for Human Action Recognition.
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DICTA16(1-8)
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1701
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Earlier:
Learning Temporal Alignment Uncertainty for Efficient Event Detection,
DICTA15(1-8)
IEEE DOI
1603
Adaptation models.
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Umakanthan, S.[Sabanadesan],
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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],
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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],
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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
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Temizel, T.T.[Tugba Taskaya],
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IET-CV(10), No. 5, 2016, pp. 374-381.
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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
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IET-IPR(12), No. 1, January 2018, pp. 133-137.
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Patil, N.,
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CVIU(169), 2018, pp. 28-39.
Elsevier DOI
1804
BibRef
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Swarm-based motion features for anomaly detection in crowds,
ICIP14(2353-2357)
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Anomaly detection, Traffic scenes, Surveillance
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Chen, X.H.[Xiao-Han],
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Detecting Abnormal Crowd Behaviors Based on the Div-Curl
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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.,
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Saad, M.N.,
Nurfarzana, A.A.,
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Gunawan, W.,
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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),
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Xu, Y.P.[Yuan-Ping],
Lu, L.[Li],
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Nayan, N.[Navneet],
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Identifying motion pathways in highly crowded scenes: A
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Manuscript, Tracklet similarity, DD-CRP, Semantic prior,
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Tomé, A.[Adrián],
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Anomaly Detection in Crowded Scenarios Using Local and Global Gaussian
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ACIVS17(363-374).
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1712
BibRef
Halbe, M.[Madhura],
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Abnormal Crowd Behavior Detection Based on Combined Approach of Energy
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PReMI17(187-195).
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Lee, D.G.[Dong-Gyu],
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Modeling crowd motions for abnormal activity detection,
AVSS14(325-330)
IEEE DOI
1411
Adaptive optics
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Zhang, T.[Teng],
Wiliem, A.,
Lovell, B.C.,
Region-Based Anomaly Localisation in Crowded Scenes via Trajectory
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DICTA13(1-7)
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1402
feature extraction
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Hu, Y.[Yang],
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Unsupervised Abnormal Crowd Activity Detection Using Semiparametric
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SISM13(767-774)
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1309
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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
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1304
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Lu, T.[Tong],
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Ma, X.L.[Xiao-Lin],
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ICPR14(2203-2208)
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1412
Context
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Ma, X.L.[Xiao-Lin],
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ARTEMIS11(934-941).
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1201
BibRef
Raghavendra, R.,
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Cristani, M.[Marco],
Murino, V.[Vittorio],
Optimizing interaction force for global anomaly detection in crowded
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1201
BibRef
Krausz, B.[Barbara],
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Analyzing pedestrian behavior in crowds for automatic detection of
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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],
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An Abnormal Event Recognition in Crowd Scene,
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1106
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Wu, S.D.[Shan-Dong],
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Chaotic invariants of Lagrangian particle trajectories for anomaly
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1006
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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 .