Crowd Detection/Recognition/Segmentation from UAV/Drone-Captured Images/Videos,
2022.
WWW Link.
Dataset, Crowd Detection. Under the auspices of the European Union's "Horizon 2020" research
framework programme. It is a collection of datasets suitable for
research on autonomous UAV/drone vision.
See also Aristotle University of Thessaloniki.
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Actions as spatio-temporal patterns. Find re-occurrence of such
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Earlier:
Tracking with local spatio-temporal motion patterns in extremely
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Earlier:
Spatio-temporal motion pattern modeling of extremely crowded scenes,
MLMotion08(xx-yy).
0810
Large numbers and frequent occlusions. Collective motion. Use model of
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Ye, M.[Mao],
Li, X.[Xue],
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RARE2012: A multi-scale rarity-based saliency detection with its
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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],
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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.
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Zhu, X.B.[Xiao-Bin],
Liu, J.[Jing],
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Nonnegative matrix factorization
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Chen, D.Y.[Duan-Yu],
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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],
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Visual Crowd Surveillance Through a Hydrodynamics Lens,
CACM(54), No. 12, December 2011, pp. 64-73.
DOI Link
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People in high-density crowds appear to move with the flow of the
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Mehran, R.[Ramin],
Moore, B.E.[Brian E.],
Shah, M.[Mubarak],
A Streakline Representation of Flow in Crowded Scenes,
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1009
BibRef
Mehran, R.[Ramin],
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Shah, M.[Mubarak],
Abnormal crowd behavior detection using social force model,
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BibRef
Sharif, M.H.[Md. Haidar],
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PR(45), No. 7, July 2012, pp. 2543-2561.
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1203
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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
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ISVC08(II: 470-481).
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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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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).
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BibRef
Li, W.X.[Wei-Xin],
Mahadevan, V.[Vijay],
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Anomaly Detection and Localization in Crowded Scenes,
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Uses:
See also Biologically Inspired Object Tracking Using Center-Surround Saliency Mechanisms. And model of normal behavior.
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Behaviour analysis
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1407
BibRef
Earlier:
Integrated multiple behavior models for abnormal crowd behavior
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Southwest12(113-116).
IEEE DOI
1205
Visual surveillance
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Xu, J.X.[Jing-Xin],
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Event detection
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AVSS14(343-348)
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Acceleration
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1507
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Earlier: A1, A2, A4, A5, Only:
Abnormal crowd behavior detection based on social attribute-aware force
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Analytical models
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Xu, J.,
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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)
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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],
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Sridharan, S.[Sridha],
Supervised Latent Dirichlet Allocation Models for Efficient Activity
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DICTA14(1-6)
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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
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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.
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1502
Computational modeling
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Lee, D.G.[Dong-Gyu],
Suk, H.I.[Heung-Il],
Park, S.K.[Sung-Kee],
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CirSysVideo(25), No. 10, October 2015, pp. 1612-1623.
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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],
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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
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SP:IC(47), No. 1, 2016, pp. 358-368.
Elsevier DOI
1610
Spatial-temporal CNN
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
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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],
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Abnormality detection in crowd videos by tracking sparse components,
MVA(28), No. 1-2, February 2017, pp. 35-48.
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1702
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Yuan, Y.[Yuan],
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Lu, X.Q.[Xiao-Qiang],
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PR(73), No. 1, 2018, pp. 99-110.
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1709
Video, surveillance
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Yuan, Y.[Yuan],
Feng, Y.C.[Ya-Chuang],
Lu, X.Q.[Xiao-Qiang],
Statistical Hypothesis Detector for Abnormal Event Detection in
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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.
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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
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IET-IPR(12), No. 1, January 2018, pp. 133-137.
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1712
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Amraee, S.[Somaieh],
Vafaei, A.[Abbas],
Jamshidi, K.[Kamal],
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Abnormal event detection in crowded scenes using one-class SVM,
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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
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Kaltsa, V.[Vagia],
Briassouli, A.[Alexia],
Kompatsiaris, I.[Ioannis],
Strintzis, M.G.[Michael G.],
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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.
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],
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Detecting anomalous crowd behavior using correlation analysis of
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SIViP(13), No. 6, September 2019, pp. 1233-1241.
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Qasim, T.[Tehreem],
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A hybrid swarm intelligence based approach for abnormal event
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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.],
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Crowd anomaly detection and localization using histogram of magnitude
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Hassanein, A.S.[Allam S.],
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Identifying motion pathways in highly crowded scenes: A
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CVIU(191), 2020, pp. 102710.
Elsevier DOI
2002
Manuscript, Tracklet similarity, DD-CRP, Semantic prior,
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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
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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.G.[Yi-Gang],
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
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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
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MultMed(23), 2021, pp. 203-215.
IEEE DOI
2012
Feature extraction, Anomaly detection, Trajectory,
Hidden Markov models, Image reconstruction, Anomaly detection,
two-stream framework
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Liu, S.Y.[Shuo-Yan],
Yang, E.[Enze],
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Self-Learning pLSA Model for Abnormal Behavior Detection in Crowded
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IEICE(E104-D), No. 3, March 2021, pp. 473-476.
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Feng, J.F.[Jiang-Fan],
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Crowd Anomaly Detection via Spatial Constraints and Meaningful
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IJGI(11), No. 3, 2022, pp. xx-yy.
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2204
BibRef
Zhao, R.Y.[Rong-Yong],
Dong, D.H.[Da-Heng],
Wang, Y.[Yan],
Li, C.L.[Cui-Ling],
Ma, Y.L.[Yun-Long],
Enríquez, V.F.[Verónica Fuentes],
Image-Based Crowd Stability Analysis Using Improved Multi-Column
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ITS(23), No. 6, June 2022, pp. 5480-5489.
IEEE DOI
2206
Estimation, Convolution, Analytical models, Task analysis,
Stability criteria, Kernel, Training, Convolutional neural network,
crowd stability analysis
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Djenouri, Y.[Youcef],
Srivastava, G.[Gautam],
Cano, A.[Alberto],
Lin, J.C.W.[Jerry Chun-Wei],
Hybrid Group Anomaly Detection for Sequence Data:
Application to Trajectory Data Analytics,
ITS(23), No. 7, July 2022, pp. 9346-9357.
IEEE DOI
2207
Anomaly detection, Trajectory, Data mining, Public transportation,
Hurricanes, Graphics processing units, Databases,
GPU computing
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Huang, W.H.[Wen-Hao],
Tsuge, A.[Akira],
Chen, Y.[Yin],
Okoshi, T.[Tadashi],
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A Bus Crowdedness Sensing System Using Deep-Learning Based Object
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IEICE(E105-D), No. 10, October 2022, pp. 1712-1720.
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2210
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Zhang, X.F.[Xin-Feng],
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Li, B.[Bin],
Hybrid Attention and Motion Constraint for Anomaly Detection in
Crowded Scenes,
CirSysVideo(33), No. 5, May 2023, pp. 2259-2274.
IEEE DOI
2305
Videos, Anomaly detection, Training, Memory modules, Dictionaries,
Testing, Surveillance, Anomaly detection, video surveillance,
attention mechanism
BibRef
Alohali, M.A.[Manal Abdullah],
Aljebreen, M.[Mohammed],
Nemri, N.[Nadhem],
Allafi, R.[Randa],
Duhayyim, M.A.[Mesfer Al],
Alsaid, M.I.[Mohamed Ibrahim],
Alneil, A.A.[Amani A.],
Osman, A.E.[Azza Elneil],
Anomaly Detection in Pedestrian Walkways for Intelligent
Transportation System Using Federated Learning and Harris Hawks
Optimizer on Remote Sensing Images,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link
2307
BibRef
Hong, Y.[Yayao],
Zhu, H.[Hang],
Shou, T.[Tieqi],
Wang, Z.[Zeyu],
Chen, L.[Liyue],
Wang, L.[Leye],
Wang, C.[Cheng],
Chen, L.[Longbiao],
STORM: A Spatio-Temporal Context-Aware Model for Predicting
Event-Triggered Abnormal Crowd Traffic,
ITS(25), No. 10, October 2024, pp. 13051-13066.
IEEE DOI
2410
Context modeling, Predictive models, Spatiotemporal phenomena,
Tropical cyclones, Data models, Fluctuations, Feature extraction,
urban computing
BibRef
Zhao, R.[Rongyong],
Wei, B.[Bingyu],
Han, C.F.[Chuan-Feng],
Jia, P.[Ping],
Zhu, W.J.[Wen-Jie],
Li, C.L.[Cui-Ling],
Ma, Y.L.[Yun-Long],
Improved Crowd Dynamics Analysis Considering Physical Contact Force
and Panic Emotional Propagation,
ITS(26), No. 2, February 2025, pp. 1840-1851.
IEEE DOI
2502
Pedestrians, Dynamics, Force, Biological system modeling,
Mathematical models, Analytical models, Accidents, Fluid dynamics,
panic propagation
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
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],
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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
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,
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
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
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Wang, Y.L.[Yi-Lin],
Zhang, Q.[Qiang],
Li, B.X.[Bao-Xin],
Efficient unsupervised abnormal crowd activity detection based on a
spatiotemporal saliency detector,
WACV16(1-9)
IEEE DOI
1511
Detectors
BibRef
Wang, S.Q.[Si-Qi],
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,
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Ergezer, H.[Hamza],
Leblebicioglu, K.[Kemal],
Anomaly Detection and Activity Perception Using Covariance Descriptor
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Crowd16(II: 728-742).
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1611
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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
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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
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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
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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
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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
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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
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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
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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
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ICPR14(2203-2208)
IEEE DOI
1412
Context
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Ma, X.L.[Xiao-Lin],
Lu, T.[Tong],
Xu, F.M.[Fei-Ming],
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Anomaly detection with spatio-temporal context using depth images,
ICPR12(2590-2593).
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1302
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Liu, J.[Jing],
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Anomaly detection in crowded scene via appearance and dynamics joint
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ICIP12(2705-2708).
IEEE DOI
1302
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Yu, Y.H.[Yuan-Hao],
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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
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Raghavendra, R.,
del Bue, A.[Alessio],
Cristani, M.[Marco],
Murino, V.[Vittorio],
Optimizing interaction force for global anomaly detection in crowded
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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
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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],
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An Abnormal Event Recognition in Crowd Scene,
ICIG11(731-736).
IEEE DOI
1109
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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
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Wu, S.D.[Shan-Dong],
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Shah, M.[Mubarak],
Chaotic invariants of Lagrangian particle trajectories for anomaly
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CVPR10(2054-2060).
IEEE DOI
1006
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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).
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1008
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Jiang, F.[Fan],
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Katsaggelos, A.K.[Aggelos K.],
Detecting contextual anomalies of crowd motion in surveillance video,
ICIP09(1117-1120).
IEEE DOI
0911
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Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Novelty Detection .