17.1.3.8.3 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
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Data collection, Legged locomotion, Tracking, Trajectory, Pedestrian behavior, pedestrian count, video analysis BibRef

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ITS(19), No. 10, October 2018, pp. 3092-3112.
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Sensors, Social network services, Estimation, Event detection, Radio frequency, Data mining, Video surveillance, Urban sensing, benchmark datasets BibRef

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IEEE DOI 1812
pedestrians, road vehicles, stochastic processes, traffic congestion scenarios, faster lanes, crowd behavior, stochastic approximation BibRef

Li, Y.,
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IEEE DOI 1812
behavioural sciences computing, feature extraction, image classification, image motion analysis, deep neural networks BibRef

Zou, Y., Zhao, X., Liu, Y.,
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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],
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IEEE DOI 1908
Mathematical model, Force, Torque, Shape, Computational modeling, Microscopy, Torso, Pedestrian behavior, rotation torque, competitive, gyroscope BibRef

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WACV18(1029-1037)
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graph theory, image motion analysis, inference mechanisms, pattern clustering, video signal processing, video surveillance, Visualization BibRef

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Zhao, R., Hu, Q., Liu, Q., Li, C., Dong, D., Ma, Y.,
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IEEE DOI 2010
Entropy, Information entropy, Microscopy, Force, Analytical models, Psychology, Numerical models, High-density crowd, AW-Rascle model BibRef

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Hazards, Solid modeling, Psychology, Computational modeling, Stress, Collision avoidance, Navigation, Crowd simulation, multihazard BibRef

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Game theory, Games, Solid modeling, Force, Psychology, Market research, Biological system modeling, Group violence, emotional contagion, evolutionary game theory. BibRef

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Wang, S.Z.[Sen-Zhang], Miao, H.[Hao], Li, J.[Jiyue], Cao, J.N.[Jian-Nong],
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IEEE DOI 2205
Urban areas, Predictive models, Transfer learning, Data models, Deep learning, Task analysis, Adaptation models, crowd flow prediction BibRef

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Group-level emotion recognition, Facial features, Feature extraction, Feature fusion, Crowd videos BibRef

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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],
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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

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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,
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IEEE DOI 2209
Computational modeling, Vehicle dynamics, Surveillance, Dynamics, Data models, Predictive models, Load modeling, Crowd surveillance, prior elicitation BibRef

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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

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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,
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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,
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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,
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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,
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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,
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IEEE DOI 2310
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Yi, W.F.[Wen-Feng], Wu, W.H.[Wen-Han], Wang, X.L.[Xiao-Lu], Zheng, X.P.[Xiao-Ping],
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IEEE DOI 2310
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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],
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IEEE DOI 2311
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Liang, D.K.[Ding-Kang], Xu, W.[Wei], Zhu, Y.Y.[Ying-Ying], Zhou, Y.[Yu],
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IEEE DOI 2311
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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,
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IEEE DOI 2311
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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,
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IEEE DOI 2312
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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,
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IEEE DOI 2404
Feature extraction, Visualization, Charge coupled devices, Decoding, Task analysis, Image analysis, Behavioral sciences, double-query attention BibRef

Wu, S.[Shaokai], Yang, F.Y.[Feng-Yu],
Boosting Detection in Crowd Analysis via Underutilized Output Features,
CVPR23(15609-15618)
IEEE DOI 2309
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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, Pattern recognition 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
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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
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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