16.7.4.4.2 Crowds, Tracking Multiple People, Multiple Pedestrian Tracking

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

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, 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

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,

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.A.[Syed Moeen Ali], 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

Fu, Z., Angelini, F., Chambers, J., Naqvi, S.M.A.[Syed Moeen Ali],
Multi-Level Cooperative Fusion of GM-PHD Filters for Online Multiple Human Tracking,
MultMed(21), No. 9, September 2019, pp. 2277-2291.
IEEE DOI 1909
Target tracking, Detectors, Correlation, Radio frequency, Fuses, Task analysis, Multiple human tracking, GM-PHD filter, data fusion BibRef

Feng, P., Wang, W., Dlay, S., Naqvi, S.M.A.[Syed Moeen Ali], 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

Tian, Y.C.[Yi-Cong], Dehghan, A.[Afshin], Shah, M.[Mubarak],
On Detection, Data Association and Segmentation for Multi-Target Tracking,
PAMI(41), No. 9, Sep. 2019, pp. 2146-2160.
IEEE DOI 1908
Target tracking, Detectors, Task analysis, Correlation, Inference algorithms, Optimization, Object segmentation, dual decomposition 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

Zhang, C.Y.[Cai-You], Huang, Y.T.[Yu-Teng], Wang, Z.Q.[Zhi-Qiang], Jiang, H.C.[Hong-Cheng], Yan, D.F.[Dong-Feng],
Retraction: Cross-camera multi-person tracking by leveraging fast graph mining algorithm,
JVCIR(67), 2020, pp. 102755.
Elsevier DOI 2004
BibRef
Earlier: JVCIR(55), 2018, pp. 711-719.
Elsevier DOI 1809
Multiple person, Tracking, Video surveillance, Matching BibRef

Carvalho, J., Marques, M., Costeira, J.P.,
Understanding People Flow in Transportation Hubs,
ITS(19), No. 10, October 2018, pp. 3282-3291.
IEEE DOI 1810
Cameras, Airports, Sensors, Monitoring, Security, Image color analysis, People flow monitoring, depth cameras BibRef

Zhou, Q., Zhong, B., Zhang, Y., Li, J., Fu, Y.,
Deep Alignment Network Based Multi-Person Tracking With Occlusion and Motion Reasoning,
MultMed(21), No. 5, May 2019, pp. 1183-1194.
IEEE DOI 1905
image motion analysis, object detection, object tracking, pedestrians, spatial motion, motion reasoning, motion reasoning BibRef

Wu, H.F.[He-Feng], Hu, Y.[Yafei], Wang, K.Z.[Ke-Ze], Li, H.[Hanhui], Nie, L.[Lin], Cheng, H.[Hui],
Instance-aware representation learning and association for online multi-person tracking,
PR(94), 2019, pp. 25-34.
Elsevier DOI 1906
Representation learning, Online tracking, Multi-person tracking, Data association BibRef

Li, J., Wei, L., Zhang, F., Yang, T., Lu, Z.,
Joint Deep and Depth for Object-Level Segmentation and Stereo Tracking in Crowds,
MultMed(21), No. 10, October 2019, pp. 2531-2544.
IEEE DOI 1910
image motion analysis, image segmentation, object detection, object tracking, stereo image processing, severe occlusion BibRef

Ranjan, A.[Anurag], Hoffmann, D.T.[David T.], Tzionas, D.[Dimitrios], Tang, S.[Siyu], Romero, J.[Javier], Black, M.J.[Michael J.],
Learning Multi-human Optical Flow,
IJCV(128), No. 4, April 2020, pp. 873-890.
Springer DOI 2004
BibRef

Fuchsberger, A.[Alexander], Ricks, B.[Brian], Chen, Z.C.[Zhi-Cheng],
A Semi-Automated Technique for Transcribing Accurate Crowd Motions,
IJIG(20), No. 2, April 2020, pp. 2050012.
DOI Link 2005
BibRef

Arbab-Zavar, B.[Banafshe], Sabeur, Z.A.[Zoheir A.],
Multi-scale crowd feature detection using vision sensing and statistical mechanics principles,
MVA(31), No. 4, April 2020, pp. Article26.
Springer DOI 2005
BibRef

Nishimura, H.[Hitoshi], Makibuchi, N.[Naoya], Tasaka, K.[Kazuyuki], Kawanishi, Y.[Yasutomo], Murase, H.[Hiroshi],
Multiple Human Tracking Using an Omnidirectional Camera with Local Rectification and World Coordinates Representation,
IEICE(E103-D), No. 6, June 2020, pp. 1265-1275.
WWW Link. 2006
BibRef

Ma, Q.L.[Qiu-Lin], Zou, Q.[Qi], Wang, N.[Nan], Guan, Q.J.[Qing-Ji], Pei, Y.T.[Yan-Ting],
Looking ahead: Joint small group detection and tracking in crowd scenes,
JVCIR(72), 2020, pp. 102876.
Elsevier DOI 2010
Group tracking, Delay decision, Joint optimization, Multiple hypothesis tracking BibRef

Ohashi, T.[Takuya], Ikegami, Y.[Yosuke], Nakamura, Y.[Yoshihiko],
Synergetic reconstruction from 2D pose and 3D motion for wide-space multi-person video motion capture in the wild,
IVC(104), 2020, pp. 104028.
Elsevier DOI 2012
Markerless motion capture, Pose estimation, Multi-person, Kinematics BibRef

Pegoraro, J., Meneghello, F., Rossi, M.,
Multiperson Continuous Tracking and Identification From mm-Wave Micro-Doppler Signatures,
GeoRS(59), No. 4, April 2021, pp. 2994-3009.
IEEE DOI 2104
Radar tracking, Target tracking, Spaceborne radar, Doppler radar, Radar antennas, Convolutional neural networks, multiperson identification BibRef

Sun, Z.H.[Zhi-Hong], Chen, J.[Jun], Chao, L.[Liang], Ruan, W.J.[Wei-Jian], Mukherjee, M.[Mithun],
A Survey of Multiple Pedestrian Tracking Based on Tracking-by-Detection Framework,
CirSysVideo(31), No. 5, 2021, pp. 1819-1833.
IEEE DOI 2105
Survey, Pedestrian Tracking. BibRef

Huang, Z.L.[Zi-Ling], Wang, Z.[Zheng], Tsai, C.C.[Chung-Chi], Satoh, S.[Shin'ichi], Lin, C.W.[Chia-Wen],
DotSCN: Group Re-Identification via Domain-Transferred Single and Couple Representation Learning,
CirSysVideo(31), No. 7, July 2021, pp. 2739-2750.
IEEE DOI 2107
Layout, Feature extraction, Task analysis, Training, Training data, Cameras, Deep learning, Group re-identification, domain transfer, deep learning BibRef

Xie, Y.F.[Ye-Fan], Zheng, J.B.[Jiang-Bin], Hou, X.[Xuan], Xi, Y.[Yue], Tian, F.M.[Feng-Ming],
Dynamic Dual-Peak Network: A real-time human detection network in crowded scenes,
JVCIR(79), 2021, pp. 103195.
Elsevier DOI 2109
Anchor free, Crowded scenes, CNN, Human detection BibRef

Zhang, J.L.[Jia-Liang], Lin, L.X.[Li-Xiang], Zhu, J.[Jianke], Li, Y.[Yang], Chen, Y.C.[Yun-Chen], Hu, Y.[Yao], Hoi, S.C.H.[Steven C. H.],
Attribute-Aware Pedestrian Detection in a Crowd,
MultMed(23), 2021, pp. 3085-3097.
IEEE DOI 2109
Detectors, Semantics, Feature extraction, Proposals, Object detection, Task analysis, Training, Attribute-aware, pedestrian detection BibRef

Zhao, R.Y.[Rong-Yong], Liu, Q.[Qiong], Hu, Q.S.[Qian-Shan], Dong, D.[Daheng], Li, C.L.[Cui-Ling], Ma, Y.L.[Yun-Long],
Lyapunov-Based Crowd Stability Analysis for Asymmetric Pedestrian Merging Layout at T-Shaped Street Junction,
ITS(22), No. 11, November 2021, pp. 6833-6842.
IEEE DOI 2112
Stability criteria, Merging, Layout, Numerical stability, Analytical models, Lyapunov methods, Crowd flow, T-shaped street junction BibRef

Gao, S.[Shan], Ye, Q.X.[Qi-Xiang], Liu, L.[Li], Kuijper, A.[Arjan], Ji, X.Y.[Xiang-Yang],
A Graphical Social Topology Model for RGB-D Multi-Person Tracking,
CirSysVideo(31), No. 11, November 2021, pp. 4305-4320.
IEEE DOI 2112
Topology, Target tracking, Feature extraction, Trajectory, Task analysis, Data models, Computational modeling, group behavior analysis BibRef

Xie, Y.[Yefan], Zheng, J.B.[Jiang-Bin], Hou, X.[Xuan], Naqvi, I.R.[Irfan Raza], Xi, Y.[Yue], Kuang, N.[Nailiang],
Multi-dimensional weighted cross-attention network in crowded scenes,
IET-IPR(15), No. 14, 2021, pp. 3585-3598.
DOI Link 2112
BibRef

Li, W.[Wenbo], Wei, Y.[Yi], Lyu, S.W.[Si-Wei], Chang, M.C.[Ming-Ching],
Simultaneous multi-person tracking and activity recognition based on cohesive cluster search,
CVIU(214), 2022, pp. 103301.
Elsevier DOI 2112
Group activity, Collective activity recognition, Pairwise interaction, Multi-person tracking BibRef

Nodehi, H.[Hamid], Shahbahrami, A.[Asadollah],
Multi-Metric Re-Identification for Online Multi-Person Tracking,
CirSysVideo(32), No. 1, January 2022, pp. 147-159.
IEEE DOI 2201
Feature extraction, Target tracking, Measurement, Image color analysis, Trajectory, Detectors, Task analysis, video surveillance BibRef

Zhang, Z.Y.[Zi-Yue], Jiang, S.[Shuai], Huang, C.[Congzhentao], Xu, R.Y.D.[Richard Yi Da],
Unsupervised Clothing Change Adaptive Person ReID,
SPLetters(29), 2022, pp. 304-308.
IEEE DOI 2202
Clothing, Feature extraction, Pipelines, Training, Cameras, Unsupervised learning, Signal processing algorithms, person ReID BibRef

Han, R.Z.[Rui-Ze], Wang, Y.[Yun], Yan, H.M.[Hao-Min], Feng, W.[Wei], Wang, S.[Song],
Multi-View Multi-Human Association with Deep Assignment Network,
IP(31), 2022, pp. 1830-1840.
IEEE DOI 2202
Cameras, Feature extraction, Optimization, Training, Video surveillance, Testing, Human association, maximum multi-clique problem BibRef

Sun, H.[Hao], Zhao, Z.[Zhiqun], Yin, Z.Z.[Zhao-Zheng], He, Z.H.[Zhi-Hai],
Reciprocal Twin Networks for Pedestrian Motion Learning and Future Path Prediction,
CirSysVideo(32), No. 3, March 2022, pp. 1483-1497.
IEEE DOI 2203
Trajectory, Predictive models, Recurrent neural networks, Visualization, Task analysis, Semantics, generative adversarial networks BibRef

Wang, J.[Jing], Zhao, C.[Cailing], Huo, Z.Q.[Zhan-Qiang], Qiao, Y.X.[Ying-Xu], Sima, H.F.[Hai-Feng],
High quality proposal feature generation for crowded pedestrian detection,
PR(128), 2022, pp. 108605.
Elsevier DOI 2205
Crowded pedestrian, Pedestrian detection, Visible proposal, Feature fusion, Paired prediction BibRef

Zhu, Y.P.[Yi-Peng], Wang, T.[Tao], Zhu, S.Q.[Shi-Qiang],
Adaptive Multi-Pedestrian Tracking by Multi-Sensor: Track-to-Track Fusion Using Monocular 3D Detection and MMW Radar,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Kothari, P.[Parth], Kreiss, S.[Sven], Alahi, A.[Alexandre],
Human Trajectory Forecasting in Crowds: A Deep Learning Perspective,
ITS(23), No. 7, July 2022, pp. 7386-7400.
IEEE DOI 2207
Trajectory, Forecasting, Predictive models, Task analysis, Artificial neural networks, Biological system modeling, Encoding, social interactions BibRef

Gajamannage, K.[Kelum], Park, Y.[Yonggi], Paffenroth, R.[Randy], Jayasumana, A.P.[Anura P.],
Reconstruction of fragmented trajectories of collective motion using Hadamard deep autoencoders,
PR(131), 2022, pp. 108891.
Elsevier DOI 2208
Multi-object tracking, Collective motion, Deep autoencoders, Hadamard product, Self-propelled particles BibRef

Li, Q.M.[Qi-Ming], Su, Y.J.[Yi-Jing], Gao, Y.[Yin], Xie, F.[Feng], Li, J.[Jun],
OAF-Net: An Occlusion-Aware Anchor-Free Network for Pedestrian Detection in a Crowd,
ITS(23), No. 11, November 2022, pp. 21291-21300.
IEEE DOI 2212
Detectors, Training, Feature extraction, Proposals, Avalanche photodiodes, Head, Benchmark testing, crowd scenes BibRef

Nishimura, H.[Hitoshi], Komorita, S.[Satoshi], Kawanishi, Y.[Yasutomo], Murase, H.[Hiroshi],
SDOF-Tracker: Fast and Accurate Multiple Human Tracking by Skipped-Detection and Optical-Flow,
IEICE(E105-D), No. 11, November 2022, pp. 1938-1946.
WWW Link. 2212
BibRef

Shuai, B.[Bing], Bergamo, A.[Alessandro], Büchler, U.[Uta], Berneshawi, A.[Andrew], Boden, A.[Alyssa], Tighe, J.[Joseph],
Large Scale Real-World Multi-person Tracking,
ECCV22(VIII:504-521).
Springer DOI 2211
BibRef

Bae, I.[Inhwan], Park, J.H.[Jin-Hwi], Jeon, H.G.[Hae-Gon],
Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction,
ECCV22(XXII:270-289).
Springer DOI 2211
BibRef

Stadler, D.[Daniel], Beyerer, J.[Jürgen],
Modelling Ambiguous Assignments for Multi-Person Tracking in Crowds,
Activity22(133-142)
IEEE DOI 2202
Adaptation models, Interpolation, Tracking, Conferences, Computational modeling, Benchmark testing BibRef

Zhang, Y.[Yue], Caliskan, A.[Akin], Hilton, A.[Adrian], Guillemaut, J.Y.[Jean-Yves],
A Novel Multi-View Labelling Network Based on Pairwise Learning,
ICIP21(3682-3686)
IEEE DOI 2201
Deep learning, Knowledge engineering, Visualization, Solid modeling, Video tracking, Lighting, Multi-view network, multiple people labelling BibRef

Marathe, A.[Aboli], Walambe, R.[Rahee], Kotecha, K.[Ketan],
Evaluating the Performance of Ensemble Methods and Voting Strategies for Dense 2D Pedestrian Detection in the Wild,
ABAW21(3568-3577)
IEEE DOI 2112
Navigation, Computational modeling, Detectors, Object detection, Computer architecture BibRef

Stadler, D.[Daniel], Beyerer, J.[Jürgen],
Improving Multiple Pedestrian Tracking by Track Management and Occlusion Handling,
CVPR21(10953-10962)
IEEE DOI 2111
Visualization, Target tracking, Benchmark testing, Feature extraction, Pattern recognition, Reliability BibRef

Ho, K.[Kalun], Kardoost, A.[Amirhossein], Pfreundt, F.J.[Franz-Josef], Keuper, J.[Janis], Keuper, M.[Margret],
A Two-stage Minimum Cost Multicut Approach to Self-supervised Multiple Person Tracking,
ACCV20(II:539-557).
Springer DOI 2103
BibRef

Delorme, G.[Guillaume], Ban, Y.T.[Yu-Tong], Sarrazin, G.[Guillaume], Alameda-Pineda, X.[Xavier],
Odanet: Online Deep Appearance Network for Identity-consistent Multi-person Tracking,
MPRSS20(803-818).
Springer DOI 2103
BibRef

Shere, M., Kim, H., Hilton, A.,
3D Multi Person Tracking With Dual 360° Cameras,
ICIP20(2765-2769)
IEEE DOI 2011
Cameras, Skeleton, Tracking, Nonlinear distortion, 360 Imaging, Multi Person Tracking BibRef

Franchi, G., Aldea, E., Dubuisson, S., Bloch, I.,
Tracking Hundreds of People in Densely Crowded Scenes With Particle Filtering Supervising Deep Convolutional Neural Networks,
ICIP20(2071-2075)
IEEE DOI 2011
Training, Adaptive optics, Target tracking, Optical imaging, Task analysis, Deep learning BibRef

He, L.X.[Ling-Xiao], Liu, W.[Wu],
Guided Saliency Feature Learning for Person Re-identification in Crowded Scenes,
ECCV20(XXVIII:357-373).
Springer DOI 2011
BibRef

Zhang, Y.X.[Yu-Xiang], Li, Z.[Zhe], An, L.[Liang], Li, M.C.[Meng-Cheng], Yu, T.[Tao], Liu, Y.B.[Ye-Bin],
Lightweight Multi-person Total Motion Capture Using Sparse Multi-view Cameras,
ICCV21(5540-5549)
IEEE DOI 2203
Location awareness, Fitting, Cameras, Faces, Stereo, 3D from multiview and other sensors, Gestures and body pose BibRef

Zhang, Y.X.[Yu-Xiang], An, L.[Liang], Yu, T.[Tao], Li, X.[Xiu], Li, K.[Kun], Liu, Y.B.[Ye-Bin],
4D Association Graph for Realtime Multi-Person Motion Capture Using Multiple Video Cameras,
CVPR20(1321-1330)
IEEE DOI 2008
Skeleton, Tracking, Image edge detection, Optimization BibRef

Huang, X., Ge, Z., Jie, Z., Yoshie, O.,
NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing,
CVPR20(10747-10756)
IEEE DOI 2008
Detectors, Standards, Proposals, Feature extraction, Task analysis, Benchmark testing, Adaptation models BibRef

Lisotto, M., Coscia, P., Ballan, L.,
Social and Scene-Aware Trajectory Prediction in Crowded Spaces,
ACVR19(2567-2574)
IEEE DOI 2004
collision avoidance, human-robot interaction, mobile robots, recurrent neural nets, long short-term memory-based model, scene aware BibRef

Liu, S.T.[Song-Tao], Huang, D.[Di], Wang, Y.H.[Yun-Hong],
Adaptive NMS: Refining Pedestrian Detection in a Crowd,
CVPR19(6452-6461).
IEEE DOI 2002
BibRef

Chen, M.[Muchun], Chen, Y.[Yugang], Loc, T.T.[Truong Tan], Ni, B.B.[Bing-Bing],
Real-time Multiple Pedestrians Tracking in Multi-camera System,
MMMod20(I:468-479).
Springer DOI 2003
BibRef

Nayak, G.K., Shreemali, U., Babu, R.V., Chakraborty, A.,
Efficient Person Re-Identification in Videos Using Sequence Lazy Greedy Determinantal Point Process (SLGDPP),
ICIP19(4569-4573)
IEEE DOI 1910
Subset Selection, Determinantal Point Process, Sequence Greedy DPP, Person Re-id, Video Re-id BibRef

Xu, Q., Yang, H., Chen, L., Zhai, G.,
Group Re-Identification with Hybrid Attention Model and Residual Distance,
ICIP19(1217-1221)
IEEE DOI 1910
Group Re-ID, Hybrid Attention Model, Least Square Residual Distance BibRef

Ma, L.Q.[Li-Qian], Tang, S.[Siyu], Black, M.J.[Michael J.], Van Gool, L.J.[Luc J.],
Customized Multi-person Tracker,
ACCV18(II:612-628).
Springer DOI 1906
BibRef

Saqib, M., Daud Khan, S., Sharma, N., Blumenstein, M.,
Extracting descriptive motion information from crowd scenes,
IVCNZ17(1-6)
IEEE DOI 1902
feature extraction, image colour analysis, image sequences, motion estimation, pattern clustering, pedestrians, crowd scenes BibRef

Wang, X., Xiao, T., Jiang, Y., Shao, S., Sun, J., Shen, C.,
Repulsion Loss: Detecting Pedestrians in a Crowd,
CVPR18(7774-7783)
IEEE DOI 1812
Detectors, Proposals, Object detection, Benchmark testing, Euclidean distance, Feature extraction BibRef

Xu, Y., Piao, Z., Gao, S.,
Encoding Crowd Interaction with Deep Neural Network for Pedestrian Trajectory Prediction,
CVPR18(5275-5284)
IEEE DOI 1812
Trajectory, History, Legged locomotion, Neural networks, Encoding, Task analysis BibRef

Zhang, S.F.[Shi-Feng], Wen, L.Y.[Long-Yin], Bian, X.[Xiao], Lei, Z.[Zhen], Li, S.Z.[Stan Z.],
Occlusion-Aware R-CNN: Detecting Pedestrians in a Crowd,
ECCV18(III: 657-674).
Springer DOI 1810
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.H.[Wen-Han], 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.D.[Ognjen D.],
Multiple-object Tracking in Cluttered and Crowded Public Spaces,
ISVC10(III: 89-98).
Springer DOI 1011
BibRef

Luo, Z.Y.[Zheng-Yi], Golestaneh, S.A.[S. Alireza], Kitani, K.M.[Kris M.],
3D Human Motion Estimation via Motion Compression and Refinement,
ACCV20(V:324-340).
Springer DOI 2103
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

Li, Y.J.[Yu-Jhe], Weng, X.S.[Xin-Shuo], Kitani, K.M.[Kris M.],
Learning Shape Representations for Person Re-Identification under Clothing Change,
WACV21(2431-2440)
IEEE DOI 2106
Training, Image recognition, Shape, Computational modeling, Clothing 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 .