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Huang, C.L.[Chung-Lin],
Multiview-Based Cooperative Tracking of Multiple Human Objects,
JIVP(2008), No. 2008, pp. xx-yy.
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0804
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Multi-view-based Cooperative Tracking of Multiple Human Objects in
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ICPR06(III: 1123-1126).
IEEE DOI
0609
BibRef
Tsai, Y.T.[Yao-Te],
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Multiple Human Objects Tracking in Crowded Scenes,
ICPR06(III: 51-54).
IEEE DOI
0609
BibRef
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Tian, Q.,
An Efficient Sequential Approach to Tracking Multiple Objects Through
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0809
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Activity Representation in Crowd,
SSPR08(107-116).
Springer DOI
0812
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Ma, Y.Q.[Yun-Qian],
Cisar, P.[Petr],
Event detection using local binary pattern based dynamic textures,
VCL-ViSU09(38-44).
IEEE DOI
0906
BibRef
Yogameena, B.,
Veeralakshmi, S.,
Komagal, E.,
Raju, S.,
Abhaikumar, V.,
RVM-Based Human Action Classification in Crowd through Projection and
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JIVP(2009), No. 2009, pp. xx-yy.
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1002
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Automatic spatial analysis and pedestrian flow control for real-time
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0804
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Graphics simulations, not recogniton and tracking.
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A path-based multi-agent navigation model,
VC(31), No. 6-8, June 2015, pp. 863-872.
Springer DOI
1506
BibRef
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Hebert, M.[Martial],
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IJCV(88), No. 3, July 2010, pp. xx-yy.
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1003
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Earlier: A1, Only:
CMU-CS-08-113, March 2008.
BibRef
Ph.D.Thesis, March 2008.
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Matikainen, P.[Pyry],
Sukthankar, R.[Rahul],
Hebert, M.[Martial],
Model recommendation for action recognition,
CVPR12(2256-2263).
IEEE DOI
1208
BibRef
Earlier:
Feature seeding for action recognition,
ICCV11(1716-1723).
IEEE DOI
1201
BibRef
Earlier: A1, A3, A2:
Representing Pairwise Spatial and Temporal Relations for Action
Recognition,
ECCV10(I: 508-521).
Springer DOI
1009
BibRef
Earlier: A1, A3, A2:
Trajectons:
Action recognition through the motion analysis of tracked features,
ObjectEvent09(514-521).
IEEE DOI
0910
BibRef
Ke, Y.[Yan],
Sukthankar, R.[Rahul],
Hebert, M.[Martial],
Event Detection in Crowded Videos,
ICCV07(1-8).
IEEE DOI
0710
BibRef
Earlier:
Spatio-temporal Shape and Flow Correlation for Action Recognition,
VS07(1-8).
IEEE DOI
0706
BibRef
Earlier:
Efficient Visual Event Detection Using Volumetric Features,
ICCV05(I: 166-173).
IEEE DOI
0510
Using 3-D volume features, not just 2-D boxes in event detection
BibRef
Jacques Junior, J.C.S.,
Mussef, S.R.,
Jung, C.R.,
Crowd Analysis Using Computer Vision Techniques,
SPMag(27), No. 5, 2010, pp. 66-77.
IEEE DOI
1003
BibRef
Krausz, B.[Barbara],
Bauckhage, C.[Christian],
Loveparade 2010: Automatic video analysis of a crowd disaster,
CVIU(116), No. 3, March 2012, pp. 307-319.
Elsevier DOI
1201
Crowd behavior; Crowd dynamics; Crowd turbulence; Congestion; Video
analysis; Optical flow
BibRef
Tian, Y.[Ye],
Cao, L.,
Liu, Z.K.[Zhi-Kang],
Wang, Z.[Zilei],
Hierarchical Filtered Motion for Action Recognition in Crowded Videos,
SMC-C(42), No. 3, May 2012, pp. 313-323.
IEEE DOI
1204
BibRef
Liu, Z.K.[Zhi-Kang],
Tian, Y.[Ye],
Wang, Z.[Zilei],
Stacked Overcomplete Independent Component Analysis for Action
Recognition,
ACCV16(II: 368-383).
Springer DOI
1704
BibRef
Solmaz, B.[Berkan],
Moore, B.E.[Brian E.],
Shah, M.[Mubarak],
Identifying Behaviors in Crowd Scenes Using Stability Analysis for
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PAMI(34), No. 10, October 2012, pp. 2064-2070.
IEEE DOI
PDF File.
HTML Version.
1208
Five crowd behaviors (bottlenecks, fountainheads, lanes, arches, and blocking).
Grid of particles defined by optical flow.
BibRef
Plaue, M.[Matthias],
Chen, M.J.[Min-Jie],
Bärwolff, G.[Günter],
Schwandt, H.[Hartmut],
Multi-View Extraction of Dynamic Pedestrian Density Fields,
PFG(2012), No. 5, 2012, pp. 547-555.
WWW Link.
1211
BibRef
Earlier:
Trajectory Extraction and Density Analysis of Intersecting Pedestrian
Flows from Video Recordings,
PIA11(285-296).
Springer DOI
1110
BibRef
Poiesi, F.[Fabio],
Mazzon, R.[Riccardo],
Cavallaro, A.[Andrea],
Multi-target tracking on confidence maps:
An application to people tracking,
CVIU(117), No. 10, 2013, pp. 1257-1272.
Elsevier DOI
1309
BibRef
Earlier: A2, A1, A3:
Detection and tracking of groups in crowd,
AVSS13(202-207)
IEEE DOI
1311
Track-before-detect.
Detectors
BibRef
Lawal, I.A.,
Poiesi, F.[Fabio],
Anguita, D.,
Cavallaro, A.[Andrea],
Support Vector Motion Clustering,
CirSysVideo(27), No. 11, November 2017, pp. 2395-2408.
IEEE DOI
1712
Clustering methods, Indexes, Kernel, Performance evaluation, Shape,
Static VAr compensators, Support vector machines, Crowd analysis,
unsupervised motion clustering
BibRef
Vizzari, G.,
Bandini, S.,
Studying Pedestrian and Crowd Dynamics through Integrated Analysis
and Synthesis,
IEEE_Int_Sys(28), No. 5, Sept 2013, pp. 56-60.
IEEE DOI
1403
computer vision
BibRef
Courty, N.[Nicolas],
Allain, P.[Pierre],
Creusot, C.[Clement],
Corpetti, T.[Thomas],
Using the Agoraset dataset: Assessing for the quality of crowd video
analysis methods,
PRL(44), No. 1, 2014, pp. 161-170.
Elsevier DOI
1407
Crowd video analysis
BibRef
Chrysostomou, D.[Dimitrios],
Sirakoulis, G.C.[Georgios Ch.],
Gasteratos, A.[Antonios],
A bio-inspired multi-camera system for dynamic crowd analysis,
PRL(44), No. 1, 2014, pp. 141-151.
Elsevier DOI
1407
Crowd analysis
BibRef
Fagette, A.[Antoine],
Courty, N.[Nicolas],
Racoceanu, D.[Daniel],
Dufour, J.Y.[Jean-Yves],
Unsupervised dense crowd detection by multiscale texture analysis,
PRL(44), No. 1, 2014, pp. 126-133.
Elsevier DOI
1407
Dense crowd
BibRef
Zawidzki, M.[Machi],
Chraibi, M.[Mohcine],
Nishinari, K.[Katsuhiro],
Crowd-Z: The user-friendly framework for crowd simulation on an
architectural floor plan,
PRL(44), No. 1, 2014, pp. 88-97.
Elsevier DOI
1407
Pedestrian dynamics
BibRef
O'Gorman, L.[Lawrence],
Yin, Y.F.[Ya-Feng],
Ho, T.K.[Tin Kam],
Motion feature filtering for event detection in crowded scenes,
PRL(44), No. 1, 2014, pp. 80-87.
Elsevier DOI
1407
Motion analysis
BibRef
Tran, K.N.,
Gala, A.,
Kakadiaris, I.A.,
Shah, S.K.,
Activity analysis in crowded environments using social cues for group
discovery and human interaction modeling,
PRL(44), No. 1, 2014, pp. 49-57.
Elsevier DOI
1407
Group activity recognition
BibRef
Manfredi, M.[Marco],
Vezzani, R.[Roberto],
Calderara, S.[Simone],
Cucchiara, R.[Rita],
Detection of static groups and crowds gathered in open spaces by
texture classification,
PRL(44), No. 1, 2014, pp. 39-48.
Elsevier DOI
1407
Crowd detection
BibRef
Kountouriotis, V.[Vassilios],
Thomopoulos, S.C.A.[Stelios C.A.],
Papelis, Y.F.[Yi-Fannis],
An agent-based crowd behaviour model for real time crowd behaviour
simulation,
PRL(44), No. 1, 2014, pp. 30-38.
Elsevier DOI
1407
Simulation
BibRef
Bandini, S.[Stefania],
Gorrini, A.[Andrea],
Vizzari, G.[Giuseppe],
Towards an integrated approach to crowd analysis and crowd synthesis:
A case study and first results,
PRL(44), No. 1, 2014, pp. 16-29.
Elsevier DOI
1407
Crowd analysis
BibRef
Ferryman, J.M.[James M.],
Ellis, A.L.[Anna-Louise],
Performance evaluation of crowd image analysis using the PETS2009
dataset,
PRL(44), No. 1, 2014, pp. 3-15.
Elsevier DOI
1407
Surveillance
BibRef
Toledo, L.[Leonel],
de Gyves, O.[Oriam],
Rudomín, I.[Isaac],
Hierarchical level of detail for varied animated crowds,
VC(30), No. 6-8, June 2014, pp. 949-961.
WWW Link.
1407
BibRef
Zhang, Y.H.[Yan-Hao],
Huang, Q.M.[Qing-Ming],
Qin, L.[Lei],
Zhao, S.C.[Si-Cheng],
Yao, H.X.[Hong-Xun],
Xu, P.F.[Peng-Fei],
Representing dense crowd patterns using bag of trajectory graphs,
SIViP(8), No. S1, December 2014, pp. 173-181.
Springer DOI
WWW Link.
1411
BibRef
Earlier: A1, A3, A5, A6, A2, Only:
Beyond particle flow:
Bag of Trajectory Graphs for dense crowd event recognition,
ICIP13(3572-3576)
IEEE DOI
1402
Attributes; Bag of Trajectory Graphs; Crowd Behavior; Event Recognition
BibRef
Zhang, P.[Peng],
Liu, H.[Hong],
Ding, Y.H.[Yan-Hui],
Crowd simulation based on constrained and controlled group formation,
VC(31), No. 1, January 2015, pp. 5-18.
WWW Link.
1503
Graphical synthesis.
BibRef
Li, T.,
Chang, H.,
Wang, M.,
Ni, B.,
Hong, R.,
Yan, S.,
Crowded Scene Analysis: A Survey,
CirSysVideo(25), No. 3, March 2015, pp. 367-386.
IEEE DOI
1503
Survey, Crowds. Analytical models
BibRef
Kim, S.J.[Su-Jeong],
Guy, S.J.[Stephen J.],
Hillesland, K.[Karl],
Zafar, B.[Basim],
Gutub, A.A.A.[Adnan Abdul-Aziz],
Manocha, D.[Dinesh],
Velocity-based modeling of physical interactions in dense crowds,
VC(31), No. 5, May 2015, pp. 541-555.
Springer DOI
1505
BibRef
Mukherjee, S.,
Goswami, D.,
Chatterjee, S.,
A Lagrangian Approach to Modeling and Analysis of a Crowd Dynamics,
SMCS(45), No. 6, June 2015, pp. 865-876.
IEEE DOI
1506
Acceleration
BibRef
Cao, L.J.[Li-Jun],
Zhang, X.[Xu],
Ren, W.Q.[Wei-Qiang],
Huang, K.Q.[Kai-Qi],
Large scale crowd analysis based on convolutional neural network,
PR(48), No. 10, 2015, pp. 3016-3024.
Elsevier DOI
1507
Crowd analysis
BibRef
Jiang, J.[Jun],
Wu, D.[Di],
Teng, Q.Z.[Qi-Zhi],
He, X.H.[Xiao-Hai],
Gao, M.L.[Ming-Liang],
Measuring Collectiveness in Crowded Scenes via Link Prediction,
IEICE(E98-D), No. 8, August 2015, pp. 1617-1620.
WWW Link.
1509
BibRef
Lee, D.G.[Dong-Gyu],
Lee, S.W.[Seong-Whan],
Human activity prediction based on Sub-volume Relationship Descriptor,
ICPR16(2060-2065)
IEEE DOI
1705
Activity recognition, Computational modeling,
Convolutional codes, Feature extraction, Training, Videos
BibRef
Rao, A.S.[Aravinda S.],
Gubbi, J.[Jayavardhana],
Marusic, S.[Slaven],
Palaniswami, M.[Marimuthu],
Estimation of crowd density by clustering motion cues,
VC(31), No. 11, November 2015, pp. 1533-1552.
Springer DOI
1512
BibRef
Rao, A.S.[Aravinda S.],
Gubbi, J.[Jayavardhana],
Marusic, S.[Slaven],
Palaniswami, M.[Marimuthu],
Crowd Event Detection on Optical Flow Manifolds,
Cyber(46), No. 7, July 2016, pp. 1524-1537.
IEEE DOI
1606
BibRef
Earlier:
Probabilistic Detection of Crowd Events on Riemannian Manifolds,
DICTA14(1-8)
IEEE DOI
1502
Event detection
image classification
BibRef
Rao, A.S.[Aravinda S.],
Gubbi, J.[Jayavardhana],
Marusic, S.[Slaven],
Maher, A.[Andrew],
Determination of Object Directions Using Optical Flow for Crowd
Monitoring,
ISVC13(II:613-622).
Springer DOI
1311
BibRef
Rao, A.S.,
Gubbi, J.,
Rajasegarar, S.,
Marusic, S.,
Palaniswami, M.,
Detection of Anomalous Crowd Behaviour Using Hyperspherical
Clustering,
DICTA14(1-8)
IEEE DOI
1502
object detection
BibRef
Qian, S.S.[Sheng-Sheng],
Zhang, T.Z.[Tian-Zhu],
Xu, C.S.[Chang-Sheng],
Shao, J.,
Multi-Modal Event Topic Model for Social Event Analysis,
MultMed(18), No. 2, February 2016, pp. 233-246.
IEEE DOI
1601
BibRef
Earlier: A1, A2, A3, Only:
Boosted Multi-modal Supervised Latent Dirichlet Allocation for Social
Event Classification,
ICPR14(1999-2004)
IEEE DOI
1412
Google.
Analytical models
BibRef
Vascon, S.[Sebastiano],
Mequanint, E.Z.[Eyasu Zemene],
Cristani, M.[Marco],
Hung, H.[Hayley],
Pelillo, M.[Marcello],
Murino, V.[Vittorio],
Detecting conversational groups in images and sequences:
A robust game-theoretic approach,
CVIU(143), No. 1, 2016, pp. 11-24.
Elsevier DOI
1601
BibRef
Earlier:
A Game-Theoretic Probabilistic Approach for Detecting Conversational
Groups,
ACCV14(V: 658-675).
Springer DOI
1504
Group detection
BibRef
Mousavi, H.[Hossein],
Nabi, M.[Moin],
Kiani, H.[Hamed],
Perina, A.[Alessandro],
Murino, V.[Vittorio],
Crowd motion monitoring using tracklet-based commotion measure,
ICIP15(2354-2358)
IEEE DOI
1512
Video analysis; abnormal detection; motion commotion; tracklets
BibRef
Mohammadi, S.[Sadegh],
Kiani, H.[Hamed],
Perina, A.[Alessandro],
Murino, V.[Vittorio],
A comparison of crowd commotion measures from generative models,
Crowd15(49-55)
IEEE DOI
1510
Cameras
BibRef
Lin, W.Y.[Wei-Yao],
Mi, Y.,
Wang, W.Y.[Wei-Yue],
Wu, J.X.[Jian-Xin],
Wang, J.D.[Jing-Dong],
Mei, T.,
A Diffusion and Clustering-Based Approach for Finding Coherent
Motions and Understanding Crowd Scenes,
IP(25), No. 4, April 2016, pp. 1674-1687.
IEEE DOI
1604
Correlation
BibRef
Wang, W.Y.[Wei-Yue],
Lin, W.Y.[Wei-Yao],
Chen, Y.Z.[Yuan-Zhe],
Wu, J.X.[Jian-Xin],
Wang, J.D.[Jing-Dong],
Sheng, B.[Bin],
Finding Coherent Motions and Semantic Regions in Crowd Scenes:
A Diffusion and Clustering Approach,
ECCV14(I: 756-771).
Springer DOI
1408
BibRef
Pennisi, A.[Andrea],
Bloisi, D.D.[Domenico D.],
Iocchi, L.[Luca],
Online real-time crowd behavior detection in video sequences,
CVIU(144), No. 1, 2016, pp. 166-176.
Elsevier DOI
1604
Event detection
BibRef
Solera, F.[Francesco],
Calderara, S.[Simone],
Cucchiara, R.[Rita],
Socially Constrained Structural Learning for Groups Detection in
Crowd,
PAMI(38), No. 5, May 2016, pp. 995-1008.
IEEE DOI
1604
Analytical models
BibRef
Earlier:
Learning to identify leaders in crowd,
Crowd15(43-48)
IEEE DOI
1510
BibRef
Earlier:
Structured learning for detection of social groups in crowd,
AVSS13(7-12)
IEEE DOI
1311
BibRef
And:
Social Groups Detection in Crowd through Shape-Augmented Structured
Learning,
CIAP13(I:542-551).
Springer DOI
1311
Acceleration.
Correlation
BibRef
Yuan, Y.[Yuan],
Wan, J.[Jia],
Wang, Q.[Qi],
Congested scene classification via efficient unsupervised feature
learning and density estimation,
PR(56), No. 1, 2016, pp. 159-169.
Elsevier DOI
1604
Computer vision
BibRef
Guo, B.,
Yu, Z.,
Chen, L.,
Zhou, X.,
Ma, X.,
MobiGroup: Enabling Lifecycle Support to Social Activity Organization
and Suggestion With Mobile Crowd Sensing,
HMS(46), No. 3, June 2016, pp. 390-402.
IEEE DOI
1605
Advertising
BibRef
Zhang, C.,
Kang, K.,
Li, H.,
Wang, X.,
Xie, R.,
Yang, X.,
Data-Driven Crowd Understanding:
A Baseline for a Large-Scale Crowd Dataset,
MultMed(18), No. 6, June 2016, pp. 1048-1061.
IEEE DOI
1605
Benchmark testing
BibRef
Liu, W.X.[Wen-Xi],
Lau, R.W.H.[Rynson W.H.],
Manocha, D.[Dinesh],
Robust individual and holistic features for crowd scene
classification,
PR(58), No. 1, 2016, pp. 110-120.
Elsevier DOI
1606
Crowd analysis
BibRef
Meynberg, O.[Oliver],
Cui, S.Y.[Shi-Yong],
Reinartz, P.[Peter],
Detection of High-Density Crowds in Aerial Images Using Texture
Classification,
RS(8), No. 6, 2016, pp. 470.
DOI Link
1608
BibRef
Yi, S.[Shuai],
Li, H.S.[Hong-Sheng],
Wang, X.G.[Xiao-Gang],
Pedestrian Behavior Modeling From Stationary Crowds With Applications
to Intelligent Surveillance,
IP(25), No. 9, September 2016, pp. 4354-4368.
IEEE DOI
1609
BibRef
And:
Pedestrian Behavior Understanding and Prediction with Deep Neural
Networks,
ECCV16(I: 263-279).
Springer DOI
1611
behavioural sciences computing
BibRef
Ma, Y.,
Lin, T.,
Cao, Z.,
Li, C.,
Wang, F.,
Chen, W.,
Mobility Viewer: An Eulerian Approach for Studying Urban Crowd Flow,
ITS(17), No. 9, September 2016, pp. 2627-2636.
IEEE DOI
1609
Cities and towns
BibRef
Deng, C.,
Cao, Z.,
Xiao, Y.,
Lu, H.,
Xian, K.,
Chen, Y.,
Exploiting Attribute Dependency for Attribute Assignment in Crowded
Scenes,
SPLetters(23), No. 10, October 2016, pp. 1325-1329.
IEEE DOI
1610
feature extraction
BibRef
Shao, J.[Jing],
Loy, C.C.[Chen Change],
Kang, K.[Kai],
Wang, X.G.[Xiao-Gang],
Crowded Scene Understanding by Deeply Learned Volumetric Slices,
CirSysVideo(27), No. 3, March 2017, pp. 613-623.
IEEE DOI
1703
BibRef
Earlier: A1, A3, A2, A4:
Deeply learned attributes for crowded scene understanding,
CVPR15(4657-4666)
IEEE DOI
1510
Feature extraction
BibRef
Shao, J.[Jing],
Loy, C.C.[Chen Change],
Wang, X.G.[Xiao-Gang],
Learning Scene-Independent Group Descriptors for Crowd Understanding,
CirSysVideo(27), No. 6, June 2017, pp. 1290-1303.
IEEE DOI
1706
BibRef
Earlier:
Scene-Independent Group Profiling in Crowd,
CVPR14(2227-2234)
IEEE DOI
1409
Circuit stability, Feature extraction, Hidden Markov models,
Psychology, Robustness, Stability analysis, Visualization,
Crowded scene understanding, group-property analysis, video, analysis
BibRef
Yi, S.[Shuai],
Li, H.S.[Hong-Sheng],
Wang, X.G.[Xiao-Gang],
Understanding pedestrian behaviors from stationary crowd groups,
CVPR15(3488-3496)
IEEE DOI
1510
BibRef
Luchetti, G.[Gioele],
Mancini, A.[Adriano],
Sturari, M.[Mirco],
Frontoni, E.[Emanuele],
Zingaretti, P.[Primo],
Whistland: An Augmented Reality Crowd-Mapping System for Civil
Protection and Emergency Management,
IJGI(6), No. 2, 2017, pp. xx-yy.
DOI Link
1703
BibRef
Ruhhammer, C.,
Baumann, M.,
Protschky, V.,
Kloeden, H.,
Klanner, F.,
Stiller, C.,
Automated Intersection Mapping From Crowd Trajectory Data,
ITS(18), No. 3, March 2017, pp. 666-677.
IEEE DOI
1703
Automobiles
BibRef
Fradi, H.,
Luvison, B.,
Pham, Q.C.[Quoc Cuong],
Crowd Behavior Analysis Using Local Mid-Level Visual Descriptors,
CirSysVideo(27), No. 3, March 2017, pp. 589-602.
IEEE DOI
1703
Character recognition
BibRef
de Almeida, I.R.,
Cassol, V.J.,
Badler, N.I.,
Musse, S.R.,
Jung, C.R.,
Detection of Global and Local Motion Changes in Human Crowds,
CirSysVideo(27), No. 3, March 2017, pp. 603-612.
IEEE DOI
1703
Adaptive optics
BibRef
Zhang, Y.,
Qin, L.,
Ji, R.,
Zhao, S.,
Huang, Q.,
Luo, J.,
Exploring Coherent Motion Patterns via Structured Trajectory Learning
for Crowd Mood Modeling,
CirSysVideo(27), No. 3, March 2017, pp. 635-648.
IEEE DOI
1703
Context
BibRef
Yi, S.[Shuai],
Wang, X.G.[Xiao-Gang],
Lu, C.W.[Ce-Wu],
Jia, J.Y.[Jia-Ya],
Li, H.,
L_0 Regularized Stationary-Time Estimation for Crowd Analysis,
PAMI(39), No. 5, May 2017, pp. 981-994.
IEEE DOI
1704
BibRef
Earlier: A1, A2, A3, A4, Only:
L_0 Regularized Stationary Time Estimation for Crowd Group Analysis,
CVPR14(2219-2226)
IEEE DOI
1409
Algorithm design and analysis
BibRef
Setti, F.[Francesco],
Conigliaro, D.[Davide],
Rota, P.[Paolo],
Bassetti, C.[Chiara],
Conci, N.[Nicola],
Sebe, N.[Nicu],
Cristani, M.[Marco],
The S-Hock dataset: A new benchmark for spectator crowd analysis,
CVIU(159), No. 1, 2017, pp. 47-58.
Elsevier DOI
1706
Dataset, Crowd Analysis.
BibRef
Earlier: A2, A3, A1, A4, A5, A6, A7:
The S-HOCK dataset: Analyzing crowds at the stadium,
CVPR15(2039-2047)
IEEE DOI
1510
Spectator, monitoring
BibRef
Setti, F.[Francesco],
Conigliaro, D.[Davide],
Tobanelli, M.,
Cristani, M.,
Count on Me: Learning to Count on a Single Image,
CirSysVideo(28), No. 8, August 2018, pp. 1798-1806.
IEEE DOI
1808
Feature extraction, Visualization, Detectors, Training, Lattices,
Algebra, Congealing Lie algebra, object counting,
template matching
BibRef
Setti, F.[Francesco],
Cristani, M.[Marco],
Evaluating the Group Detection Performance: The GRODE Metrics,
PAMI(41), No. 3, March 2019, pp. 566-580.
IEEE DOI
1902
BibRef
Earlier:
The GRODE metrics:
Exploring the performance of group detection approaches,
Crowd15(36-42)
IEEE DOI
1510
Measurement, Surveillance, Feature extraction,
Detectors, Signal processing, Standards, Group detection,
social signal processing.
Accuracy; Cameras; Detectors; Head; Magnetic heads; Measurement; Standards
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Dhall, A.,
Joshi, J.,
Sikka, K.,
Goecke, R.,
Sebe, N.,
The more the merrier: Analysing the affect of a group of people in
images,
FG15(1-8)
IEEE DOI
1508
emotion recognition
BibRef
Wu, S.[Shuang],
Yang, H.[Hua],
Zheng, S.[Shibao],
Su, H.[Hang],
Fan, Y.W.[Ya-Wen],
Yang, M.H.[Ming-Hsuan],
Crowd Behavior Analysis via Curl and Divergence of Motion Trajectories,
IJCV(123), No. 3, July 2017, pp. 499-519.
Springer DOI
1706
BibRef
Chen, L.B.[Long-Biao],
Jakubowicz, J.[Jérémie],
Yang, D.Q.[Ding-Qi],
Zhang, D.Q.[Da-Qing],
Pan, G.[Gang],
Fine-Grained Urban Event Detection and Characterization Based on
Tensor Cofactorization,
HMS(47), No. 3, June 2017, pp. 380-391.
IEEE DOI
1706
Data integration, Event detection, Global Positioning System,
Semantics, Tensile stress, Urban planning, Event detection,
tensor factorization, urban data
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Tan, S.,
Wang, Y.,
Chen, Y.,
Wang, Z.,
Evolutionary Dynamics of Collective Behavior Selection and Drift:
Flocking, Collapse, and Oscillation,
Cyber(47), No. 7, July 2017, pp. 1694-1705.
IEEE DOI
1706
Game theory, Games, Mathematical model, Oscillators, Sociology,
Statistics, Behavior networks, behavior patterns,
evolutionary dynamics, game theory, stable, equilibrium, point
BibRef
Tan, K.[Kai],
Xu, L.F.[Lin-Feng],
Liu, Y.N.[Yi-Nan],
Luo, B.[Bing],
Small Group Detection in Crowds using Interaction Information,
IEICE(E100-D), No. 7, July 2017, pp. 1542-1545.
WWW Link.
1708
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Wu, S.[Shuang],
Su, H.[Hang],
Yang, H.[Hua],
Zheng, S.[Shibao],
Fan, Y.W.[Ya-Wen],
Zhou, Q.[Qin],
Bilinear dynamics for crowd video analysis,
JVCIR(48), No. 1, 2017, pp. 461-470.
Elsevier DOI
1708
BibRef
Earlier: A1, A2, A4, A3, A6, Only:
Motion sketch based crowd video retrieval via motion structure coding,
ICIP16(1205-1209)
IEEE DOI
1610
Bilinear dynamics.
Encoding
BibRef
Huang, W.[Wei],
Fan, H.C.[Hong-Chao],
Zipf, A.[Alexander],
Towards Detecting the Crowd Involved in Social Events,
IJGI(6), No. 10, 2017, pp. xx-yy.
DOI Link
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BibRef
Zhao, W.Q.[Wei-Qi],
Zhang, Z.[Zhang],
Huang, K.Q.[Kai-Qi],
Gestalt laws based tracklets analysis for human crowd understanding,
PR(75), No. 1, 2018, pp. 112-127.
Elsevier DOI
1712
BibRef
Earlier:
Joint crowd detection and semantic scene modeling using a Gestalt
laws-based similarity,
ICIP16(1220-1224)
IEEE DOI
1610
Similarity measurement.
Algorithm design and analysis
BibRef
Draghici, A.[Adriana],
van Steen, M.[Maarten],
A Survey of Techniques for Automatically Sensing the Behavior of a
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Surveys(51), No. 1, 2018, pp. Article No 21.
DOI Link
1804
Survey, Crowds.
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Zhang, R.C.[Ri-Chong],
Mao, Y.Y.[Yong-Yi],
On the integration of crowd knowledge in pattern recognition,
PRL(106), 2018, pp. 1-6.
Elsevier DOI
1804
Knowledge integration, Crowd recognition
BibRef
Dhamecha, T.I.[Tejas I.],
Shah, M.[Mahek],
Verma, P.[Priyanka],
Vatsa, M.[Mayank],
Singh, R.[Richa],
CrowdFaceDB: Database and benchmarking for face verification in crowd,
PRL(107), 2018, pp. 17-24.
Elsevier DOI
1805
Face detection, Face recognition, Benchmark database
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Zhang, J.[Junbo],
Zheng, Y.[Yu],
Qi, D.[Dekang],
Li, R.Y.[Rui-Yuan],
Yi, X.W.[Xiu-Wen],
Li, T.R.[Tian-Rui],
Predicting citywide crowd flows using deep spatio-temporal residual
networks,
AI(259), 2018, pp. 147-166.
Elsevier DOI
1805
Convolutional neural networks, Spatio-temporal data,
Residual learning, Crowd flows, Cloud
BibRef
Zaki, M.H.,
Sayed, T.,
Automated Analysis of Pedestrian Group Behavior in Urban Settings,
ITS(19), No. 6, June 2018, pp. 1880-1889.
IEEE DOI
1806
Data collection, Legged locomotion, Tracking,
Trajectory, Pedestrian behavior, pedestrian count,
video analysis
BibRef
Liu, C.Y.[Chun-Yu],
Liao, W.H.[Wei-Hao],
Ruan, S.J.[Shanq-Jang],
Crowd Gathering Detection Based on the Foreground Stillness Model,
IEICE(E101-D), No. 7, July 2018, pp. 1968-1971.
WWW Link.
1807
BibRef
Kaiser, M.S.,
Lwin, K.T.,
Mahmud, M.,
Hajializadeh, D.,
Chaipimonplin, T.,
Sarhan, A.,
Hossain, M.A.,
Advances in Crowd Analysis for Urban Applications Through Urban Event
Detection,
ITS(19), No. 10, October 2018, pp. 3092-3112.
IEEE DOI
1810
Sensors, Social network services, Estimation, Event detection,
Radio frequency, Data mining, Video surveillance, Urban sensing,
benchmark datasets
BibRef
Wang, Q.,
Dong, H.,
Ning, B.,
Wang, L.Y.,
Yin, G.,
Two-Time-Scale Hybrid Traffic Models for Pedestrian Crowds,
ITS(19), No. 11, November 2018, pp. 3449-3460.
IEEE DOI
1812
pedestrians, road vehicles, stochastic processes,
traffic congestion scenarios, faster lanes, crowd behavior,
stochastic approximation
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Li, Y.,
A Deep Spatiotemporal Perspective for Understanding Crowd Behavior,
MultMed(20), No. 12, December 2018, pp. 3289-3297.
IEEE DOI
1812
behavioural sciences computing,
feature extraction, image classification, image motion analysis,
deep neural networks
BibRef
Zou, Y.,
Zhao, X.,
Liu, Y.,
Measuring Crowd Collectiveness by Macroscopic and Microscopic Motion
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MultMed(20), No. 12, December 2018, pp. 3311-3323.
IEEE DOI
1812
data mining, image motion analysis, video surveillance,
macroscopic motion consistencies, surveillance applications,
maximum consistency path
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Zhou, Y.R.[Yi-Rong],
Chen, H.[Hao],
Li, J.[Jun],
Wu, Y.[Ye],
Wu, J.J.[Jiang-Jiang],
Chen, L.[Luo],
Large-Scale Station-Level Crowd Flow Forecast with ST-Unet,
IJGI(8), No. 3, 2019, pp. xx-yy.
DOI Link
1903
BibRef
Dogan, Y.[Yalim],
Demirci, S.[Serkan],
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Dibeklioglu, H.[Hamdi],
Augmentation of virtual agents in real crowd videos,
SIViP(13), No. 4, June 2019, pp. 643-650.
Springer DOI
1906
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Tripathi, G.[Gaurav],
Singh, K.[Kuldeep],
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Convolutional neural networks for crowd behaviour analysis: a survey,
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1906
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Favaretto, R.M.[Rodolfo Migon],
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Vilanova, F.[Felipe],
Costa, Â.B.[Ângelo Brandelli],
Detecting personality and emotion traits in crowds from video sequences,
MVA(30), No. 5, July 2019, pp. 999-101.
Springer DOI
1907
BibRef
Shehab, D.[Doaa],
Ammar, H.[Heyfa],
Statistical detection of a panic behavior in crowded scenes,
MVA(30), No. 5, July 2019, pp. 919-931.
Springer DOI
1907
BibRef
Song, X.,
Xie, H.,
Sun, J.,
Han, D.,
Cui, Y.,
Chen, B.,
Simulation of Pedestrian Rotation Dynamics Near Crowded Exits,
ITS(20), No. 8, August 2019, pp. 3142-3155.
IEEE DOI
1908
Mathematical model, Force, Torque, Shape, Computational modeling,
Microscopy, Torso, Pedestrian behavior, rotation torque, competitive,
gyroscope
BibRef
Mahmood, A.[Arif],
Al-Maadeed, S.[Somaya],
Action recognition in poor-quality spectator crowd videos using head
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MVA(30), No. 6, September 2019, pp. 1083-1096.
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1909
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Ebrahimpour, Z.[Zeinab],
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Cervantes, O.[Ofelia],
Luo, T.H.[Tian-Hang],
Ullah, H.[Hidayat],
Comparison of Main Approaches for Extracting Behavior Features from
Crowd Flow Analysis,
IJGI(8), No. 10, 2019, pp. xx-yy.
DOI Link
1910
BibRef
Wang, Q.[Qi],
Chen, M.L.[Mu-Lin],
Nie, F.P.[Fei-Ping],
Li, X.L.[Xue-Long],
Detecting Coherent Groups in Crowd Scenes by Multiview Clustering,
PAMI(42), No. 1, January 2020, pp. 46-58.
IEEE DOI
1912
Feature extraction, Clustering methods, Optical imaging,
Videos, Computer science, Correlation,
graph clustering
BibRef
Qin, K.[Kun],
Xu, Y.Q.[Yuan-Quan],
Kang, C.G.[Chao-Gui],
Sobolevsky, S.[Stanislav],
Kwan, M.P.[Mei-Po],
Modeling Spatio-Temporal Evolution of Urban Crowd Flows,
IJGI(8), No. 12, 2019, pp. xx-yy.
DOI Link
1912
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Mao, Y.[Yan],
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Li, Y.J.[Yong-Jian],
He, W.[Wu],
Emotion-based diversity crowd behavior simulation in public emergency,
VC(35), No. 12, December 2018, pp. 1725-1739.
WWW Link.
1912
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Petrasova, A.[Anna],
Hipp, J.A.[J. Aaron],
Mitasova, H.[Helena],
Visualization of Pedestrian Density Dynamics Using Data Extracted
from Public Webcams,
IJGI(8), No. 12, 2019, pp. xx-yy.
DOI Link
1912
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You, Q.Z.[Quan-Zeng],
Jiang, H.[Hao],
Action4D: Online Action Recognition in the Crowd and Clutter,
CVPR19(11849-11858).
IEEE DOI
2002
BibRef
Medynska-Gulij, B.[Beata],
Wielebski, L.[Lukasz],
Halik, L.[Lukasz],
Smaczynski, M.[Maciej],
Complexity Level of People Gathering Presentation on an Animated Map:
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IJGI(9), No. 2, 2020, pp. xx-yy.
DOI Link
2003
BibRef
Mei, L.,
Lai, J.,
Chen, Z.,
Xie, X.,
Measuring Crowd Collectiveness via Global Motion Correlation,
HBU19(1222-1231)
IEEE DOI
2004
image motion analysis, image sequences, optical flow,
energy spread process, crowd scene behavior consistency,
BibRef
Li, X.,
Chen, M.,
Wang, Q.,
Quantifying and Detecting Collective Motion in Crowd Scenes,
IP(29), 2020, pp. 5571-5583.
IEEE DOI
2005
Dynamics, Feature extraction, Motion detection, Manifolds,
Optical imaging, Robustness, Trajectory, Crowd analysis,
Clustering
BibRef
Yang, B.[Bing],
Kang, Y.[Yan],
Li, H.[Hao],
Zhang, Y.[Yachuan],
Yang, Y.[Yan],
Zhang, L.[Lan],
Spatio-temporal expand-and-squeeze networks for crowd flow prediction
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IET-ITS(14), No. 5, May 2020, pp. 313-322.
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2005
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BibRef
Earlier:
Graph-Based Correlated Topic Model for Trajectory Clustering in
Crowded Videos,
WACV18(1029-1037)
IEEE DOI
1806
graph theory, image motion analysis, inference mechanisms,
pattern clustering, video signal processing, video surveillance,
Visualization
BibRef
Li, Q.,
Zhao, X.,
He, R.,
Huang, K.,
Recurrent Prediction With Spatio-Temporal Attention for Crowd
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CirSysVideo(30), No. 7, July 2020, pp. 2167-2177.
IEEE DOI
2007
Semantics, Task analysis, Visualization, Context modeling,
Correlation, Predictive models, Automation,
multi-label classification
BibRef
Zhao, R.,
Hu, Q.,
Liu, Q.,
Li, C.,
Dong, D.,
Ma, Y.,
Panic Propagation Dynamics of High-Density Crowd Based on Information
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IEEE DOI
2010
Entropy, Information entropy, Microscopy, Force, Analytical models,
Psychology, Numerical models, High-density crowd,
AW-Rascle model
BibRef
Sohn, S.S.[Samuel S.],
Zhou, H.[Honglu],
Moon, S.[Seonghyeon],
Yoon, S.[Sejong],
Pavlovic, V.[Vladimir],
Kapadia, M.[Mubbasir],
Laying the Foundations of Deep Long-term Crowd Flow Prediction,
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Springer DOI
2010
BibRef
Bisagno, N.[Niccoló],
Saltori, C.[Cristiano],
Zhang, B.[Bo],
de Natale, F.G.B.[Francesco G.B.],
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Embedding group and obstacle information in LSTM networks for human
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CVIU(203), 2021, pp. 103126.
Elsevier DOI
2101
BibRef
Earlier: A1, A3, A5, Only:
Group LSTM: Group Trajectory Prediction in Crowded Scenarios,
AnticipateBeh18(III:213-225).
Springer DOI
1905
Trajectory prediction, Group, Obstacle, LSTM-based
BibRef
Bisagno, N.[Niccoló],
Garau, N.[Nicola],
Montagner, A.[Andrea],
Conci, N.[Nicola],
Virtual Crowds: An LSTM-Based Framework for Crowd Simulation,
CIAP19(I:117-127).
Springer DOI
1909
BibRef
Xu, M.L.[Ming-Liang],
Xie, X.Z.[Xiao-Zheng],
Lv, P.[Pei],
Niu, J.W.[Jian-Wei],
Wang, H.[Hua],
Li, C.C.[Chao-Chao],
Zhu, R.J.[Rui-Jie],
Deng, Z.G.[Zhi-Gang],
Zhou, B.[Bing],
Crowd Behavior Simulation With Emotional Contagion in Unexpected
Multihazard Situations,
SMCS(51), No. 3, March 2021, pp. 1567-1581.
IEEE DOI
2102
Hazards, Solid modeling, Psychology, Computational modeling, Stress,
Collision avoidance, Navigation, Crowd simulation,
multihazard
BibRef
Li, C.C.[Chao-Chao],
Lv, P.[Pei],
Manocha, D.[Dinesh],
Wang, H.[Hua],
Li, Y.[Yafei],
Zhou, B.[Bing],
Xu, M.L.[Ming-Liang],
ACSEE: Antagonistic Crowd Simulation Model With Emotional Contagion
and Evolutionary Game Theory,
AffCom(13), No. 2, April 2022, pp. 729-745.
IEEE DOI
2206
Game theory, Games, Solid modeling, Force, Psychology,
Biological system modeling, Group violence, emotional contagion,
evolutionary game theory.
BibRef
Lv, P.[Pei],
Yu, Q.Q.[Qing-Qing],
Xu, B.[Boya],
Li, C.C.[Chao-Chao],
Zhou, B.[Bing],
Xu, M.L.[Ming-Liang],
Emotional Contagion-Aware Deep Reinforcement Learning for
Antagonistic Crowd Simulation,
AffCom(14), No. 4, October 2023, pp. 2939-2953.
IEEE DOI
2312
BibRef
Tanaka, Y.[Yusuke],
Iwata, T.[Tomoharu],
Kurashima, T.[Takeshi],
Toda, H.[Hiroyuki],
Ueda, N.[Naonori],
Tanaka, T.[Toshiyuki],
Time-delayed collective flow diffusion models for inferring latent
people flow from aggregated data at limited locations,
AI(292), 2021, pp. 103430.
Elsevier DOI
2102
Collective graphical models, Travel duration, Aggregated population data
BibRef
Behera, S.[Shreetam],
Dogra, D.P.[Debi Prosad],
Bandyopadhyay, M.K.[Malay Kumar],
Roy, P.P.[Partha Pratim],
Understanding crowd flow patterns using active-Langevin model,
PR(119), 2021, pp. 108037.
Elsevier DOI
2106
Visual surveillance, Active Langevin equation, Crowd analysis,
Human flow segmentation, Dense crowd
BibRef
Kang, J.P.[Jun-Peng],
Zhang, J.[Jing],
Li, W.S.[Wen-Sheng],
Zhuo, L.[Li],
Crowd activity recognition in live video streaming via 3D-ResNet and
region graph convolution network,
IET-IPR(15), No. 14, 2021, pp. 3476-3486.
DOI Link
2112
BibRef
Liu, M.Y.[Ming-Yu],
Meng, F.M.[Fan-Man],
Wu, Q.B.[Qing-Bo],
Xu, L.F.[Lin-Feng],
Liao, Q.H.[Qiang-Hua],
Behaviour detection in crowded classroom scenes via enhancing
features robust to scale and perspective variations,
IET-IPR(15), No. 14, 2021, pp. 3466-3475.
DOI Link
2112
BibRef
Sun, B.Y.[Bang-Yong],
Yuan, N.Z.[Nian-Zzeng],
Li, S.Y.[Shu-Ying],
Wu, S.Y.[Si-Yuan],
Wang, N.[Nan],
Human behaviour recognition with mid-level representations for crowd
understanding and analysis,
IET-IPR(15), No. 14, 2021, pp. 3414-3424.
DOI Link
2112
BibRef
Wang, Q.[Qi],
Liu, B.[Bo],
Lin, J.Z.[Jian-Zhe],
Crowd understanding and analysis,
IET-IPR(15), No. 14, 2021, pp. 3411-3413.
DOI Link
2112
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BibRef
Wang, S.Z.[Sen-Zhang],
Miao, H.[Hao],
Li, J.[Jiyue],
Cao, J.N.[Jian-Nong],
Spatio-Temporal Knowledge Transfer for Urban Crowd Flow Prediction
via Deep Attentive Adaptation Networks,
ITS(23), No. 5, May 2022, pp. 4695-4705.
IEEE DOI
2205
Urban areas, Predictive models, Transfer learning, Data models,
Deep learning, Task analysis, Adaptation models,
crowd flow prediction
BibRef
Quach, K.G.[Kha Gia],
Le, N.[Ngan],
Duong, C.N.[Chi Nhan],
Jalata, I.[Ibsa],
Roy, K.[Kaushik],
Luu, K.[Khoa],
Non-volume preserving-based fusion to group-level emotion recognition
on crowd videos,
PR(128), 2022, pp. 108646.
Elsevier DOI
2205
Group-level emotion recognition, Facial features,
Feature extraction, Feature fusion, Crowd videos
BibRef
Varghese, E.B.[Elizabeth B.],
Thampi, S.M.[Sabu M.],
Berretti, S.[Stefano],
A Psychologically Inspired Fuzzy Cognitive Deep Learning Framework to
Predict Crowd Behavior,
AffCom(13), No. 2, April 2022, pp. 1005-1022.
IEEE DOI
2206
Computational modeling, Psychology, Predictive models,
Feature extraction, Machine learning, Visualization, Videos,
convolutional LSTM (Conv LSTM)
BibRef
Huang, X.H.[Xiao-Hua],
Dhall, A.[Abhinav],
Goecke, R.[Roland],
Pietikäinen, M.[Matti],
Zhao, G.Y.[Guo-Ying],
Analyzing Group-Level Emotion with Global Alignment Kernel based
Approach,
AffCom(13), No. 2, April 2022, pp. 713-728.
IEEE DOI
2206
Kernel, Emotion recognition, Face recognition, Mood,
Computational modeling, Group-level emotion recognition,
convolution neural network
BibRef
Rezaee, K.[Khosro],
Mousavirad, S.J.[Seyed Jalaleddin],
Khosravi, M.R.[Mohammad R.],
Moghimi, M.K.[Mohammad Kazem],
Heidari, M.[Mohsen],
An Autonomous UAV-Assisted Distance-Aware Crowd Sensing Platform
Using Deep ShuffleNet Transfer Learning,
ITS(23), No. 7, July 2022, pp. 9404-9413.
IEEE DOI
2207
Social factors, Human factors, Monitoring, Videos, COVID-19,
Kalman filters, Unmanned aerial vehicles,
modified ShuffleNet
BibRef
Wu, W.H.[Wen-Han],
Chen, M.Y.[Mao-Yin],
Li, J.H.[Jing-Hai],
Liu, B.L.[Bing-Lu],
Zheng, X.P.[Xiao-Ping],
An Extended Social Force Model via Pedestrian Heterogeneity Affecting
the Self-Driven Force,
ITS(23), No. 7, July 2022, pp. 7974-7986.
IEEE DOI
2207
Psychology, Force, Physiology, Dynamics, Stress, Shape, Microscopy,
Crowd dynamics, social force model, pedestrian heterogeneity,
nonlinear system
BibRef
Wu, W.H.[Wen-Han],
Li, J.H.[Jing-Hai],
Yi, W.F.[Wen-Feng],
Zheng, X.P.[Xiao-Ping],
Modeling Crowd Evacuation via Behavioral Heterogeneity-Based Social
Force Model,
ITS(23), No. 9, September 2022, pp. 15476-15486.
IEEE DOI
2209
Mathematical models, Indexes, Force, Stress, Psychology, Physiology,
Dynamics, Crowd dynamics, social force model,
nonlinear system
BibRef
Bruno, A.[Alessandro],
Ferjani, M.[Marouane],
Sabeur, Z.[Zoheir],
Arbab-Zavar, B.[Banafshe],
Cetinkaya, D.[Deniz],
Johnstone, L.[Liam],
Sallal, M.[Muntadher],
Benaouda, D.[Djamel],
High-Level Feature Extraction for Crowd Behaviour Analysis:
A Computer Vision Approach,
HBAxSCES22(59-70).
Springer DOI
2208
BibRef
Yuan, Y.F.[Yi-Fei],
Son, Y.J.[Young-Jun],
Liu, J.[Jian],
Bayesian Modeling of Crowd Dynamics by Aggregating Multiresolution
Observations From UAVs and UGVs,
SMCS(52), No. 10, October 2022, pp. 6406-6417.
IEEE DOI
2209
Computational modeling, Vehicle dynamics, Surveillance, Dynamics,
Data models, Predictive models, Load modeling, Crowd surveillance,
prior elicitation
BibRef
Li, H.P.[Hao-Peng],
Liu, L.B.[Ling-Bo],
Yang, K.L.[Kun-Lin],
Liu, S.N.[Shi-Nan],
Gao, J.Y.[Jun-Yu],
Zhao, B.[Bin],
Zhang, R.[Rui],
Hou, J.[Jun],
Video Crowd Localization With Multifocus Gaussian Neighborhood
Attention and a Large-Scale Benchmark,
IP(31), 2022, pp. 6032-6047.
IEEE DOI
2209
Head, Location awareness, Task analysis, Feature extraction,
Annotations, Convolutional neural networks, Context modeling,
spatial-temporal modeling
BibRef
Zhao, R.Y.[Rong-Yong],
Liu, Q.[Qiong],
Wang, Y.[Yan],
Jia, P.[Ping],
Li, C.L.[Cui-Ling],
Ma, Y.L.[Yun-Long],
Zhu, W.J.[Wen-Jie],
Dynamic Crowd Accident-Risk Assessment Based on Internal Energy and
Information Entropy for Large-Scale Crowd Flow Considering COVID-19
Epidemic,
ITS(23), No. 10, October 2022, pp. 17466-17478.
IEEE DOI
2210
Accidents, Risk management, Epidemics, COVID-19, Analytical models,
Rail transportation, Information entropy, Crowd accident,
COVID-19 epidemic
BibRef
Yu, B.[Bin],
Parallel Simulation of Crowd Multi-Cell Occupancy and Velocity
Variety,
ITS(23), No. 10, October 2022, pp. 17506-17515.
IEEE DOI
2210
Geometry, Heuristic algorithms, Graphics processing units,
Upper bound, Automata, Mathematical models, parallel algorithm
BibRef
Xie, Y.[Yulai],
Niu, J.J.[Jing-Jing],
Zhang, Y.[Yang],
Ren, F.[Fang],
Multisize Patched Spatial-Temporal Transformer Network for Short- and
Long-Term Crowd Flow Prediction,
ITS(23), No. 11, November 2022, pp. 21548-21568.
IEEE DOI
2212
Sensors, Transformers, Predictive models, Task analysis,
Public transportation, Encoding, Deep learning, multi-task learning
BibRef
Giraldo, J.J.[Juan-José],
Zhang, J.[Jie],
Álvarez, M.A.[Mauricio A.],
Correlated Chained Gaussian Processes for Modelling Citizens Mobility
Using a Zero-Inflated Poisson Likelihood,
ITS(23), No. 11, November 2022, pp. 20337-20351.
IEEE DOI
2212
Data models, Convolution, Kernel, Gaussian processes,
Mathematical models, Context modeling, Predictive models,
stochastic variational inference
BibRef
Peng, J.X.[Jing-Xuan],
Wei, Z.H.[Zhong-Hua],
Yang, Y.[Yang],
Wang, W.J.[Wen-Juan],
Qiu, S.[Shi],
Wang, S.[Shaofan],
What Size of Aisle Is Necessary? a System Dynamics Model for
Mitigating Bottleneck Congestion in Entrance Halls of Metro Stations,
ITS(23), No. 12, December 2022, pp. 22923-22936.
IEEE DOI
2212
System dynamics, Data models, Layout, Logic gates, Delays, Inspection,
Costs, Bottleneck congestion, system dynamics, security check, aisle length
BibRef
Becattini, F.[Federico],
Ferracani, A.[Andrea],
Becchi, G.[Giuseppe],
del Bimbo, A.[Alberto],
Events in crowded places: A smart service management,
PRL(164), 2022, pp. 153-160.
Elsevier DOI
2212
Videosurveillance, Indoor routing, Crowd analysis
BibRef
Wang, J.C.[Jun-Cheng],
Gao, J.Y.[Jun-Yu],
Yuan, Y.[Yuan],
Wang, Q.[Qi],
Crowd Localization From Gaussian Mixture Scoped Knowledge and Scoped
Teacher,
IP(32), 2023, pp. 1802-1814.
IEEE DOI
2303
Location awareness, Semantics, Transforms, Training, Chaos,
Data models, Task analysis, Congested scenes perception,
intrinsic scale shift
BibRef
Behera, S.[Shreetam],
Dogra, D.P.[Debi Prosad],
Bandyopadhyay, M.K.[Malay Kumar],
Roy, P.P.[Partha Pratim],
Crowd Characterization in Surveillance Videos Using Deep-Graph
Convolutional Neural Network,
Cyber(53), No. 6, June 2023, pp. 3428-3439.
IEEE DOI
2305
Videos, Mathematical models, Force, Analytical models,
Computational modeling, Microscopy,
visual surveillance
BibRef
Li, Y.[Yuke],
Wang, P.[Pin],
Chan, C.Y.[Ching-Yao],
RESTEP Into the Future: Relational Spatio-Temporal Learning for
Multi-Person Action Forecasting,
MultMed(25), 2023, pp. 1954-1963.
IEEE DOI
2306
Forecasting, Proposals, Feature extraction, Trajectory,
Mutual information, Cognition, Task analysis,
weakly-supervised learning
BibRef
Liao, X.C.[Xiao-Cheng],
Chen, W.N.[Wei-Neng],
Guo, X.Q.[Xiao-Qi],
Zhong, J.H.[Jing-Hui],
Hu, X.M.[Xiao-Min],
Crowd Management Through Optimal Layout of Fences: An Ant Colony
Approach Based on Crowd Simulation,
ITS(24), No. 9, September 2023, pp. 9137-9149.
IEEE DOI
2310
BibRef
Yi, W.F.[Wen-Feng],
Wu, W.H.[Wen-Han],
Wang, X.L.[Xiao-Lu],
Zheng, X.P.[Xiao-Ping],
Modeling the Mutual Anticipation in Human Crowds With Attention
Distractions,
ITS(24), No. 9, September 2023, pp. 10108-10117.
IEEE DOI
2310
BibRef
Wang, L.X.[Lan-Xiao],
Li, H.L.[Hong-Liang],
Hu, W.Z.[Wen-Zhe],
Zhang, X.L.[Xiao-Liang],
Qiu, H.Q.[He-Qian],
Meng, F.M.[Fan-Man],
Wu, Q.B.[Qing-Bo],
What Happens in Crowd Scenes: A New Dataset About Crowd Scenes for
Image Captioning,
MultMed(25), 2023, pp. 5400-5412.
IEEE DOI
2311
BibRef
Liang, D.K.[Ding-Kang],
Xu, W.[Wei],
Zhu, Y.Y.[Ying-Ying],
Zhou, Y.[Yu],
Focal Inverse Distance Transform Maps for Crowd Localization,
MultMed(25), 2023, pp. 6040-6052.
IEEE DOI
2311
BibRef
Zhao, H.T.[Han-Tao],
Guo, T.[Tan],
Tong, W.P.[Wei-Ping],
Yin, H.D.[Hao-Dong],
Liu, Z.Y.[Zhi-Yuan],
PaCS: A Parallel Computation Framework for Field-Based Crowd
Simulation,
ITS(24), No. 11, November 2023, pp. 12659-12670.
IEEE DOI
2311
BibRef
Khosravi, M.R.[Mohammad R.],
Rezaee, K.[Khosro],
Moghimi, M.K.[Mohammad Kazem],
Wan, S.H.[Shao-Hua],
Menon, V.G.[Varun G.],
Crowd Emotion Prediction for Human-Vehicle Interaction Through
Modified Transfer Learning and Fuzzy Logic Ranking,
ITS(24), No. 12, December 2023, pp. 15752-15761.
IEEE DOI
2312
BibRef
Wang, L.X.[Lan-Xiao],
Li, H.L.[Hong-Liang],
Zhang, M.J.[Min-Jian],
Qiu, H.Q.[He-Qian],
Meng, F.M.[Fan-Man],
Wu, Q.B.[Qing-Bo],
Xu, L.F.[Lin-Feng],
CrowdCaption++: Collective-Guided Crowd Scenes Captioning,
MultMed(26), 2024, pp. 4974-4986.
IEEE DOI
2404
Feature extraction, Visualization, Charge coupled devices,
Decoding, Task analysis, Image analysis, Behavioral sciences,
double-query attention
BibRef
Zhou, Y.X.[Yu-Xin],
Liu, C.G.[Chen-Guang],
Ding, Y.L.[Yu-Long],
Yuan, D.[Diping],
Yin, J.[Jiyao],
Yang, S.H.[Shuang-Hua],
Crowd Descriptors and Interpretable Gathering Understanding,
MultMed(26), 2024, pp. 8651-8664.
IEEE DOI
2408
Pedestrians, Computational modeling, Task analysis,
Feature extraction, Deep learning, Analytical models,
interpretable framework
BibRef
Liang, X.W.[Xuan-Wen],
Lee, E.W.M.[Eric Wai Ming],
Visual-Information-Driven Model for Crowd Simulation Using Temporal
Convolutional Network,
ITS(25), No. 9, September 2024, pp. 12297-12314.
IEEE DOI
2409
Pedestrians, Adaptation models, Geometry, Visualization,
Predictive models, Neural networks, Feature extraction,
data-driven
BibRef
Yu, B.[Bin],
Ye, J.H.[Jian-Hong],
Ou, D.X.[Dong-Xiu],
Consideration of Human Vision in Crowd Simulations,
ITS(25), No. 10, October 2024, pp. 13364-13374.
IEEE DOI
2410
Routing, Pedestrians, Legged locomotion, Microscopy,
Mathematical models, Computational modeling, Trajectory, routing
BibRef
Honzák, K.[Klára],
Schmidt, S.[Sebastian],
Resch, B.[Bernd],
Ruthensteiner, P.[Philipp],
Contextual Enrichment of Crowds from Mobile Phone Data through
Multimodal Geo-Social Media Analysis,
IJGI(13), No. 10, 2024, pp. 350.
DOI Link
2411
BibRef
Qiu, H.Q.[He-Qian],
Wang, L.X.[Lan-Xiao],
Zhao, T.J.[Tai-Jin],
Meng, F.M.[Fan-Man],
Li, H.L.[Hong-Liang],
HumanFormer: Human-centric Prompting Multi-modal Perception
Transformer for Referring Crowd Detection,
Crowded24(5530-5540)
IEEE DOI
2410
Visualization, Accuracy, Semantics, Natural languages,
Green products, Interference, Transformers
BibRef
Ranasinghe, Y.[Yasiru],
Patel, V.M.[Vishal M.],
Crowd Detection via Point Localization with Diffusion Models,
FG24(1-10)
IEEE DOI
2408
Location awareness, Measurement, Annotations, Face recognition,
Noise reduction, Stochastic processes, Gesture recognition
BibRef
Wu, S.[Shaokai],
Yang, F.Y.[Feng-Yu],
Boosting Detection in Crowd Analysis via Underutilized Output
Features,
CVPR23(15609-15618)
IEEE DOI
2309
BibRef
Tran, T.M.[Tan M.],
Tran, N.H.[Nguyen H.],
Duong, S.T.M.[Soan T. M.],
Ta, H.D.[Huy D.],
Nguyen, C.D.T.[Chanh D.T.],
Bui, T.H.[Trung H.],
Truong, S.Q.H.[Steven Q.H.],
ReSORT: an ID-recovery multi-face tracking method for surveillance
cameras,
FG21(01-08)
IEEE DOI
2303
Measurement, Annotations, Face recognition, Surveillance,
Neural networks, Cameras, Robustness
BibRef
Zheng, A.[Anlin],
Zhang, Y.[Yuang],
Zhang, X.Y.[Xiang-Yu],
Qi, X.J.[Xiao-Juan],
Sun, J.[Jian],
Progressive End-to-End Object Detection in Crowded Scenes,
CVPR22(847-856)
IEEE DOI
2210
Code, Object Detection.
WWW Link. Representation learning, Performance evaluation, Deep learning,
Machine vision, Detectors, Prediction methods, Object detection,
Vision applications and systems
BibRef
Wu, S.K.[Shao-Kai],
Liu, Z.G.[Zhao-Geng],
Pei, W.C.[Wen-Cheng],
Hong, J.B.[Jian-Bo],
Li, Z.S.[Zhan-Shan],
Faster, Lighter, Robuster: A Weakly-Supervised Crowd Analysis
Enhancement Network and A Generic Feature Extraction Framework,
L3D-IVU22(4049-4058)
IEEE DOI
2210
Training, Location awareness, Object detection,
Feature extraction
BibRef
Kothari, P.[Parth],
Sifringer, B.[Brian],
Alahi, A.[Alexandre],
Interpretable Social Anchors for Human Trajectory Forecasting in
Crowds,
CVPR21(15551-15561)
IEEE DOI
2111
Measurement, Neural networks,
Knowledge based systems, Predictive models, Data models, Trajectory
BibRef
Sundararaman, R.[Ramana],
de Almeida Braga, C.[Cédric],
Marchand, E.[Eric],
Pettré, J.[Julien],
Tracking Pedestrian Heads in Dense Crowd,
CVPR21(3864-3874)
IEEE DOI
2111
Visualization, Head, Tracking, Scalability, Video sequences, Detectors, Real-time systems
BibRef
Nelson, M.G.[Michael G.],
Mazumdar, A.[Angshuman],
Jamal, S.[Saad],
Chen, Y.J.[Ying-Jie],
Mousas, C.[Christos],
Walking in a Crowd Full of Virtual Characters: Effects of Virtual
Character Appearance on Human Movement Behavior,
ISVC20(I:617-629).
Springer DOI
2103
BibRef
Zhu, J.,
Yuan, Z.,
Zhang, C.,
Chi, W.,
Ling, Y.,
Zhang, S.,
Crowded Human Detection via an Anchor-pair Network,
WACV20(1380-1388)
IEEE DOI
2006
Feature extraction, Detectors, Head, Correlation, Fuses, Training
BibRef
Sam, D.B.[Deepak Babu],
Peri, S.V.[Skand Vishwanath],
Mukuntha, N.S.,
Babu, R.V.[R. Venkatesh],
Going Beyond the Regression Paradigm with Accurate Dot Prediction for
Dense Crowds,
WACV20(2853-2861)
IEEE DOI
2006
Feature extraction, Training, Task analysis, Head, Predictive models,
Image resolution, Kernel
BibRef
Liu, N.[Ning],
Long, Y.C.[Yong-Chao],
Zou, C.Q.[Chang-Qing],
Niu, Q.[Qun],
Pan, L.[Li],
Wu, H.F.[He-Feng],
ADCrowdNet: An Attention-Injective Deformable Convolutional Network for
Crowd Understanding,
CVPR19(3220-3229).
IEEE DOI
2002
BibRef
Ma, X.,
Du, S.,
Liu, Y.,
A Lightweight Neural Network For Crowd Analysis Of Images With
Congested Scenes,
ICIP19(979-983)
IEEE DOI
1910
CNN, crowd analysis
BibRef
Cheng, Y.,
Yang, H.,
Chen, L.,
An Online Crowd Semantic Segmentation Method Based on Reinforcement
Learning,
ICIP19(2429-2433)
IEEE DOI
1910
Crowd segmentation, reinforcement learning, threshold decision,
velocity-constrained natural nearest neighbor, semantic
BibRef
Lin, J.[Jing],
Li, N.[Nan],
Towards a Framework to Model Intelligent Avatars in Immersive Virtual
Environments for Studying Human Behavior in Building Fire Emergencies,
VAMR19(I:349-360).
Springer DOI
1909
BibRef
Sam, D.B.,
Sajjan, N.N.,
Babu, R.V.,
Srinivasan, M.,
Divide and Grow: Capturing Huge Diversity in Crowd Images with
Incrementally Growing CNN,
CVPR18(3618-3626)
IEEE DOI
1812
Training, Feature extraction, Adaptation models, Head,
Neural networks, Task analysis, Regression tree analysis
BibRef
Yang, M.,
Rashidi, L.,
Rajasegarar, S.,
Leckie, C.,
Rao, A.S.,
Palaniswami, M.,
Crowd Activity Change Point Detection in Videos via Graph Stream
Mining,
Crowd18(328-3288)
IEEE DOI
1812
Videos, Trajectory, Clustering algorithms, Monitoring,
Object detection, Task analysis, Video sequences
BibRef
Mandal, B.,
Fajtl, J.,
Argyriou, V.,
Monekosso, D.,
Remagnino, P.,
Deep Residual Network with Subclass Discriminant Analysis for Crowd
Behavior Recognition,
ICIP18(938-942)
IEEE DOI
1809
Feature extraction, Eigenvalues and eigenfunctions, Vegetation,
Task analysis, Data models, Training,
residual network
BibRef
Zheng, J.[Juan],
Zhang, X.G.[Xu-Guang],
Detection of Salient Regions in Crowded Scenes Based on Weighted
Networks Approach,
PSIVTWS17(54-62).
Springer DOI
1806
BibRef
Boltes, M.,
Schumann, J.,
Salden, D.,
Gathering of data under laboratory conditions for the deep analysis
of pedestrian dynamics in crowds,
AVSS17(1-6)
IEEE DOI
1806
object tracking, pedestrians, dense crowds, free framework PeTrack,
inertial sensors, invisible people tracking, pedestrian dynamics,
Trajectory
BibRef
Moustafa, A.N.,
Hussein, M.E.,
Gomaa, W.,
Gate and Common Pathway Detection in Crowd Scenes Using Motion Units
and Meta-Tracking,
DICTA17(1-8)
IEEE DOI
1804
crowdsourcing, image motion analysis, motion estimation,
object detection, object tracking, pattern clustering,
Trajectory
BibRef
Wei, M.[Meng],
Kang, Y.[Yu],
Song, W.G.[Wei-Guo],
Cao, Y.[Yang],
Crowd Distribution Estimation with Multi-scale Recursive Convolutional
Neural Network,
MMMod18(I:142-153).
Springer DOI
1802
BibRef
Sindagi, V.A.,
Patel, V.M.,
Generating High-Quality Crowd Density Maps Using Contextual Pyramid
CNNs,
ICCV17(1879-1888)
IEEE DOI
1802
cellular neural nets, feature extraction, image classification,
image fusion, image recognition, image resolution,
Image resolution
BibRef
Dupont, C.,
Tobías, L.,
Luvison, B.,
Crowd-11: A Dataset for Fine Grained Crowd Behaviour Analysis,
DeepLearn-T17(2184-2191)
IEEE DOI
1709
Cameras, Dynamics, Estimation,
Monitoring, Motion, pictures
BibRef
Gowda, S.N.,
Human Activity Recognition Using Combinatorial Deep Belief Networks,
Crowd17(1589-1594)
IEEE DOI
1709
Activity recognition, Encoding,
Feature extraction, Histograms, Machine learning, Video, sequences
BibRef
Nakamura, K.,
Ono, T.,
Babaguchi, N.,
Detection of groups in crowd considering their activity state,
ICPR16(277-282)
IEEE DOI
1705
Force, Legged locomotion, Machine learning algorithms,
Support vector machines, Testing, Training, Trajectory,
activity state of groups, group detection, structural, SVM, (SSVM)
BibRef
Gong, S.[Sixue],
Han, H.[Hu],
Shan, S.G.[Shi-Guang],
Chen, X.L.[Xi-Lin],
Actions Recognition in Crowd Based on Coarse-to-Fine Multi-object
Tracking,
BEST16(III: 478-490).
Springer DOI
1704
BibRef
Shao, J.,
Loy, C.C.,
Kang, K.,
Wang, X.,
Slicing Convolutional Neural Network for Crowd Video Understanding,
CVPR16(5620-5628)
IEEE DOI
1612
BibRef
Trojanová, J.[Jana],
Krehnác, K.[Karel],
Brémond, F.[François],
Data-Driven Motion Pattern Segmentation in a Crowded Environments,
Crowd16(II: 760-774).
Springer DOI
1611
BibRef
Wang, H.[He],
O'Sullivan, C.[Carol],
Globally Continuous and Non-Markovian Crowd Activity Analysis from
Videos,
ECCV16(V: 527-544).
Springer DOI
1611
BibRef
Li, J.J.[Ji-Jia],
Yang, H.,
Wu, S.,
Crowd semantic segmentation based on spatial-temporal dynamics,
AVSS16(102-108)
IEEE DOI
1611
Coherence
BibRef
Wang, L.[Lu],
Xu, L.S.[Li-Sheng],
Yang, M.H.[Ming-Hsuan],
Pedestrian detection in crowded scenes via scale and occlusion
analysis,
ICIP16(1210-1214)
IEEE DOI
1610
Algorithm design and analysis
BibRef
Sharma, R.,
Guha, T.,
A trajectory clustering approach to crowd flow segmentation in videos,
ICIP16(1200-1204)
IEEE DOI
1610
Clustering algorithms
BibRef
Ullah, H.[Habib],
Ullah, M.[Mohib],
Conci, N.[Nicola],
de Natale, F.G.B.[Francesco G.B.],
Crowd behavior identification,
ICIP16(1195-1199)
IEEE DOI
1610
Diffusion processes
BibRef
Brunner, S.[Seth],
Ricks, B.[Brian],
Egbert, P.K.[Parris K.],
Realistic Crowds via Motion Capture and Cell Marking,
AMDO16(66-80).
Springer DOI
1608
BibRef
Shao, J.,
Dong, N.,
Zhao, Q.,
An adaptive clustering approach for group detection in the crowd,
WSSIP15(77-80)
IEEE DOI
1603
feature extraction
BibRef
Sabeur, Z.[Zoheir],
Doulamis, N.[Nikolaos],
Middleton, L.[Lee],
Arbab-Zavar, B.[Banafshe],
Correndo, G.[Gianluca],
Amditis, A.[Aggelos],
Multi-modal Computer Vision for the Detection of Multi-scale Crowd
Physical Motions and Behavior in Confined Spaces,
ISVC15(I: 162-173).
Springer DOI
1601
BibRef
Wang, C.J.[Chong-Jing],
Zhao, X.[Xu],
Shou, Z.[Zheng],
Zhou, Y.[Yi],
Liu, Y.C.[Yun-Cai],
A discriminative tracklets representation for crowd analysis,
ICIP15(1805-1809)
IEEE DOI
1512
Deep networks; crowd analysis; tracklets
BibRef
Sethi, R.J.[Ricky J.],
Towards defining groups and crowds in video using the atomic group
actions dataset,
ICIP15(2925-2929)
IEEE DOI
1512
Atomic Group Actions; Group Action Dataset; Group Action Detection
BibRef
Zou, Y.[Yi],
Zhao, X.[Xu],
Liu, Y.C.[Yun-Cai],
Detect coherent motions in crowd scenes based on tracklets
association,
ICIP15(4456-4460)
IEEE DOI
1512
Crowded scenes; coherent motions; point tracker; tracklets association
BibRef
Chaker, R.[Rima],
Junejo, I.N.[Imran N.],
Al Aghbari, Z.[Zaher],
Crowd modeling using social networks,
ICIP15(1280-1284)
IEEE DOI
1512
Crowd Modeling; Social Network Model
BibRef
Kruthiventi, S.S.S.[Srinivas S. S.],
Babu, R.V.[R. Venkatesh],
Crowd flow segmentation in compressed domain using CRF,
ICIP15(3417-3421)
IEEE DOI
1512
Compressed Domain Processing
BibRef
Yogameena, B.,
Priya, K.S.,
Synoptic video based human crowd behavior analysis for forensic video
surveillance,
ICAPR15(1-6)
IEEE DOI
1511
computer vision
BibRef
Chandran, A.K.,
Poh, L.A.[Loh Ai],
Vadakkepat, P.,
Identifying social groups in pedestrian crowd videos,
ICAPR15(1-6)
IEEE DOI
1511
image classification
BibRef
Neves, J.C.,
Proenca, H.,
Dynamic camera scheduling for visual surveillance in crowded scenes
using Markov random fields,
AVSS15(1-6)
IEEE DOI
1511
Markov processes
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Denman, S.,
Fookes, C.,
Ryan, D.,
Sridharan, S.,
Large scale monitoring of crowds and building utilisation:
A new database and distributed approach,
AVSS15(1-6)
IEEE DOI
1511
building management systems
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Mehner, W.[Wolfgang],
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Robust Marker-Based Tracking for Measuring Crowd Dynamics,
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Springer DOI
1507
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Hassan, M.A.[Mohamed Abul],
Malik, A.S.[Aamir Saeed],
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Adaptive Foreground Extraction for Crowd Analytics Surveillance on
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VSegCV14(390-400).
Springer DOI
1504
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Ruz, C.,
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Peralta, B.,
Lillo, I.,
Espinace, P.,
Gonzalez, R.,
Wendt, B.,
Mery, D.,
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Visual Recognition to Access and Analyze People Density and Flow
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WACV15(1-8)
IEEE DOI
1503
Cameras. Crowd flow.
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Han, T.T.[Ting-Ting],
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Sun, X.S.[Xiao-Shuai],
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ICIP14(2388-2392)
IEEE DOI
1502
Abstracts
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Khokher, M.R.,
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Crowd Behavior Recognition Using Dense Trajectories,
DICTA14(1-7)
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1502
feature extraction
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Wang, B.[Bing],
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Pedestrian detection in highly crowded scenes using 'online'
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ICIP14(2418-2422)
IEEE DOI
1502
Computer vision
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Climent-Perez, P.[Pau],
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Multi-view Event Detection in Crowded Scenes Using Tracklet Plots,
ICPR14(4370-4375)
IEEE DOI
1412
Cameras
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Zou, J.L.[Jia-Ling],
Cui, Y.T.[Yan-Ting],
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A cluster specific latent dirichlet allocation model for trajectory
clustering in crowded videos,
ICIP14(2348-2352)
IEEE DOI
1502
Decision support systems
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Zou, J.L.[Jia-Ling],
Ye, Q.X.[Qi-Xiang],
Cui, Y.T.[Yan-Ting],
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A Belief Based Correlated Topic Model for Trajectory Clustering in
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ICPR14(2543-2548)
IEEE DOI
1412
Accuracy
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Lim, M.K.[Mei Kuan],
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Crowd Saliency Detection via Global Similarity Structure,
ICPR14(3957-3962)
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1412
Dynamics
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Chen, J.,
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DOI Link
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1411
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1411
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Cermeno, E.,
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Learning crowd behavior for event recognition,
PETS13(1-5)
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1411
image colour analysis
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Detecting Social Groups in Crowded Surveillance Videos Using Visual
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SocialInter14(467-473)
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1409
Computer aided analysis;Machine vision;Video surveillance
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Eyjolfsdottir, E.[Eyrun],
Branson, S.[Steve],
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Hoopfer, E.D.[Eric D.],
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Detecting Social Actions of Fruit Flies,
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1408
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Li, M.Z.[Ming-Zhong],
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ICIP13(1172-1176)
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Feature extraction
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feature extraction
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Wang, C.J.[Chong-Jing],
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Motion pattern analysis in crowded scenes based on hybrid
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ICIP13(2837-2841)
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automatic clustering
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ICIP14(184-188)
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1502
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Frame-by-frame crowd motion classification from affine motion models,
AVSS13(282-287)
IEEE DOI
1311
Clocks.
Analytical models
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SBA13(517-526).
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ICIP12(2697-2700).
IEEE DOI
1302
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AVSS12(458-463).
IEEE DOI
1211
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WACV12(417-424).
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AVSBS11(515).
IEEE DOI
1111
AVSS 2011 demo session
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MSVALC11(150-157).
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1201
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Reinartz, P.[Peter],
Automatic crowd density and motion analysis in airborne image sequences
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ARTEMIS11(898-905).
IEEE DOI
1201
BibRef
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ARTEMIS11(928-933).
IEEE DOI
1201
BibRef
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Manocha, D.[Dinesh],
Virtual Tawaf: A case study in simulating the behavior of dense,
heterogeneous crowds,
MSVALC11(128-135).
IEEE DOI
1201
BibRef
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Xu, Y.[Yi],
Yang, X.K.[Xiao-Kang],
Yan, Q.[Qing],
Measuring orderliness based on social force model in collective
motions,
VCIP13(1-6)
IEEE DOI
1402
computer vision
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Xu, Y.[Yi],
Yang, X.K.[Xiao-Kang],
Yan, Q.[Qing],
Crowd instability analysis using velocity-field based social force
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VCIP11(1-4).
IEEE DOI
1201
BibRef
Boszormenyi, L.,
Vision of the crowds,
MMSysS11(401).
IEEE DOI
1111
BibRef
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Ng, K.K.,
Delp, E.J.,
Crowd flow estimation using multiple visual features for scenes with
changing crowd densities,
AVSBS11(60-65).
IEEE DOI
1111
BibRef
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Zou, Y.[Yi],
Liu, Y.C.[Yun-Cai],
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ICIG11(434-439).
IEEE DOI
1109
BibRef
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Wang, X.G.[Xiao-Gang],
Tang, X.[Xiaoou],
Random field topic model for semantic region analysis in crowded scenes
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CVPR11(3441-3448).
IEEE DOI
1106
BibRef
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Al-Hamadi, A.[Ayoub],
Michaelis, B.[Bernd],
Using Conditional Random Field for Crowd Behavior Analysis,
VECTaR10(370-379).
Springer DOI
1109
BibRef
And:
Incorporating Social Entropy for Crowd Behavior Detection Using SVM,
ISVC10(I: 153-162).
Springer DOI
1011
BibRef
Dee, H.M.[Hannah M.],
Caplier, A.[Alice],
Crowd behaviour analysis using histograms of motion direction,
ICIP10(1545-1548).
IEEE DOI
1009
BibRef
Chang, M.C.[Ming-Ching],
Krahnstoever, N.[Nils],
Ge, W.[Weina],
Probabilistic group-level motion analysis and scenario recognition,
ICCV11(747-754).
IEEE DOI
1201
BibRef
Chang, M.C.[Ming-Ching],
Krahnstoever, N.,
Lim, S.,
Yu, T.[Ting],
Group Level Activity Recognition in Crowded Environments across
Multiple Cameras,
AVSS10(56-63).
IEEE DOI
1009
BibRef
Srikrishnan, V.[Viswanthan],
Chaudhuri, S.[Subhasis],
Crowd Motion Analysis Using Linear Cyclic Pursuit,
ICPR10(3340-3343).
IEEE DOI
1008
BibRef
Ozturk, O.[Ovgu],
Yamasaki, T.[Toshihiko],
Aizawa, K.[Kiyoharu],
Detecting Dominant Motion Flows in Unstructured/Structured Crowd Scenes,
ICPR10(3533-3536).
IEEE DOI
1008
BibRef
Widhalm, P.[Peter],
Brandle, N.[Norbert],
Learning Major Pedestrian Flows in Crowded Scenes,
ICPR10(4064-4067).
IEEE DOI
1008
BibRef
Guo, P.[Ping],
Miao, Z.J.[Zhen-Jiang],
Cheng, H.D.[Heng-Da],
Masks based human action detection in crowded videos,
ICIP10(693-696).
IEEE DOI
1009
BibRef
Guo, P.[Ping],
Miao, Z.J.[Zhen-Jiang],
Action Detection in Crowded Videos Using Masks,
ICPR10(1767-1770).
IEEE DOI
1008
BibRef
Siva, P.[Parthipan],
Xiang, T.[Tao],
Weakly Supervised Action Detection,
BMVC11(xx-yy).
HTML Version.
1110
BibRef
Earlier:
Action Detection in Crowd,
BMVC10(xx-yy).
HTML Version.
1009
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Lerner, A.[Alon],
Chrysanthou, Y.[Yiorgos],
Shamir, A.[Ariel],
Cohen-Or, D.[Daniel],
Data Driven Evaluation of Crowds,
MIG09(75-83).
Springer DOI
0911
BibRef
Allain, P.[Pierre],
Courty, N.[Nicolas],
Corpetti, T.[Thomas],
Crowd Flow Characterization with Optimal Control Theory,
ACCV09(II: 279-290).
Springer DOI
0909
BibRef
Paris, S.[Sébastien],
Gerdelan, A.[Anton],
O'Sullivan, C.[Carol],
CA-LOD:
Collision Avoidance Level of Detail for Scalable, Controllable Crowds,
MIG09(13-28).
Springer DOI
0911
BibRef
Koperski, M.[Michal],
Bremond, F.[Francois],
Modeling spatial layout of features for real world scenario RGB-D
action recognition,
AVSS16(44-50)
IEEE DOI
1611
Computational modeling
BibRef
Koperski, M.[Michal],
Bilinski, P.[Piotr],
Bremond, F.[Francois],
3D trajectories for action recognition,
ICIP14(4176-4180)
IEEE DOI
1502
Accuracy
BibRef
Ortiz, J.,
Bak, S.[Slawomir],
Koperski, M.[Michal],
Brémond, F.[Francois],
Minimizing hallucination in histogram of Oriented Gradients,
AVSS15(1-6)
IEEE DOI
1511
image processing
BibRef
Bilinski, P.[Piotr],
Koperski, M.[Michal],
Bak, S.[Slawomir],
Bremond, F.[Francois],
Representing visual appearance by video Brownian covariance
descriptor for human action recognition,
AVSS14(87-92)
IEEE DOI
1411
Computational modeling
BibRef
Sethi, R.J.[Ricky J.],
Jo, H.[Hyunjoon],
Roy-Chowdhury, A.K.[Amit K.],
A generalized data-driven Hamiltonian Monte Carlo for hierarchical
activity search,
ICIP13(829-833)
IEEE DOI
1402
Databases
BibRef
Sethi, R.J.[Ricky J.],
Roy-Chowdhury, A.K.[Amit K.],
Individuals, groups, and crowds:
Modelling complex, multi-object behaviour in phase space,
VECTaR11(1502-1509).
IEEE DOI
1201
BibRef
Earlier:
Physics-based activity modelling in phase space,
ICCVGIP10(170-177).
DOI Link
1111
BibRef
And:
The human action image and its application to motion recognition,
ICCVGIP10(1-8).
DOI Link
1111
BibRef
And:
Modeling Multi-Object Activities in Phase Space,
VECTaR10(328-337).
Springer DOI
1109
BibRef
And:
The Human Action Image,
ICPR10(3674-3678).
IEEE DOI
1008
BibRef
And:
A Neurobiologically Motivated Stochastic Method for Analysis of Human
Activities in Video,
ICPR10(281-285).
IEEE DOI
1008
BibRef
Bilinski, P.[Piotr],
Corvee, E.,
Bak, S.,
Bremond, F.[Francois],
Relative dense tracklets for human action recognition,
FG13(1-7)
IEEE DOI
1309
health care
BibRef
Bilinski, P.[Piotr],
Bremond, F.[Francois],
Contextual Statistics of Space-Time Ordered Features for Human Action
Recognition,
AVSS12(228-233).
IEEE DOI
1211
BibRef
And:
Statistics of Pairwise Co-occurring Local Spatio-temporal Features for
Human Action Recognition,
VECTaR12(I: 311-320).
Springer DOI
1210
BibRef
Earlier:
Evaluation of Local Descriptors for Action Recognition in Videos,
CVS11(61-70).
Springer DOI
1109
BibRef
Garate, C.[Carolina],
Bilinsky, P.[Piotr],
Bremond, F.[Francois],
Crowd event recognition using HOG tracker,
PETS-Winter09(1-6).
IEEE DOI
0912
BibRef
Qiao, W.[Wei],
Wang, H.Y.[Hui-Yuan],
Wu, X.J.[Xiao-Juan],
Liu, P.W.[Peng-Wei],
Crowd Target Extraction and Density Analysis Based on FTLE and GLCM,
CISP09(1-5).
IEEE DOI
0910
BibRef
Krahnstoever, N.,
Tu, P.,
Yu, T.,
Patwardhan, K.,
Hamilton, D.,
Yu, B.,
Greco, C.,
Doretto, G.,
Intelligent Video for Protecting Crowded Sports Venues,
AVSBS09(116-121).
IEEE DOI
0909
BibRef
Saxena, S.[Shobhit],
Brémond, F.[François],
Thonnat, M.[Monnique],
Ma, R.H.[Rui-Hua],
Crowd Behavior Recognition for Video Surveillance,
ACIVS08(xx-yy).
Springer DOI
0810
BibRef
Sim, C.H.[Chern-Horng],
Rajmadhan, E.[Ekambaram],
Ranganath, S.[Surendra],
A Two-Step Approach for Detecting Individuals within Dense Crowds,
AMDO08(xx-yy).
Springer DOI
0807
BibRef
Ihaddadene, N.[Nacim],
Djeraba, C.[Chabane],
Real-time crowd motion analysis,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Xu, L.Q.[Li-Qun],
Anjulan, A.[Arasanathan],
Relating 'Pace' to Activity Changes in Mono- and Multi-camera
Surveillance Videos,
AVSBS09(104-109).
IEEE DOI
0909
BibRef
Xu, L.Q.[Li-Qun],
Anjulan, A.[Arasanathan],
Crowd behaviours analysis in dynamic visual scenes of complex
environment,
ICIP08(9-12).
IEEE DOI
0810
BibRef
Sim, C.H.[Chern-Horng],
Rajmadhan, E.[Ekambaram],
Ranganath, S.[Surendra],
Using color bin images for crowd detections,
ICIP08(1468-1471).
IEEE DOI
0810
BibRef
Zhan, B.B.[Bei-Bei],
Remagnino, P.[Paolo],
Monekosso, D.N.[Dorothy N.],
Velastin, S.A.[Sergio A.],
Self-Organizing Maps for the Automatic Interpretation of Crowd Dynamics,
ISVC08(I: 440-449).
Springer DOI
0812
BibRef
Zhan, B.B.[Bei-Bei],
Remagnino, P.[Paolo],
Velastin, S.A.[Sergio A.],
Mining Paths of Complex Crowd Scenes,
ISVC05(126-133).
Springer DOI
0512
BibRef
Sharif, M.H.[M. Haidar],
Uyaver, S.[Sahin],
Djeraba, C.[Chabane],
Crowd Behavior Surveillance Using Bhattacharyya Distance Metric,
CompIMAGE10(311-323).
Springer DOI
1006
BibRef
Hu, M.[Min],
Ali, S.[Saad],
Shah, M.[Mubarak],
Detecting global motion patterns in complex videos,
ICPR08(1-5).
IEEE DOI
0812
BibRef
And:
Learning motion patterns in crowded scenes using motion flow field,
ICPR08(1-5).
IEEE DOI
0812
BibRef
Rodriguez, M.D.[Mikel D.],
Laptev, I.[Ivan],
Sivic, J.[Josef],
Audibert, J.Y.[Jean-Yves],
Density-aware person detection and tracking in crowds,
ICCV11(2423-2430).
IEEE DOI
1201
BibRef
Rodriguez, M.D.[Mikel D.],
Sivic, J.[Josef],
Laptev, I.[Ivan],
Audibert, J.Y.[Jean-Yves],
Data-driven crowd analysis in videos,
ICCV11(1235-1242).
IEEE DOI
1201
Learn from large databse. Offline behavior priors.
BibRef
Rodriguez, M.D.[Mikel D.],
Ali, S.[Saad],
Kanade, T.[Takeo],
Tracking in unstructured crowded scenes,
ICCV09(1389-1396).
IEEE DOI
0909
BibRef
Ali, S.[Saad],
Shah, M.[Mubarak],
Floor Fields for Tracking in High Density Crowd Scenes,
ECCV08(II: 1-14).
Springer DOI
PDF File.
0810
Dataset, Tracking.
WWW Link.
BibRef
Ali, S.[Saad],
Shah, M.[Mubarak],
A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and
Stability Analysis,
CVPR07(1-6).
IEEE DOI
PDF File.
Dataset, Surveillance. The dataset for this paper is available:
WWW Link. UCF Lists:
WWW Link. But no link to data.
0706
BibRef
Ali, S.[Saad],
Crowd Flow Segmentation and Stability Analysis,
Online2007
HTML Version. The more general discussion of the issues of the other papers.
Includes a more complete dataset and pointers to other useful code.
Dataset, Surveillance.
WWW Link.
BibRef
0700
Scovanner, P.[Paul],
Ali, S.[Saad],
Shah, M.[Mubarak],
A 3-Dimensional SIFT Descriptor and its Application
to Action Recognition,
MMC07(xx-yy).
PDF File.
BibRef
0700
Ali, S.[Saad],
Shah, M.[Mubarak],
A Supervised Learning Framework for Generic Object Detection in Images,
ICCV05(II: 1347-1354).
IEEE DOI
0510
BibRef
Earlier:
An Integrated Approach for Generic Object Detection Using
Kernel PCA and Boosting,
ICME05(xx-yy).
PDF File. Combine Kernel PCA and AdaBoost.
BibRef
Li, Y.[Yuan],
Ai, H.Z.[Hai-Zhou],
Fast Detection of Independent Motion in Crowds Guided by Supervised
Learning,
ICIP07(III: 341-344).
IEEE DOI
0709
BibRef
Andrade, E.L.[Ernesto L.],
Blunsden, S.[Scott],
Fisher, R.B.[Robert B.],
Modelling Crowd Scenes for Event Detection,
ICPR06(I: 175-178).
IEEE DOI
0609
BibRef
And:
Hidden Markov Models for Optical Flow Analysis in Crowds,
ICPR06(I: 460-463).
IEEE DOI
0609
BibRef
Marana, A.N.,
Cavenaghi, M.A.,
Ulson, R.S.,
Drumond, F.L.,
Real-Time Crowd Density Estimation Using Images,
ISVC05(355-362).
Springer DOI
0512
BibRef
Beleznai, C.[Csaba],
Bischof, H.[Horst],
Fast human detection in crowded scenes by contour integration and local
shape estimation,
CVPR09(2246-2253).
IEEE DOI
0906
BibRef
Brostow, G.J.[Gabriel J.],
Cipolla, R.[Roberto],
Unsupervised Bayesian Detection of Independent Motion in Crowds,
CVPR06(I: 594-601).
IEEE DOI
0606
BibRef
Beleznai, C.[Csaba],
Fruhstuck, B.[Bernhard],
Bischof, H.[Horst],
Human detection in groups using a fast mean shift procedure,
ICIP04(I: 349-352).
IEEE DOI
0505
BibRef
Reisman, P.,
Mano, O.,
Avidan, S.,
Shashua, A.,
Crowd detection in video sequences,
IVS04(66-71).
IEEE DOI
0411
BibRef
Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Human Activities, Violence, Violent Actions .