16.7.3.3.4 Detecting Anomalies, Abnormal Behavior In Crowds

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

Kratz, L.[Louis], Nishino, K.[Ko],
Tracking Pedestrians Using Local Spatio-Temporal Motion Patterns in Extremely Crowded Scenes,
PAMI(34), No. 5, May 2012, pp. 987-1002.
IEEE DOI 1204
BibRef
Earlier:
Tracking with local spatio-temporal motion patterns in extremely crowded scenes,
CVPR10(693-700).
IEEE DOI 1006
BibRef
And:
Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models,
CVPR09(1446-1453).
IEEE DOI 0906
BibRef
Earlier:
Spatio-temporal motion pattern modeling of extremely crowded scenes,
MLMotion08(xx-yy). 0810
Large numbers and frequent occlusions. Collective motion. Use model of crowd motion for tracking individuals. BibRef

Wang, B.[Bo], Ye, M.[Mao], Li, X.[Xue], Zhao, F.J.[Feng-Juan], Ding, J.[Jian],
Abnormal crowd behavior detection using high-frequency and spatio-temporal features,
MVA(23), No. 3, May 2012, pp. 501-511.
WWW Link. 1204
BibRef

Riche, N.[Nicolas], Mancas, M.[Matei], Duvinage, M.[Matthieu], Mibulumukini, M.[Makiese], Gosselin, B.[Bernard], Dutoit, T.[Thierry],
RARE2012: A multi-scale rarity-based saliency detection with its comparative statistical analysis,
SP:IC(28), No. 6, July 2013, pp. 642-658.
Elsevier DOI 1306
Bottom-up saliency; Comparative statistical analysis; Multi-scale rarity mechanism; Regions of interest; Saliency models evaluation; Visual attention BibRef

Riche, N.[Nicolas], Mancas, M.[Matei], Gosselin, B.[Bernard], Dutoit, T.[Thierry],
Rare: A new bottom-up saliency model,
ICIP12(641-644).
IEEE DOI 1302
BibRef
Earlier:
3D Saliency for Abnormal Motion Selection: The Role of the Depth Map,
CVS11(143-152).
Springer DOI 1109
BibRef

Mancas, M.[Matei], Riche, N.[Nicolas], Leroy, J.[Julien], Gosselin, B.[Bernard],
Abnormal motion selection in crowds using bottom-up saliency,
ICIP11(229-232).
IEEE DOI 1201
BibRef

Mibulumukini, M.[Makiese], Riche, N.[Nicolas], Mancas, M.[Matei], Gosselin, B.[Bernard], Dutoit, T.[Thierry],
Biologically plausible context recognition algorithms,
ICIP13(2612-2616)
IEEE DOI 1402
Biologically plausible algorithms 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

Leach, M.J.V.[Michael J.V.], Sparks, E.P., Robertson, N.M.[Neil M.],
Contextual anomaly detection in crowded surveillance scenes,
PRL(44), No. 1, 2014, pp. 71-79.
Elsevier DOI 1407
Behaviour analysis BibRef

Cho, S.H.[Sang-Hyun], Kang, H.B.[Hang-Bong],
Abnormal behavior detection using hybrid agents in crowded scenes,
PRL(44), No. 1, 2014, pp. 64-70.
Elsevier DOI 1407
BibRef
Earlier:
Integrated multiple behavior models for abnormal crowd behavior detection,
Southwest12(113-116).
IEEE DOI 1205
Visual surveillance BibRef

Xu, J.X.[Jing-Xin], Denman, S.[Simon], Reddy, V.[Vikas], Fookes, C.[Clinton], Sridharan, S.[Sridha],
Real-time video event detection in crowded scenes using MPEG derived features: A multiple instance learning approach,
PRL(44), No. 1, 2014, pp. 113-125.
Elsevier DOI 1407
Event detection BibRef

Nallaivarothayan, H.[Hajananth], Fookes, C.[Clinton], Denman, S.[Simon], Sridharan, S.[Sridha],
An MRF based abnormal event detection approach using motion and appearance features,
AVSS14(343-348)
IEEE DOI 1411
Acceleration BibRef

Zhang, Y.H.[Yan-Hao], Qin, L.[Lei], Ji, R., Yao, H.X.[Hong-Xun], Huang, Q.M.[Qing-Ming],
Social Attribute-Aware Force Model: Exploiting Richness of Interaction for Abnormal Crowd Detection,
CirSysVideo(25), No. 7, July 2015, pp. 1231-1245.
IEEE DOI 1507
BibRef
Earlier: A1, A2, A4, A5, Only:
Abnormal crowd behavior detection based on social attribute-aware force model,
ICIP12(2689-2692).
IEEE DOI 1302
Analytical models BibRef

Nam, Y.Y.[Yun-Young], Hong, S.J.[Sang-Jin],
Real-time abnormal situation detection based on particle advection in crowded scenes,
RealTimeIP(10), No. 4, December 2015, pp. 771-784.
WWW Link. 1512
BibRef

Xu, J., Denman, S.[Simon], Sridharan, S.[Sridha], Fookes, C.[Clinton],
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
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 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

Yuan, Y., Fang, J., Wang, Q.,
Online Anomaly Detection in Crowd Scenes via Structure Analysis,
Cyber(45), No. 3, March 2015, pp. 562-575.
IEEE DOI 1502
Computational modeling BibRef

Lee, D.G.[Dong-Gyu], Suk, H.I.[Heung-Il], Park, S.K.[Sung-Kee], Lee, S.W.[Seong-Whan],
Motion Influence Map for Unusual Human Activity Detection and Localization in Crowded Scenes,
CirSysVideo(25), No. 10, October 2015, pp. 1612-1623.
IEEE DOI 1511
BibRef
Earlier: A1, A2, A4, Only:
Crowd Behavior Representation Using Motion Influence Matrix for Anomaly Detection,
ACPR13(110-114)
IEEE DOI 1408
image representation. image segmentation 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

Wang, J.[Jing], Xu, Z.J.[Zhi-Jie],
Spatio-temporal texture modelling for real-time crowd anomaly detection,
CVIU(144), No. 1, 2016, pp. 177-187.
Elsevier DOI 1604
Crowd anomaly BibRef

Zhou, S.F.[Shi-Fu], Shen, W.[Wei], Zeng, D.[Dan], Fang, M.[Mei], Wei, Y.W.[Yuan-Wang], Zhang, Z.J.[Zhi-Jiang],
Spatial-temporal convolutional neural networks for anomaly detection and localization in crowded scenes,
SP:IC(47), No. 1, 2016, pp. 358-368.
Elsevier DOI 1610
Spatial-temporal CNN BibRef

Liu, W., Lau, R.W.H., Wang, X.G.[Xiao-Gang], Manocha, D.[Dinesh],
Exemplar-AMMs: Recognizing Crowd Movements From Pedestrian Trajectories,
MultMed(18), No. 12, December 2016, pp. 2398-2406.
IEEE DOI 1612
Computational modeling BibRef

Cheung, E.[Ernest], Wong, T.K.[Tsan Kwong], Bera, A.[Aniket], Wang, X.G.[Xiao-Gang], Manocha, D.[Dinesh],
LCrowdV: Generating Labeled Videos for Simulation-Based Crowd Behavior Learning,
Crowd16(II: 709-727).
Springer DOI 1611
BibRef

Bera, A.[Aniket], Manocha, D.[Dinesh],
Realtime Anomaly Detection Using Trajectory-Level Crowd Behavior Learning,
PETS16(1289-1296)
IEEE DOI 1612
BibRef
Earlier:
Realtime Multilevel Crowd Tracking Using Reciprocal Velocity Obstacles,
ICPR14(4164-4169)
IEEE DOI 1412
Accuracy BibRef

Biswas, S.[Soma], Gupta, V.[Vikas],
Abnormality detection in crowd videos by tracking sparse components,
MVA(28), No. 1-2, February 2017, pp. 35-48.
WWW Link. 1702
BibRef

Yuan, Y.[Yuan], Feng, Y.C.[Ya-Chuang], Lu, X.Q.[Xiao-Qiang],
Structured dictionary learning for abnormal event detection in crowded scenes,
PR(73), No. 1, 2018, pp. 99-110.
Elsevier DOI 1709
Video, surveillance BibRef

Yuan, Y.[Yuan], Feng, Y.C.[Ya-Chuang], Lu, X.Q.[Xiao-Qiang],
Statistical Hypothesis Detector for Abnormal Event Detection in Crowded Scenes,
Cyber(47), No. 11, November 2017, pp. 3597-3608.
IEEE DOI 1710
Detectors, Event detection, Feature extraction, Trajectory, Abnormal event detection, mixture of Gaussian (MoG). BibRef

Xu, M., Li, C., Lv, P., Lin, N., Hou, R., Zhou, B.,
An Efficient Method of Crowd Aggregation Computation in Public Areas,
CirSysVideo(28), No. 10, October 2018, pp. 2814-2825.
IEEE DOI 1811
Feature extraction, Trajectory, Tracking, Hidden Markov models, Dynamics, Computational modeling, Analytical models, abnormal detection 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

Amraee, S.[Somaieh], Vafaei, A.[Abbas], Jamshidi, K.[Kamal], Adibi, P.[Peyman],
Abnormal event detection in crowded scenes using one-class SVM,
SIViP(12), No. 6, September 2018, pp. 1115-1123.
Springer DOI
WWW Link. 1808
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.
Elsevier DOI 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.,
A review on classifying abnormal behavior in crowd scene,
JVCIR(58), 2019, pp. 285-303.
Elsevier DOI 1901
Crowd analysis, Abnormal detection, Gaussian Mixture Model (GMM), Hidden Markov Model (HMM), Spatio-Temporal Technique (STT) BibRef

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.
Springer DOI 1907
BibRef

Nayan, N.[Navneet], Sahu, S.S.[Sitanshu Sekhar], Kumar, S.[Sanjeet],
Detecting anomalous crowd behavior using correlation analysis of optical flow,
SIViP(13), No. 6, September 2019, pp. 1233-1241.
WWW Link. 1908
BibRef

Qasim, T.[Tehreem], Bhatti, N.[Naeem],
A hybrid swarm intelligence based approach for abnormal event detection in crowded environments,
PRL(128), 2019, pp. 220-225.
Elsevier DOI 1912
Anomaly detection, Swarm intelligence, Ant colony optimization, Predator-prey algorithm, Histogram of swarms BibRef

Bansod, S.D.[Suprit D.], Nandedkar, A.V.[Abhijeet V.],
Crowd anomaly detection and localization using histogram of magnitude and momentum,
VC(36), No. 3, March 2020, pp. 609-620.
WWW Link. 2002
BibRef

Hassanein, A.S.[Allam S.], Hussein, M.E.[Mohamed E.], Gomaa, W.[Walid], Makihara, Y.S.[Yasu-Shi], Yagi, Y.S.[Yasu-Shi],
Identifying motion pathways in highly crowded scenes: A non-parametric tracklet clustering approach,
CVIU(191), 2020, pp. 102710.
Elsevier DOI 2002
Manuscript, Tracklet similarity, DD-CRP, Semantic prior, Tracklet cluster likelihood, Anomaly detection BibRef

Hu, Z.P.[Zheng-Ping], Zhang, L.[Le], Li, S.F.[Shu-Fang], Sun, D.G.[De-Gang],
Parallel spatial-temporal convolutional neural networks for anomaly detection and location in crowded scenes,
JVCIR(67), 2020, pp. 102765.
Elsevier DOI 2004
Abnormal detection, Video surveillance, Parallel 3D convolution neural networks, Spatial-temporal interest cuboids BibRef

Li, A.[Ang], Miao, Z.J.[Zhen-Jiang], Cen, Y.[Yigang], Zhang, X.P.[Xiao-Ping], Zhang, L.[Linna], Chen, S.M.[Shi-Ming],
Abnormal event detection in surveillance videos based on low-rank and compact coefficient dictionary learning,
PR(108), 2020, pp. 107355.
Elsevier DOI 2008
LRCCDL, Reconstruction cost, Abnormal event detection, Crowded scenes, Surveillance videos BibRef

Nguyen, M.T.[Minh Tri], Siritanawan, P.[Prarinya], Kotani, K.[Kazunori],
Saliency detection in human crowd images of different density levels using attention mechanism,
SP:IC(88), 2020, pp. 115976.
Elsevier DOI 2009
Saliency, Human crowd, Deep neural network, Attention mechanism BibRef

Li, N., Chang, F., Liu, C.,
Spatial-Temporal Cascade Autoencoder for Video Anomaly Detection in Crowded Scenes,
MultMed(23), 2021, pp. 203-215.
IEEE DOI 2012
Feature extraction, Anomaly detection, Trajectory, Hidden Markov models, Image reconstruction, Anomaly detection, two-stream framework BibRef

Liu, S.Y.[Shuo-Yan], Yang, E.[Enze], Fang, K.[Kai],
Self-Learning pLSA Model for Abnormal Behavior Detection in Crowded Scenes,
IEICE(E104-D), No. 3, March 2021, pp. 473-476.
WWW Link. 2103
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

Sah, M., Direkoglu, C.,
Semantic annotation of surveillance videos for abnormal crowd behaviour search and analysis,
AVSS17(1-6)
IEEE DOI 1806
behavioural sciences computing, computer vision, image annotation, meta data, multimedia systems, open systems, Videos BibRef

Direkoglu, C., Sah, M., O'Connor, N.E.,
Abnormal crowd behavior detection using novel optical flow-based features,
AVSS17(1-6)
IEEE DOI 1806
behavioural sciences computing, feature extraction, image motion analysis, image sequences, Videos BibRef

Ravanbakhsh, M.[Mahdyar], Sangineto, E.[Enver], Nabi, M.[Moin], Sebe, N.[Nicu],
Training Adversarial Discriminators for Cross-Channel Abnormal Event Detection in Crowds,
WACV19(1896-1904)
IEEE DOI 1904
computer vision, feature extraction, image motion analysis, image representation, learning (artificial intelligence), Testing BibRef

Ravanbakhsh, M.[Mahdyar], Nabi, M.[Moin], Mousavi, H., Sangineto, E.[Enver], Sebe, N.[Nicu],
Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection,
WACV18(1689-1698)
IEEE DOI 1806
convolution, feature extraction, feedforward neural nets, image motion analysis, image representation, image sequences, Videos BibRef

Rojas, O.E.[Oscar Ernesto], Tozzi, C.L.[Clesio Luis],
Abnormal Behavior Detection in Crowded Scenes Based on Optical Flow Connected Components,
CIARP16(266-273).
Springer DOI 1703
BibRef
And:
Abnormal Crowd Behavior Detection Based on Gaussian Mixture Model,
Crowd16(II: 668-675).
Springer DOI 1611
BibRef

Rabiee, H., Haddadnia, J., Mousavi, H., Kalantarzadeh, M., Nabi, M., Murino, V.,
Novel dataset for fine-grained abnormal behavior understanding in crowd,
AVSS16(95-101)
IEEE DOI 1611
Benchmark testing BibRef

Wang, Y., Zhang, Q., Li, B.,
Efficient unsupervised abnormal crowd activity detection based on a spatiotemporal saliency detector,
WACV16(1-9)
IEEE DOI 1511
Detectors BibRef

Wang, S.[Siqi], Zhu, E.[En], Yin, J.P.[Jian-Ping], Porikli, F.M.,
Anomaly detection in crowded scenes by SL-HOF descriptor and foreground classification,
ICPR16(3398-3403)
IEEE DOI 1705
Feature extraction, Histograms, Legged locomotion, Principal component analysis, Robustness, Testing, BibRef

Ergezer, H.[Hamza], Leblebicioglu, K.[Kemal],
Anomaly Detection and Activity Perception Using Covariance Descriptor for Trajectories,
Crowd16(II: 728-742).
Springer DOI 1611
BibRef

Marsden, M., McGuinness, K., Little, S., O'Connor, N.E.,
Holistic features for real-time crowd behaviour anomaly detection,
ICIP16(918-922)
IEEE DOI 1610
Feature extraction BibRef

Lin, H.[Hanhe], Deng, J.D.[Jeremiah D.], Woodford, B.J.[Brendon J.],
Anomaly detection in crowd scenes via online adaptive one-class support vector machines,
ICIP15(2434-2438)
IEEE DOI 1512
anomaly detection; crowd scenes; online learning; support vector machines BibRef

Mousavi, H.[Hossein], Mohammadi, S.[Sadegh], Perina, A.[Alessandro], Chellali, R.[Ryad], Mur, V.[Vittorio],
Analyzing Tracklets for the Detection of Abnormal Crowd Behavior,
WACV15(148-155)
IEEE DOI 1503
Computational modeling 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

Alqaysi, H.H., Sasi, S.,
Detection of Abnormal behavior in Dynamic Crowded Gatherings,
AIPR13(1-6)
IEEE DOI 1408
behavioural sciences computing BibRef

Tang, X.[Xun], Zhang, S.P.[Sheng-Ping], Yao, H.X.[Hong-Xun],
Sparse coding based motion attention for abnormal event detection,
ICIP13(3602-3606)
IEEE DOI 1402
abnormal detection; activity intensity; crowd behavior; sparse coding BibRef

de-la-Calle-Silos, E., Gonzalez-Diaz, I., Diaz-de-Maria, E.,
Mid-level feature set for specific event and anomaly detection in crowded scenes,
ICIP13(4001-4005)
IEEE DOI 1402
Clutter environment BibRef

Wang, L.J.[Li-Jun], Dong, M.[Ming],
Real-time detection of abnormal crowd behavior using a matrix approximation-based approach,
ICIP12(2701-2704).
IEEE DOI 1302
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

Zhu, X.B.[Xiao-Bin], Liu, J.[Jing], Wang, J.Q.[Jin-Qiao], Fang, Y.K.[Yi-Kai], Lu, H.Q.[Han-Qing],
Anomaly detection in crowded scene via appearance and dynamics joint modeling,
ICIP12(2705-2708).
IEEE DOI 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

Feng, J.[Jie], Zhang, C.[Chao], Hao, P.W.[Peng-Wei],
Online Learning with Self-Organizing Maps for Anomaly Detection in Crowd Scenes,
ICPR10(3599-3602).
IEEE DOI 1008
BibRef

Jiang, F.[Fan], Wu, Y.[Ying], Katsaggelos, A.K.[Aggelos K.],
Detecting contextual anomalies of crowd motion in surveillance video,
ICIP09(1117-1120).
IEEE DOI 0911
BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Novelty Detection .