17.1.3.9.3 Human Activities, Crowds, Lots of People

Chapter Contents (Back)
Group Activity. Crowds. Crowd Behavior.
See also Crowds, Tracking Multiple People, Multiple Pedestrian Tracking.
See also Detecting Anomalies, Abnormal Behavior In Crowds.
See also Human Activities, Violence, Violent Actions.
See also Counting People, Crowds, Crowd Counting.
See also Evacuation Management.

Lien, K.C.[Kuo-Chin], Huang, C.L.[Chung-Lin],
Multiview-Based Cooperative Tracking of Multiple Human Objects,
JIVP(2008), No. 2008, pp. xx-yy.
DOI Link 0804
BibRef
Earlier:
Multi-view-based Cooperative Tracking of Multiple Human Objects in Cluttered Scenes,
ICPR06(III: 1123-1126).
IEEE DOI 0609
BibRef

Tsai, Y.T.[Yao-Te], Shih, H.C.[Huang-Chia], Huang, C.L.[Chung-Lin],
Multiple Human Objects Tracking in Crowded Scenes,
ICPR06(III: 51-54).
IEEE DOI 0609
BibRef

Li, L., Huang, W., Gu, I.Y.H., Luo, R., Tian, Q.,
An Efficient Sequential Approach to Tracking Multiple Objects Through Crowds for Real-Time Intelligent CCTV Systems,
SMC-B(37), No. 5, September 2007, pp. 1254-1269.
IEEE DOI 0809
BibRef

Ma, Y.Q.[Yun-Qian], Cisar, P.[Petr], Kembhavi, A.[Aniruddha],
Motion segmentation and activity representation in crowds,
IJIST(19), No. 2, June 2009, pp. 80-90.
DOI Link 0905
BibRef

Ma, Y.Q.[Yun-Qian], Cisar, P.[Petr],
Activity Representation in Crowd,
SSPR08(107-116).
Springer DOI 0812
BibRef

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 Star Skeletonization,
JIVP(2009), No. 2009, pp. xx-yy.
DOI Link 1002
BibRef

Haciomeroglu, M., Laycock, R.G., Day, A.M.,
Automatic spatial analysis and pedestrian flow control for real-time crowd simulation in an urban environment,
VC(24), No. 10, October 2008, pp. xx-yy.
Springer DOI 0804
BibRef

Barut, O.[Oner], Haciomeroglu, M.[Murat],
Real-time collision-free linear trajectory generation on GPU for crowd simulations,
VC(31), No. 6-8, June 2015, pp. 843-852.
WWW Link. 1506
Graphics simulations, not recogniton and tracking. BibRef

Ozcan, C.Y.[Cumhur Yigit], Haciomeroglu, M.[Murat],
A path-based multi-agent navigation model,
VC(31), No. 6-8, June 2015, pp. 863-872.
Springer DOI 1506
BibRef

Ke, Y.[Yan], Sukthankar, R.[Rahul], Hebert, M.[Martial],
Volumetric Features for Video Event Detection,
IJCV(88), No. 3, July 2010, pp. xx-yy.
Springer DOI 1003
BibRef
Earlier: A1, Only: CMU-CS-08-113, March 2008. BibRef Ph.D.Thesis, March 2008.
HTML Version. BibRef

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 Dynamical Systems,
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 BibRef

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 BibRef

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
BibRef

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 1710
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 Crowd,
Surveys(51), No. 1, 2018, pp. Article No 21.
DOI Link 1804
Survey, Crowds. BibRef

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 BibRef

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 BibRef

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 Consistencies,
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 BibRef

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], Güdükbay, U.[Ugur], Dibeklioglu, H.[Hamdi],
Augmentation of virtual agents in real crowd videos,
SIViP(13), No. 4, June 2019, pp. 643-650.
Springer DOI 1906
BibRef

Tripathi, G.[Gaurav], Singh, K.[Kuldeep], Vishwakarma, D.K.[Dinesh Kumar],
Convolutional neural networks for crowd behaviour analysis: a survey,
VC(35), No. 5, May 2019, pp. 753-776.
WWW Link. 1906
BibRef

Favaretto, R.M.[Rodolfo Migon], Knob, P.[Paulo], Musse, S.R.[Soraia Raupp], 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 distribution-based person segmentation,
MVA(30), No. 6, September 2019, pp. 1083-1096.
WWW Link. 1909
BibRef

Ebrahimpour, Z.[Zeinab], Wan, W.G.[Wang-Gen], 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
BibRef

Mao, Y.[Yan], Li, Z.[Zuning], 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
BibRef

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
BibRef

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: Objective Effectiveness Versus Expert Opinion,
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 in metropolis,
IET-ITS(14), No. 5, May 2020, pp. 313-322.
DOI Link 2005
BibRef

Lamba, S.[Sonu], Nain, N.[Neeta],
Segmentation of crowd flow by trajectory clustering in active contours,
VC(36), No. 5, May 2020, pp. 989-1000.
WWW Link. 2005
BibRef

Al Ghamdi, M.[Manal], Gotoh, Y.[Yoshihiko],
Graph-based topic models for trajectory clustering in crowd videos,
MVA(31), No. 5, July 2020, pp. Article39.
WWW Link. 2006
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 Attribute Recognition,
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 Entropy and Aw-Rascle Model,
ITS(21), No. 10, October 2020, pp. 4425-4434.
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,
ECCV20(XXIX: 711-728).
Springer DOI 2010
BibRef

Bisagno, N.[Niccoló], Saltori, C.[Cristiano], Zhang, B.[Bo], de Natale, F.G.B.[Francesco G.B.], Conci, N.[Nicola],
Embedding group and obstacle information in LSTM networks for human trajectory prediction in crowded scenes,
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
Special section intro. 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


Bisagno, N.[Niccolò], Stefani, A.L.[Antonio Luigi], Garau, N.[Nicola], de Natale, F.[Francesco], Conci, N.[Nicola],
Unicrowd Simulator: Visual and Behavioral Fidelity For The Generation of Crowd Datasets,
ICIP24(193-199)
IEEE DOI 2411
Training, Visualization, Rendering (computer graphics), Behavioral sciences, Trajectory, Task analysis, Simulation, Pedestrian 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 BibRef

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 BibRef

Mehner, W.[Wolfgang], Boltes, M.[Maik], Mathias, M.[Markus], Leibe, B.[Bastian],
Robust Marker-Based Tracking for Measuring Crowd Dynamics,
CVS15(445-455).
Springer DOI 1507
BibRef

Hassan, M.A.[Mohamed Abul], Malik, A.S.[Aamir Saeed], Nicolas, W.[Walter], Faye, I.[Ibrahima],
Adaptive Foreground Extraction for Crowd Analytics Surveillance on Unconstrained Environments,
VSegCV14(390-400).
Springer DOI 1504
BibRef

Ruz, C., Pieringer, C., Peralta, B., Lillo, I., Espinace, P., Gonzalez, R., Wendt, B., Mery, D., Soto, A.,
Visual Recognition to Access and Analyze People Density and Flow Patterns in Indoor Environments,
WACV15(1-8)
IEEE DOI 1503
Cameras. Crowd flow. BibRef

Han, T.T.[Ting-Ting], Yao, H.X.[Hong-Xun], Sun, X.S.[Xiao-Shuai], Zhang, Y.H.[Yan-Hao],
Clustering by saliency: Unsupervised discovery of crowd activities,
ICIP14(2388-2392)
IEEE DOI 1502
Abstracts BibRef

Khokher, M.R., Bouzerdoum, A., Phung, S.L.[Son Lam],
Crowd Behavior Recognition Using Dense Trajectories,
DICTA14(1-7)
IEEE DOI 1502
feature extraction BibRef

Wang, B.[Bing], Chan, K.L.[Kap Luk], Wang, G.[Gang], Zhang, H.J.[Hai-Jian],
Pedestrian detection in highly crowded scenes using 'online' dictionary learning for occlusion handling,
ICIP14(2418-2422)
IEEE DOI 1502
Computer vision BibRef

Climent-Perez, P.[Pau], Monekosso, D.N.[Dorothy N.], Remagnino, P.[Paolo],
Multi-view Event Detection in Crowded Scenes Using Tracklet Plots,
ICPR14(4370-4375)
IEEE DOI 1412
Cameras BibRef

Zou, J.L.[Jia-Ling], Cui, Y.T.[Yan-Ting], Wan, F.[Fang], Ye, Q.X.[Qi-Xiang], Jiao, J.B.[Jian-Bin],
A cluster specific latent dirichlet allocation model for trajectory clustering in crowded videos,
ICIP14(2348-2352)
IEEE DOI 1502
Decision support systems BibRef

Zou, J.L.[Jia-Ling], Ye, Q.X.[Qi-Xiang], Cui, Y.T.[Yan-Ting], Doermann, D.S.[David S.], Jiao, J.B.[Jian-Bin],
A Belief Based Correlated Topic Model for Trajectory Clustering in Crowded Video Scenes,
ICPR14(2543-2548)
IEEE DOI 1412
Accuracy BibRef

Lim, M.K.[Mei Kuan], Kok, V.J.[Ven Jyn], Loy, C.C.[Chen Change], Chan, C.S.[Chee Seng],
Crowd Saliency Detection via Global Similarity Structure,
ICPR14(3957-3962)
IEEE DOI 1412
Dynamics BibRef

Chen, J., Hu, T., Zhang, P., Shi, W., Shan, J.,
Trajectory Clustering for People's Movement Pattern Based on Crowd Souring Data,
Geospatial14(55-62).
DOI Link 1411
BibRef

Ullah, H.[Habib], Ullah, M.[Mohib], Conci, N.[Nicola],
Dominant Motion Analysis in Regular and Irregular Crowd Scenes,
HBU14(62-72).
Springer DOI 1411
BibRef

Zhang, Y.H.[Yan-Hao], Zhang, S.P.[Sheng-Ping], Huang, Q.M.[Qing-Ming], Serre, T.[Thomas],
Learning Sparse Prototypes for Crowd Perception via Ensemble Coding Mechanisms,
HBU14(86-100).
Springer DOI 1411
BibRef

Cermeno, E., Mallor, S., Siguenza, J.A.,
Learning crowd behavior for event recognition,
PETS13(1-5)
IEEE DOI 1411
image colour analysis BibRef

Leach, M.[Michael], Baxter, R.H.[Rolf H.], Robertson, N.M.[Neil M.], Sparks, E.[Ed],
Detecting Social Groups in Crowded Surveillance Videos Using Visual Attention,
SocialInter14(467-473)
IEEE DOI 1409
Computer aided analysis;Machine vision;Video surveillance BibRef

Eyjolfsdottir, E.[Eyrun], Branson, S.[Steve], Burgos-Artizzu, X.P.[Xavier P.], Hoopfer, E.D.[Eric D.], Schor, J.[Jonathan], Anderson, D.J.[David J.], Perona, P.[Pietro],
Detecting Social Actions of Fruit Flies,
ECCV14(II: 772-787).
Springer DOI 1408
BibRef

Li, M.Z.[Ming-Zhong], Yin, Z.Z.[Zhao-Zheng], Thimgan, M.S.[Matthew S.], Qin, R.[Ruwen],
Track fast-moving tiny flies by adaptive LBP feature and cascaded data association,
ICIP13(1172-1176)
IEEE DOI 1402
Feature extraction BibRef

Perko, R.[Roland], Schnabel, T.[Thomas], Fritz, G.[Gerald], Almer, A.[Alexander], Paletta, L.[Lucas],
Airborne Based High Performance Crowd Monitoring for Security Applications,
SCIA13(664-674).
Springer DOI 1311
BibRef

Karpagavalli, P., Ramprasad, A.V.,
Human detection and segmentation in the crowd environment by coimbining APD with HLBD approaches,
NCVPRIPG13(1-4)
IEEE DOI 1408
feature extraction BibRef

Wang, C.J.[Chong-Jing], Zhao, X.[Xu], Wu, Z.[Zhe], Liu, Y.C.[Yun-Cai],
Motion pattern analysis in crowded scenes based on hybrid generative-discriminative feature maps,
ICIP13(2837-2841)
IEEE DOI 1402
automatic clustering BibRef

Basset, A.[Antoine], Bouthemy, P.[Patrick], Kervrann, C.[Charles],
Recovery of motion patterns and dominant paths in videos of crowded scenes,
ICIP14(184-188)
IEEE DOI 1502
BibRef
And:
Frame-by-frame crowd motion classification from affine motion models,
AVSS13(282-287)
IEEE DOI 1311
Clocks. Analytical models BibRef

Conigliaro, D.[Davide], Setti, F.[Francesco], Bassetti, C.[Chiara], Ferrario, R.[Roberta], Cristani, M.[Marco],
Viewing the Viewers: A Novel Challenge for Automated Crowd Analysis,
SBA13(517-526).
Springer DOI 1309
BibRef

Yiicel, Z.[Zeynep], Miyashita, T.[Takahiro], Hagita, N.[Norihiro],
Modeling and identification of group motion via compound evaluation of positional and directional cues,
ICPR12(1172-1176).
WWW Link. 1302
BibRef

Kaltsa, V.[Vagia], Briassouli, A.[Alexia], Kompatsiaris, I.[Ioannis], Strintzis, M.G.[Michael G.],
Timely, robust crowd event characterization,
ICIP12(2697-2700).
IEEE DOI 1302
BibRef

Su, H.[Hang], Yang, H.[Hua], Zheng, S.[Shibao], Fan, Y.W.[Ya-Wen], Wei, S.[Sha],
Crowd Event Perception Based on Spatio-temporal Viscous Fluid Field,
AVSS12(458-463).
IEEE DOI 1211
BibRef

Lasdas, V.[Vasilis], Timofte, R.[Radu], Van Gool, L.J.[Luc J.],
Non-parametric motion-priors for flow understanding,
WACV12(417-424).
IEEE DOI 1203
Dominant dynamic properties of crowded scenes from single camera. Tracklets of fixed length from optic flow. BibRef

Clauss, S.[Stephane], Pelisson, F.[Fabien],
People flow analysis,
AVSBS11(515).
IEEE DOI 1111
AVSS 2011 demo session BibRef

Butenuth, M.[Matthias], Burkert, F.[Florian], Schmidt, F.[Florian], Hinz, S.[Stefan], Hartmann, D.[Dirk], Kneidl, A.[Angelika], Borrmann, A.[Andre], Sirmacek, B.[Beril],
Integrating pedestrian simulation, tracking and event detection for crowd analysis,
MSVALC11(150-157).
IEEE DOI 1201
BibRef

Sirmacek, B.[Beril], Reinartz, P.[Peter],
Automatic crowd density and motion analysis in airborne image sequences based on a probabilistic framework,
ARTEMIS11(898-905).
IEEE DOI 1201
BibRef

Briassouli, A.[Alexia], Kompatsiaris, I.[Ioannis],
Spatiotemporally localized new event detection in crowds,
ARTEMIS11(928-933).
IEEE DOI 1201
BibRef

Curtis, S.[Sean], Guy, S.J.[Stephen J.], Zafar, B.[Basim], Manocha, D.[Dinesh],
Virtual Tawaf: A case study in simulating the behavior of dense, heterogeneous crowds,
MSVALC11(128-135).
IEEE DOI 1201
BibRef

Bai, Y.[Yu], 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 BibRef

Zhao, J.[Jing], Xu, Y.[Yi], Yang, X.K.[Xiao-Kang], Yan, Q.[Qing],
Crowd instability analysis using velocity-field based social force model,
VCIP11(1-4).
IEEE DOI 1201
BibRef

Boszormenyi, L.,
Vision of the crowds,
MMSysS11(401).
IEEE DOI 1111
BibRef

Srivastava, S., 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

Wang, C.J.[Chong-Jing], Zhao, X.[Xu], Zou, Y.[Yi], Liu, Y.C.[Yun-Cai],
Detecting Motion Patterns in Dynamic Crowd Scenes,
ICIG11(434-439).
IEEE DOI 1109
BibRef

Zhou, B.[Bolei], Wang, X.G.[Xiao-Gang], Tang, X.[Xiaoou],
Random field topic model for semantic region analysis in crowded scenes from tracklets,
CVPR11(3441-3448).
IEEE DOI 1106
BibRef

Pathan, S.S.[Saira Saleem], 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
BibRef

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 .


Last update:Nov 26, 2024 at 16:40:19