16.7.3.4 Detecting Anomalies, Abnormal Behavior In Crowds

Chapter Contents (Back)
Anomaly Detection. Abnormal Event. Crowds. Anomalous event in the crowd. General crowd behavior: See also Human Activities, Crowds, Lots of People.

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

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

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

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 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

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
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


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

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

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 .


Last update:Nov 17, 2018 at 09:12:27