16.7.4.4.1 Crowds, Tracking Multiple People, Multiple Pedestrian Tracking

Chapter Contents (Back)
Motion, Human. Tracking. Crowds. See also Detecting Anomalies, Abnormal Behavior In Crowds. See also Tracking Several People, Occlusions. For understanding crowd motions: See also Human Activities, Crowds, Lots of People. See also Counting People, Crowds, Crowd Counting.

Ess, A.[Andreas], Leibe, B.[Bastian], Schindler, K.[Konrad], Van Gool, L.J.[Luc J.],
Robust Multiperson Tracking from a Mobile Platform,
PAMI(31), No. 10, October 2009, pp. 1831-1846.
IEEE DOI 0909
BibRef
Earlier:
A mobile vision system for robust multi-person tracking,
CVPR08(1-8).
IEEE DOI 0806
Multi pedestrian tracking with stereo. For each frame solve a simplified version of the problem (calibration, depth, objects), then with these constraints, for multiple frames, use interactions, tracking, etc. BibRef

Mitzel, D.[Dennis], Leibe, B.[Bastian],
Close-Range Human Detection and Tracking for Head-Mounted Cameras,
BMVC12(8).
DOI Link 1301
BibRef

Mitzel, D.[Dennis], Leibe, B.[Bastian],
Taking Mobile Multi-object Tracking to the Next Level: People, Unknown Objects, and Carried Items,
ECCV12(V: 566-579).
Springer DOI 1210
BibRef
Earlier:
Real-time multi-person tracking with detector assisted structure propagation,
RobPerc11(974-981).
IEEE DOI 1201
BibRef

Mitzel, D.[Dennis], Sudowe, P.[Patrick], Leibe, B.[Bastian],
Real-Time Multi-Person Tracking with Time-Constrained Detection,
BMVC11(xx-yy).
HTML Version. 1110
BibRef

Mitzel, D.[Dennis], Horbert, E.[Esther], Ess, A.[Andreas], Leibe, B.[Bastian],
Multi-Person Tracking with Sparse Detection and Continuous Segmentation,
ECCV10(I: 397-410).
Springer DOI 1009
BibRef

Horbert, E.[Esther], Rematas, K.[Konstantinos], Leibe, B.[Bastian],
Level-set person segmentation and tracking with multi-region appearance models and top-down shape information,
ICCV11(1871-1878).
IEEE DOI 1201
BibRef

Pellegrini, S.[Stefano], Van Gool, L.J.[Luc J.],
Tracking with a mixed continuous-discrete Conditional Random Field,
CVIU(117), No. 10, 2013, pp. 1215-1228.
Elsevier DOI 1309
Tracking BibRef

Pellegrini, S.[Stefano], Ess, A.[Andreas], Van Gool, L.J.[Luc J.],
Improving Data Association by Joint Modeling of Pedestrian Trajectories and Groupings,
ECCV10(I: 452-465).
Springer DOI 1009
BibRef

Pellegrini, S., Ess, A.[Andreas], Schindler, K.[Konrad], Van Gool, L.J.,
You'll never walk alone: Modeling social behavior for multi-target tracking,
ICCV09(261-268).
IEEE DOI 0909
BibRef

Ess, A.[Andreas], Leibe, B.[Bastian], Van Gool, L.J.[Luc J.],
Depth and Appearance for Mobile Scene Analysis,
ICCV07(1-8).
IEEE DOI 0710
BibRef

Breitenstein, M.D.[Michael D.], Reichlin, F.[Fabian], Leibe, B.[Bastian], Koller-Meier, E.[Esther], Van Gool, L.J.[Luc J.],
Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera,
PAMI(33), No. 9, September 2011, pp. 1820-1833.
IEEE DOI 1109
Possibly moving camera. Particle filter approach. Generic and object specific knowledge. BibRef

Schindler, K.[Konrad], Ess, A.[Andreas], Leibe, B.[Bastian], Van Gool, L.J.[Luc J.],
Automatic detection and tracking of pedestrians from a moving stereo rig,
PandRS(65), No. 6, November 2010, pp. 523-537.
Elsevier DOI 1101
Award, Best Paper, ISPRS. Detection; Tracking; Vision; Urban Scene BibRef

Gammeter, S.[Stephan], Ess, A.[Andreas], Jäggli, T.[Tobias], Schindler, K.[Konrad], Leibe, B.[Bastian], Van Gool, L.J.[Luc J.],
Articulated Multi-body Tracking under Egomotion,
ECCV08(II: 816-830).
Springer DOI 0810
BibRef

Pellegrini, S.[Stefano], Ess, A.[Andreas], Tanaskovic, M.[Marko], Van Gool, L.J.[Luc J.],
Wrong turn - No dead end: A stochastic pedestrian motion model,
SISM10(15-22).
IEEE DOI 1006
BibRef

Germa, T., Lerasle, F., Ouadah, N., Cadenat, V.,
Vision and RFID data fusion for tracking people in crowds by a mobile robot,
CVIU(114), No. 6, June 2010, pp. 641-651.
Elsevier DOI 1006
Radio frequency ID; Multimodal data fusion; Particle filtering; Person tracking; Person following; Multi-sensor fusion; Human visual servoing BibRef

Bai, Y.[Yang], Qi, H.R.[Hai-Rong],
Feature-Based Image Comparison for Semantic Neighbor Selection in Resource-Constrained Visual Sensor Networks,
JIVP(2010), No. 2010, pp. xx-yy.
DOI Link 1011
to merge information from multiple cameras Combine Harris detector and moment invariants. BibRef

Qian, C.[Cheng], Qi, H.R.[Hai-Rong],
A distributed solution to detect targets in crowds using visual sensor networks,
ICDSC08(1-10).
IEEE DOI 0809
BibRef

Duan, G.Q.[Gen-Quan], Ai, H.Z.[Hai-Zhou], Xing, J.L.[Jun-Liang], Cao, S.[Song], Lao, S.H.[Shi-Hong],
Scene Aware Detection and Block Assignment Tracking in crowded scenes,
IVC(30), No. 4-5, May 2012, pp. 292-305.
Elsevier DOI 1206
Visual surveillance; Object detection; Object tracking; Particle filter BibRef

Zhou, B.Y.[Bing-Yin], Zhang, F.[Fan], Peng, L.Z.[Li-Zhong],
Higher-order SVD analysis for crowd density estimation,
CVIU(116), No. 9, September 2012, pp. 1014-1021.
Elsevier DOI 1208
Crowd density estimation; Tensor; HOSVD; SVM BibRef

Wu, S., Wong, H.S.,
Crowd Motion Partitioning in a Scattered Motion Field,
SMC-B(42), No. 5, October 2012, pp. 1443-1454.
IEEE DOI 1209
Local motion approximation. Optical flow at some points, Not really individual tracking, more crowd flow. BibRef

Thalmann, D.[Daniel], Musse, S.R.[Soraia Raupp],
Crowd Simulation,
Springer2013. ISBN 978-1-4471-4449-6


WWW Link. 1211
Graphics, motion capture. BibRef

Ali, I.[Irshad], Dailey, M.N.[Matthew N.],
Multiple Human Tracking in High-Density Crowds,
IVC(30), No. 12, December 2012, pp. 966-977.
Elsevier DOI 1212
BibRef
Earlier: ACIVS09(540-549).
Springer DOI 0909
Head detection; Pedestrian tracking; Crowd tracking; Particle filters; 3D object tracking; 3D head plane estimation; Human detection; Least-squares plane estimation; AdaBoost detection cascade BibRef

Shao, J.[Jie], Dong, N.[Nan], Tong, M.[Minglei],
Multi-part sparse representation in random crowded scenes tracking,
PRL(34), No. 7, 1 May 2013, pp. 780-788.
Elsevier DOI 1303
Visual tracking; Multi-part sparse representation; Crowded scenes; Particle filter BibRef

Lv, W., Song, W., Ma, J., Fang, Z.,
A Two-Dimensional Optimal Velocity Model for Unidirectional Pedestrian Flow Based on Pedestrian's Visual Hindrance Field,
ITS(14), No. 4, 2013, pp. 1753-1763.
IEEE DOI 1312
Data models BibRef

Curtis, S.[Sean], Zafar, B.[Basim], Gutub, A.[Adnan], Manocha, D.[Dinesh],
Right of way,
VC(29), No. 12, December 2013, pp. 1277-1292.
Springer DOI 1312
Pedestrian motion analysis. BibRef

Brscic, D., Kanda, T.,
Changes in Usage of an Indoor Public Space: Analysis of One Year of Person Tracking,
HMS(45), No. 2, April 2015, pp. 228-237.
IEEE DOI 1503
Cameras BibRef

Ali, S., Nishino, K., Manocha, D., Shah, M., (Eds.)
Modeling, Simulation and Visual Analysis of Crowds: A Multidisciplinary Perspective,

Springer2013. ISBN 978-1-4614-8482-0.
WWW Link. Discusses common challenges and points to problem areas related to modeling, simulation, and visual analysis of crowds. Facilitates the process of cross-disciplinary interaction among researchers from areas of computer vision, computer graphics and evacuation dynamics by providing a common platform. Provides a comprehensive map of the current state of the art in these distinct but related fields. BibRef 1300

Idrees, H.[Haroon], Warner, N.[Nolan], Shah, M.[Mubarak],
Tracking in dense crowds using prominence and neighborhood motion concurrence,
IVC(32), No. 1, 2014, pp. 14-26.
Elsevier DOI 1402
Crowd analysis BibRef

Idrees, H.[Haroon], Soomro, K., Shah, M.[Mubarak],
Detecting Humans in Dense Crowds Using Locally-Consistent Scale Prior and Global Occlusion Reasoning,
PAMI(37), No. 10, October 2015, pp. 1986-1998.
IEEE DOI 1509
Cognition BibRef

Zhou, B.[Bolei], Tang, X.[Xiaoou], Zhang, H., Wang, X.G.[Xiao-Gang],
Measuring Crowd Collectiveness,
PAMI(36), No. 8, August 2014, pp. 1586-1599.
IEEE DOI 1407
BibRef
Earlier: A1, A2, A4, Only:
Measuring Crowd Collectiveness,
CVPR13(3049-3056)
IEEE DOI 1309
BibRef
Earlier: A1, A2, A4, Only:
Coherent Filtering: Detecting Coherent Motions from Crowd Clutters,
ECCV12(II: 857-871).
Springer DOI 1210
Collective Motion; Crowd Behavior; Video Analysis Computational modeling. BibRef

Zhou, B.[Bolei], Tang, X.[Xiaoou], Wang, X.G.[Xiao-Gang],
Learning Collective Crowd Behaviors with Dynamic Pedestrian-Agents,
IJCV(111), No. 1, January 2015, pp. 50-68.
WWW Link. 1502
BibRef
Earlier: A1, A3, A2:
Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents,
CVPR12(2871-2878).
IEEE DOI 1208
BibRef

Fradi, H.[Hajer], Eiselein, V.[Volker], Dugelay, J.L.[Jean-Luc], Keller, I.[Ivo], Sikora, T.[Thomas],
Spatio-temporal crowd density model in a human detection and tracking framework,
SP:IC(31), No. 1, 2015, pp. 100-111.
Elsevier DOI 1502
BibRef
Earlier: A2, A1, A4, A5, A3:
Enhancing human detection using crowd density measures and an adaptive correction filter,
AVSS13(19-24)
IEEE DOI 1311
Crowd density. adaptive filters BibRef

Senst, T.[Tobias], Eiselein, V.[Volker], Keller, I.[Ivo], Sikora, T.[Thomas],
Crowd analysis in non-static cameras using feature tracking and multi-person density,
ICIP14(6041-6045)
IEEE DOI 1502
Cameras BibRef

Jin, Z.X.[Zhi-Xing], Bhanu, B.[Bir],
Analysis-by-synthesis: Pedestrian tracking with crowd simulation models in a multi-camera video network,
CVIU(134), No. 1, 2015, pp. 48-63.
Elsevier DOI 1504
BibRef
Earlier:
Optimizing crowd simulation based on real video data,
ICIP13(3186-3190)
IEEE DOI 1402
BibRef
Earlier:
Single camera multi-person tracking based on crowd simulation,
ICPR12(3660-3663).
WWW Link. 1302
Crowd simulation. Pedestrian tracking BibRef

Rojas, F.[Francisco], Tarnogol, F.[Fernando], Yang, H.S.[Hyun S.],
Dynamic social formations of pedestrian groups navigating and using public transportation in a virtual city,
VC(32), No. 3, March 2016, pp. 335-345.
WWW Link. 1604
BibRef

Ortego, D.[Diego], San Miguel, J.C.[Juan C.], Martínez, J.M.[José M.],
Rejection based multipath reconstruction for background estimation in video sequences with stationary objects,
CVIU(147), No. 1, 2016, pp. 23-37.
Elsevier DOI 1605
BibRef
Earlier: A1, A2, Only:
Multi-feature stationary foreground detection for crowded video-surveillance,
ICIP14(2403-2407)
IEEE DOI 1502
Background estimation. Adaptation models See also semantic-guided and self-configurable framework for video analysis, A. BibRef

Feng, P., Wang, W., Naqvi, S.M., Chambers, J.,
Adaptive Retrodiction Particle PHD Filter for Multiple Human Tracking,
SPLetters(23), No. 11, November 2016, pp. 1592-1596.
IEEE DOI 1609
Gaussian processes BibRef

Feng, P., Wang, W., Dlay, S., Naqvi, S.M., Chambers, J.,
Social Force Model-Based MCMC-OCSVM Particle PHD Filter for Multiple Human Tracking,
MultMed(19), No. 4, April 2017, pp. 725-739.
IEEE DOI 1704
Atmospheric measurements BibRef

Yang, M.[Min], Jia, Y.D.[Yun-De],
Temporal dynamic appearance modeling for online multi-person tracking,
CVIU(153), No. 1, 2016, pp. 16-28.
Elsevier DOI 1612
Online multi-person tracking BibRef

Mazimpaka, J.D.[Jean Damascène], Timpf, S.[Sabine],
How They Move Reveals What Is Happening: Understanding the Dynamics of Big Events from Human Mobility Pattern,
IJGI(6), No. 1, 2017, pp. xx-yy.
DOI Link 1702
BibRef

Yoo, H., Kim, K.[Kikyung], Byeon, M.[Moonsub], Jeon, Y., Choi, J.Y.[Jin Young],
Online Scheme for Multiple Camera Multiple Target Tracking Based on Multiple Hypothesis Tracking,
CirSysVideo(27), No. 3, March 2017, pp. 454-469.
IEEE DOI 1703
Cameras BibRef

Kim, S.W.[Soo Wan], Byeon, M.[Moonsub], Kim, K.[Kikyung], Choi, J.Y.[Jin Young],
MAP-Based Online Data Association for Multiple People Tracking in Crowded Scenes,
ICPR14(1212-1217)
IEEE DOI 1412
Computational modeling BibRef

Ju, J.[Jaeyong], Kim, D.[Daehun], Ku, B.[Bonhwa], Han, D.K.[David K.], Ko, H.S.[Han-Seok],
Online multi-object tracking with efficient track drift and fragmentation handling,
JOSA-A(34), No. 2, February 2017, pp. 280-293.
DOI Link 1702
Digital image processing BibRef

Ju, J.[Jaeyong], Kim, D.[Daehun], Ku, B.[Bonhwa], Han, D.K.[David K.], Ko, H.S.[Han-Seok],
Online multi-person tracking with two-stage data association and online appearance model learning,
IET-CV(11), No. 1, February 2017, pp. 87-95.
DOI Link 1703
BibRef

Jiang, Y.F.[Yi-Fan], Shin, H.[Hyunhak], Ju, J.[Jaeyong], Ko, H.S.[Han-Seok],
Online pedestrian tracking with multi-stage re-identification,
AVSS17(1-6)
IEEE DOI 1806
image sequences, object detection, object tracking, pedestrians, traffic engineering computing, FOV, lost reappeared targets, Trajectory BibRef

Kok, V.J., Chan, C.S.,
GrCS: Granular Computing-Based Crowd Segmentation,
Cyber(47), No. 5, May 2017, pp. 1157-1168.
IEEE DOI 1704
Context BibRef

Chen, X.J.[Xiao-Jing], Bhanu, B.[Bir],
Integrating Social Grouping for Multitarget Tracking Across Cameras in a CRF Model,
CirSysVideo(27), No. 11, November 2017, pp. 2382-2394.
IEEE DOI 1712
BibRef
Earlier:
Soft Biometrics Integrated Multi-target Tracking,
ICPR14(4146-4151)
IEEE DOI 1412
Adaptation models, Cameras, Image color analysis, Lighting, Surveillance, Target tracking, social grouping behavior. Biological system modeling BibRef

Chen, X.J.[Xiao-Jing], Qin, Z.[Zhen], An, L.[Le], Bhanu, B.[Bir],
Multiperson Tracking by Online Learned Grouping Model With Nonlinear Motion Context,
CirSysVideo(26), No. 12, December 2016, pp. 2226-2239.
IEEE DOI 1612
BibRef
Earlier:
An Online Learned Elementary Grouping Model for Multi-target Tracking,
CVPR14(1242-1249)
IEEE DOI 1409
Context BibRef

Dehghan, A.[Afshin], Shah, M.[Mubarak],
Binary Quadratic Programing for Online Tracking of Hundreds of People in Extremely Crowded Scenes,
PAMI(40), No. 3, March 2018, pp. 568-581.
IEEE DOI 1802
Computational complexity, Detectors, Linear programming, Object tracking, Optimization, Target tracking, quadratic programing BibRef

Zhu, F.[Feng], Wang, X.G.[Xiao-Gang], Yu, N.H.[Neng-Hai],
Crowd Tracking by Group Structure Evolution,
CirSysVideo(28), No. 3, March 2018, pp. 772-786.
IEEE DOI 1804
BibRef
Earlier:
Crowd Tracking with Dynamic Evolution of Group Structures,
ECCV14(VI: 139-154).
Springer DOI 1408
image sequences, motion estimation, object tracking, trees (mathematics), accurate local motions, model-free tracking BibRef


Bisagno, N., Conci, N., Zhang, B.,
Data-Driven crowd simulation,
AVSS17(1-6)
IEEE DOI 1806
behavioural sciences, pattern clustering, pedestrians, software agents, virtual reality, collision-avoidance, Videos BibRef

Vandoni, J., Aldea, E., Le Hégarat-Mascle, S.,
An evidential framework for pedestrian detection in high-density crowds,
AVSS17(1-6)
IEEE DOI 1806
feature extraction, image fusion, image texture, learning (artificial intelligence), object detection, clutter, Shape BibRef

Ghosh, S., Amon, P., Hutter, A., Kaup, A.,
Detecting closely spaced and occluded pedestrians using specialized deep models for counting,
VCIP17(1-4)
IEEE DOI 1804
convolution, feature extraction, feedforward neural nets, object detection, pedestrians, base counting model, Pedestrian Detection BibRef

Xu, K.P.[Kai-Ping], Qin, Z.[Zheng], Wang, G.L.[Guo-Long], Huang, K.[Kai], Ye, S.X.[Shu-Xiong], Zhang, H.D.[Hui-Di],
Collision-Free LSTM for Human Trajectory Prediction,
MMMod18(I:106-116).
Springer DOI 1802
BibRef

Insafutdinov, E., Andriluka, M., Pishchulin, L., Tang, S., Levinkov, E., Andres, B., Schiele, B.,
ArtTrack: Articulated Multi-Person Tracking in the Wild,
CVPR17(1293-1301)
IEEE DOI 1711
Detectors, Image edge detection, Pose estimation, Proposals, Tracking, Videos BibRef

Huang, S.S., Chen, C.Y.,
Crowd pedestrian detection using expectation maximization with weighted local features,
MVA17(177-180)
DOI Link 1708
Cameras, Clustering algorithms, Feature extraction, Head, Legged locomotion, Torso, Training BibRef

Takada, H., Hotta, K., Janney, P.,
Human tracking in crowded scenes using target information at previous frames,
ICPR16(1809-1814)
IEEE DOI 1705
Adaptation models, Color, Computational modeling, Image color analysis, Mathematical model, Target tracking, crowded scenes, distractors, human tracking, information at previous frames, occlusion BibRef

Yun, S., Choi, J., Yoo, Y., Yun, K., Choi, J.Y.,
Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning,
CVPR17(1349-1358)
IEEE DOI 1711
Correlation, Learning (artificial intelligence), Neural networks, Target tracking, Training, Visualization BibRef

Yoo, Y., Yun, K., Yun, S., Hong, J., Jeong, H., Choi, J.Y.,
Visual Path Prediction in Complex Scenes with Crowded Moving Objects,
CVPR16(2668-2677)
IEEE DOI 1612
BibRef

Stewart, R., Andriluka, M., Ng, A.Y.,
End-to-End People Detection in Crowded Scenes,
CVPR16(2325-2333)
IEEE DOI 1612
BibRef

Yu, H., Zhou, Y., Simmons, J., Przybyla, C.P., Lin, Y., Fan, X., Mi, Y., Wang, S.,
Groupwise Tracking of Crowded Similar-Appearance Targets from Low-Continuity Image Sequences,
CVPR16(952-960)
IEEE DOI 1612
BibRef

Alahi, A.[Alexandre], Goel, K., Ramanathan, V.[Vignesh], Robicquet, A., Fei-Fei, L.[Li], Savarese, S.,
Social LSTM: Human Trajectory Prediction in Crowded Spaces,
CVPR16(961-971)
IEEE DOI 1612
BibRef

Alahi, A.[Alexandre], Ramanathan, V.[Vignesh], Fei-Fei, L.[Li],
Socially-Aware Large-Scale Crowd Forecasting,
CVPR14(2211-2218)
IEEE DOI 1409
crowd; detection; forecasting; od matrix; pedestrian; tracking BibRef

Assari, S.M.[Shayan Modiri], Idrees, H.[Haroon], Shah, M.[Mubarak],
Human Re-identification in Crowd Videos Using Personal, Social and Environmental Constraints,
ECCV16(II: 119-136).
Springer DOI 1611
BibRef

Babaee, M.[Mohammadreza], You, Y.[Yue], Rigoll, G.[Gerhard],
Pixel Level Tracking of Multiple Targets in Crowded Environments,
Crowd16(II: 692-708).
Springer DOI 1611
BibRef

Yun, S.[Sangdoo], Yun, K.[Kimin], Choi, J.W.[Jong-Won], Choi, J.Y.[Jin Young],
Density-Aware Pedestrian Proposal Networks for Robust People Detection in Crowded Scenes,
Crowd16(II: 643-654).
Springer DOI 1611
BibRef

Kieritz, H.[Hilke], Becker, S.[Stefan], Hübner, W.[Wolfgang], Arens, M.[Michael],
Online multi-person tracking using Integral Channel Features,
AVSS16(122-130)
IEEE DOI 1611
Benchmark testing BibRef

Yoon, S.[Sejong], Kapadia, M.[Mubbasir], Sahu, P.[Pritish], Pavlovic, V.[Vladimir],
Filling in the blanks: reconstructing microscopic crowd motion from multiple disparate noisy sensors,
CVAST16(1-9)
IEEE DOI 1606
image denoising. Individuals in a crowd. BibRef

Liu, J., Fan, Q., Pankanti, S., Metaxas, D.N.,
People detection in crowded scenes by context-driven label propagation,
WACV16(1-9)
IEEE DOI 1606
Context BibRef

Takada, H., Hotta, K.,
Robust Human Tracking to Occlusion in Crowded Scenes,
DICTA15(1-8)
IEEE DOI 1603
learning (artificial intelligence) BibRef

Yi, S., Li, H., Wang, X.,
Pedestrian Travel Time Estimation in Crowded Scenes,
ICCV15(3137-3145)
IEEE DOI 1602
Computer vision BibRef

Bastani, V.[Vahid], Marcenaro, L.[Lucio], Regazzoni, C.S.[Carlo S.],
A particle filter based sequential trajectory classifier for behavior analysis in video surveillance,
ICIP15(3690-3694)
IEEE DOI 1512
Behavior Analysis; On-line Trajectory Classification; Video Surveillance BibRef

Bastani, V., Campo, D., Marcenaro, L., Regazzoni, C.S.,
Online pedestrian group walking event detection using spectral analysis of motion similarity graph,
AVSS15(1-5)
IEEE DOI 1511
gait analysis BibRef

Setia, A.[Achint], Mittal, A.[Anurag],
Co-operative Pedestrians Group Tracking in Crowded Scenes Using an MST Approach,
WACV15(102-108)
IEEE DOI 1503
Clustering algorithms BibRef

Biswas, S.[Sovan], Praveen, R.G.[R. Gnana], Babu, R.V.[R. Venkatesh],
Super-pixel based crowd flow segmentation in H.264 compressed videos,
ICIP14(2319-2323)
IEEE DOI 1502
Computer vision BibRef

Fradi, H.[Hajer], Dugelay, J.L.[Jean-Luc],
Sparse Feature Tracking for Crowd Change Detection and Event Recognition,
ICPR14(4116-4121)
IEEE DOI 1412
Feature extraction BibRef

Creusot, C.[Clement],
Local Segmentation for Pedestrian Tracking in Dense Crowds,
MMMod14(I: 266-277).
Springer DOI 1405
See also Ground Truth for Pedestrian Analysis and Application to Camera Calibration. BibRef

Pishchulin, L.[Leonid], Jain, A.[Arjun], Wojek, C.[Christian], Andriluka, M.[Mykhaylo], Thormahlen, T.[Thorsten], Schiele, B.[Bernt],
Learning people detection models from few training samples,
CVPR11(1473-1480).
IEEE DOI 1106
BibRef

Luo, W.[Wenhan], Kim, T.K.[Tae-Kyun],
Generic Object Crowd Tracking by Multi-Task Learning,
BMVC13(xx-yy).
DOI Link 1402
BibRef

Ullah, H.[Habib], Conci, N.[Nicola],
Structured learning for crowd motion segmentation,
ICIP13(824-828)
IEEE DOI 1402
Feature extraction BibRef

Chen, K.[Ke], Gong, S.G.[Shao-Gang], Xiang, T.[Tao], Loy, C.C.[Chen Change],
Cumulative Attribute Space for Age and Crowd Density Estimation,
CVPR13(2467-2474)
IEEE DOI 1309
Age estimation; Crowd density estimation; Cumulative attributes BibRef

Idrees, H.[Haroon], Saleemi, I.[Imran], Seibert, C.[Cody], Shah, M.[Mubarak],
Multi-source Multi-scale Counting in Extremely Dense Crowd Images,
CVPR13(2547-2554)
IEEE DOI 1309
Counting; Dense Crowds; Markov Random Field; Multi-scale Analysis BibRef

Li, C.[Chi], Lu, L.[Le], Hager, G.D.[Gregory D.], Tang, J.Y.[Jian-Yu], Wang, H.Z.[Han-Zi],
Robust Object Tracking in Crowd Dynamic Scenes Using Explicit Stereo Depth,
ACCV12(III:71-85).
Springer DOI 1304
BibRef

Iwasaki, M.[Masahiro], Komoto, A.[Ayako], Nobori, K.[Kunio],
Dense motion segmentation of articulated objects in crowds,
ICPR12(861-865).
WWW Link. 1302
BibRef

Pellegrini, S.[Stefano], Gall, J.[Jürgen], Sigal, L.[Leonid], Van Gool, L.J.[Luc J.],
Destination Flow for Crowd Simulation,
ARTEMIS12(III: 162-171).
Springer DOI 1210
BibRef

Kratz, L.[Louis], Nishino, K.[Ko],
Going with the Flow: Pedestrian Efficiency in Crowded Scenes,
ECCV12(IV: 558-572).
Springer DOI 1210
BibRef

Yan, J.J.[Jun-Jie], Lei, Z.[Zhen], Yi, D.[Dong], Li, S.Z.[Stan Z.],
Multi-pedestrian detection in crowded scenes: A global view,
CVPR12(3124-3129).
IEEE DOI 1208
BibRef

Yu, J.[Jie], Farin, D.[Dirk], Schiele, B.[Bernt],
Multi-target Tracking in Crowded Scenes,
DAGM11(406-415).
Springer DOI 1109
BibRef

Ali, I., Dailey, M.N.,
Head plane estimation improves the accuracy of pedestrian tracking in dense crowds,
ICARCV10(2054-2059).
IEEE DOI 1109
BibRef

Martin, R.[Rhys], Arandjelovic, O.[Ognjen],
Multiple-object Tracking in Cluttered and Crowded Public Spaces,
ISVC10(III: 89-98).
Springer DOI 1011
BibRef

Ma, W.C., Huang, D.A., Lee, N., Kitani, K.M.[Kris M.],
Forecasting Interactive Dynamics of Pedestrians with Fictitious Play,
CVPR17(4636-4644)
IEEE DOI 1711
Computational modeling, Dynamics, Forecasting, Game theory, Predictive models, Trajectory, Visualization BibRef

Sugimura, D.[Daisuke], Kitani, K.M.[Kris M.], Okabe, T.[Takahiro], Sato, Y.[Yoichi], Sugimoto, A.[Akihiro],
Using individuality to track individuals: Clustering individual trajectories in crowds using local appearance and frequency trait,
ICCV09(1467-1474).
IEEE DOI 0909
BibRef

Wu, H.S.[Hai Shan], Zhao, Q.[Qi], Zou, D.P.[Dan-Ping], Chen, Y.Q.[Yan Qiu],
Acquiring 3D motion trajectories of large numbers of swarming animals,
ObjectEvent09(593-600).
IEEE DOI 0910
BibRef

Hinz, S.,
Density and Motion Estimation of People in Crowded Environments based on Aerial Image Sequences,
HighRes09(xx-yy).
PDF File. 0906
BibRef

Zhang, Z.[Zui], Gunes, H.[Hatice], Piccardi, M.[Massimo],
Tracking People in Crowds by a Part Matching Approach,
AVSBS08(88-95).
IEEE DOI 0809
See also Commentary Paper 2 on Tracking People in Crowds by a Part Matching Approach. See also Commentary Paper for: Tracking People in Crowds by a Part Matching Approach. BibRef

Moeslund, T.B.[Thomas B.],
Commentary Paper for: 'Tracking People in Crowds by a Part Matching Approach',
AVSBS08(96-97).
IEEE DOI 0809
See also Tracking People in Crowds by a Part Matching Approach. BibRef

Lipton, A.J.[Alan J.],
Commentary Paper 2 on 'Tracking People in Crowds by a Part Matching Approach',
AVSBS08(98-98).
IEEE DOI 0809
See also Tracking People in Crowds by a Part Matching Approach. BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Tracking Several People, Occlusions .


Last update:Jul 19, 2018 at 13:26:08