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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,
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BibRef
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Taking Mobile Multi-object Tracking to the Next Level:
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ECCV12(V: 566-579).
Springer DOI
1210
BibRef
Earlier:
Real-time multi-person tracking with detector assisted structure
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RobPerc11(974-981).
IEEE DOI
1201
BibRef
Mitzel, D.[Dennis],
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BMVC11(xx-yy).
HTML Version.
1110
BibRef
Mitzel, D.[Dennis],
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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
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ICCV11(1871-1878).
IEEE DOI
1201
BibRef
Breitenstein, M.D.[Michael D.],
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Leibe, B.[Bastian],
Koller-Meier, E.[Esther],
Van Gool, L.J.[Luc J.],
Online Multiperson Tracking-by-Detection from a Single, Uncalibrated
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PAMI(33), No. 9, September 2011, pp. 1820-1833.
IEEE DOI
1109
Possibly moving camera. Particle filter approach.
Generic and object specific knowledge.
BibRef
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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
Bai, Y.[Yang],
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Feature-Based Image Comparison for Semantic Neighbor Selection in
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JIVP(2010), No. 2010, pp. xx-yy.
DOI Link
1011
to merge information from multiple cameras
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BibRef
Qian, C.[Cheng],
Qi, H.R.[Hai-Rong],
A distributed solution to detect targets in crowds using visual sensor
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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],
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Higher-order SVD analysis for crowd density estimation,
CVIU(116), No. 9, September 2012, pp. 1014-1021.
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1208
Crowd density estimation; Tensor; HOSVD; SVM
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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],
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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.L.[Ming-Lei],
Multi-part sparse representation in random crowded scenes tracking,
PRL(34), No. 7, 1 May 2013, pp. 780-788.
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1303
Visual tracking; Multi-part sparse representation; Crowded scenes;
Particle filter
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Lv, W.,
Song, W.,
Ma, J.,
Fang, Z.,
A Two-Dimensional Optimal Velocity Model for Unidirectional
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ITS(14), No. 4, 2013, pp. 1753-1763.
IEEE DOI
1312
Data models
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Zafar, B.[Basim],
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Springer DOI
1312
Pedestrian motion analysis.
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Changes in Usage of an Indoor Public Space:
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HMS(45), No. 2, April 2015, pp. 228-237.
IEEE DOI
1503
Cameras
BibRef
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Nishino, K.,
Manocha, D.,
Shah, M., (Eds.)
Modeling, Simulation and Visual Analysis of Crowds:
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Warner, N.[Nolan],
Shah, M.[Mubarak],
Tracking in dense crowds using prominence and neighborhood motion
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1402
Crowd analysis
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Shah, M.[Mubarak],
Detecting Humans in Dense Crowds Using Locally-Consistent Scale Prior
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PAMI(37), No. 10, October 2015, pp. 1986-1998.
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1509
Cognition
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Tang, X.[Xiaoou],
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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.
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Tang, X.[Xiaoou],
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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,
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IEEE DOI
1208
BibRef
Fradi, H.[Hajer],
Eiselein, V.[Volker],
Dugelay, J.L.[Jean-Luc],
Keller, I.[Ivo],
Sikora, T.[Thomas],
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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
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Keller, I.[Ivo],
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Crowd analysis in non-static cameras using feature tracking and
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ICIP14(6041-6045)
IEEE DOI
1502
Cameras
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Jin, Z.X.[Zhi-Xing],
Bhanu, B.[Bir],
Analysis-by-synthesis: Pedestrian tracking with crowd simulation
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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.
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Rojas, F.[Francisco],
Tarnogol, F.[Fernando],
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Dynamic social formations of pedestrian groups navigating and using
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VC(32), No. 3, March 2016, pp. 335-345.
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1604
BibRef
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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
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Feng, P.,
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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
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IEICE(E103-D), No. 6, June 2020, pp. 1265-1275.
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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.B.[Wen-Bo],
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.Z.T.[Cong-Zhen-Tao],
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
Han, R.Z.[Rui-Ze],
Feng, W.[Wei],
Wang, F.F.[Fei-Fan],
Qian, Z.K.[Ze-Kun],
Yan, H.M.[Hao-Min],
Wang, S.[Song],
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IJCV(132), No. 1, January 2024, pp. 118-136.
Springer DOI
2402
BibRef
Sun, H.[Hao],
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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],
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High quality proposal feature generation for crowded pedestrian
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PR(128), 2022, pp. 108605.
Elsevier DOI
2205
Crowded pedestrian, Pedestrian detection, Visible proposal,
Feature fusion, Paired prediction
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Zhu, Y.P.[Yi-Peng],
Wang, T.[Tao],
Zhu, S.Q.[Shi-Qiang],
Adaptive Multi-Pedestrian Tracking by Multi-Sensor: Track-to-Track
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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.G.[Yong-Gi],
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
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WWW Link.
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Li, M.[Ming],
Bai, L.[Lu],
Multiple Pedestrian Tracking With Graph Attention Map on Urban Road
Scene,
ITS(24), No. 8, August 2023, pp. 8567-8579.
IEEE DOI
2308
Target tracking, Feature extraction, Roads, Head, Detectors,
Task analysis, Object tracking, Multiple pedestrian tracking,
urban roads
BibRef
He, H.Y.[Hao-Yang],
Li, Z.[Zhishan],
Tian, G.Z.[Guan-Zhong],
Chen, H.X.[Hong-Xu],
Xie, L.[Lei],
Lu, S.[Shan],
Su, H.Y.[Hong-Ye],
Towards accurate dense pedestrian detection via occlusion-prediction
aware label assignment and hierarchical-NMS,
PRL(174), 2023, pp. 78-84.
Elsevier DOI
2310
Pedestrian detection, Matching quality,
Occlusion-Prediction aware Label Assignment,
Hierarchical Non-Maximum Suppression
BibRef
Cai, K.[Kuanqi],
Chen, W.N.[Wei-Nan],
Dugas, D.[Daniel],
Siegwart, R.[Roland],
Chung, J.J.[Jen Jen],
Sampling-Based Path Planning in Highly Dynamic and Crowded Pedestrian
Flow,
ITS(24), No. 12, December 2023, pp. 14732-14742.
IEEE DOI
2312
BibRef
Liu, C.C.[Chen-Chen],
Mu, Y.D.[Ya-Dong],
Multi-Granularity Interaction for Multi-Person 3D Motion Prediction,
CirSysVideo(34), No. 3, March 2024, pp. 1546-1558.
IEEE DOI
2403
Predictive models, Task analysis, Trajectory, Transformers,
Convolutional neural networks, Solid modeling,
multi-granularity interaction
BibRef
Wang, C.[Cui],
Wu, Z.W.[Ze-Wei],
Ke, W.[Wei],
Xiong, Z.[Zhang],
A simple transformer-based baseline for crowd tracking with
Sequential Feature Aggregation and Hybrid Group Training,
JVCIR(100), 2024, pp. 104144.
Elsevier DOI
2405
Crowd tracking, Transformer-based tracking,
Temporal enhanced representation, Hybrid Group Training
BibRef
Ali, H.[Hassan],
Butt, M.A.[Muhammad Atif],
Filali, F.[Fethi],
Al-Fuqaha, A.[Ala],
Qadir, J.[Junaid],
Consistent Valid Physically-Realizable Adversarial Attack Against
Crowd-Flow Prediction Models,
ITS(25), No. 6, June 2024, pp. 5567-5582.
IEEE DOI
2406
Perturbation methods, Standards, Adaptation models,
Computer architecture, Analytical models, History, Data models,
adversarial ML
BibRef
Feng, W.[Wei],
Wang, F.[Feifan],
Han, R.[Ruize],
Gan, Y.Y.[Yi-Yang],
Qian, Z.K.[Ze-Kun],
Hou, J.H.[Jun-Hui],
Wang, S.[Song],
Unveiling the Power of Self-Supervision for Multi-View Multi-Human
Association and Tracking,
PAMI(47), No. 1, January 2025, pp. 351-368.
IEEE DOI
2412
Target tracking, Cameras, Benchmark testing,
Self-supervised learning, Training, Object tracking
BibRef
Sun, Z.H.[Zhi-Hong],
Wei, G.[Guoheng],
Fu, W.[Wei],
Ye, M.[Mang],
Jiang, K.[Kui],
Liang, C.[Chao],
Zhu, T.T.[Ting-Ting],
He, T.[Tao],
Mukherjee, M.[Mithun],
Multiple Pedestrian Tracking Under Occlusion: A Survey and Outlook,
CirSysVideo(35), No. 2, February 2025, pp. 1009-1027.
IEEE DOI
2502
Pedestrians, Surveys, Trajectory, Target tracking, Cameras, Reviews,
Object tracking, trajectory recovery
BibRef
Xie, W.[Wei],
Jiang, N.[Nan],
Ma, Y.[Yi],
Lee, E.W.M.[Eric Wai Ming],
Li, X.T.[Xin-Tong],
Yu, H.[Hanchen],
Simulating Pedestrian Flow on Slopes via Transfer Learning Approach:
From Single-File to Crowd,
ITS(26), No. 3, March 2025, pp. 3873-3884.
IEEE DOI
2503
Pedestrians, Legged locomotion, Dynamics, Stairs, Transfer learning,
Trajectory, Data models, Microscopy, Long short term memory,
sloped walkways
BibRef
Jung, H.[Hyeonchul],
Kang, S.[Seokjun],
Kim, T.[Takgen],
Kim, H.[HyeongKi],
ConfTrack: Kalman Filter-based Multi-Person Tracking by Utilizing
Confidence Score of Detection Box,
WACV24(6569-6578)
IEEE DOI
2404
Matched filters, Noise, Detectors, Switches, Filtering algorithms,
Robustness, Algorithms, Video recognition and understanding,
Image recognition and understanding
BibRef
Xu, Q.Y.[Qing-Yao],
Mao, W.[Weibo],
Gong, J.Z.[Jing-Ze],
Xu, C.X.[Chen-Xin],
Chen, S.[Siheng],
Xie, W.[Weidi],
Zhang, Y.[Ya],
Wang, Y.F.[Yan-Feng],
Joint-Relation Transformer for Multi-Person Motion Prediction,
ICCV23(9782-9792)
IEEE DOI Code:
WWW Link.
2401
BibRef
Stadler, D.[Daniel],
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Past Information Aggregation for Multi-Person Tracking,
ICIP23(321-325)
IEEE DOI
2312
BibRef
Wei, R.N.[Ruo-Nan],
Wang, Y.[Yuehuan],
Zhang, J.[Jinpu],
Learning Mutually in Crowd Scenes for Pedestrian Detection,
ICIP23(1900-1904)
IEEE DOI
2312
BibRef
Stadler, D.[Daniel],
Beyerer, J.[Jürgen],
An Improved Association Pipeline for Multi-Person Tracking,
E2EAD23(3170-3179)
IEEE DOI
2309
BibRef
Kim, J.[Jeongho],
Shin, W.[Wooksu],
Park, H.[Hancheol],
Baek, J.W.[Jong-Won],
Addressing the Occlusion Problem in Multi-Camera People Tracking with
Human Pose Estimation,
AICity23(5463-5469)
IEEE DOI
2309
BibRef
Huang, H.W.[Hsiang-Wei],
Yang, C.Y.[Cheng-Yen],
Jiang, Z.Y.[Zhong-Yu],
Kim, P.K.[Pyong-Kun],
Lee, K.[Kyoungoh],
Kim, K.[Kwangju],
Ramkumar, S.[Samartha],
Mullapudi, C.[Chaitanya],
Jang, I.S.[In-Su],
Huang, C.I.[Chung-I],
Hwang, J.N.[Jenq-Neng],
Enhancing Multi-Camera People Tracking with Anchor-Guided Clustering
and Spatio-Temporal Consistency ID Re-Assignment,
AICity23(5239-5249)
IEEE DOI
2309
BibRef
Yang, W.J.[Wen-Jie],
Xie, Z.Y.[Zhen-Yu],
Wang, Y.M.[Yao-Ming],
Zhang, Y.[Yang],
Ma, X.[Xiao],
Hao, B.[Bing],
Integrating Appearance and Spatial-Temporal Information for
Multi-Camera People Tracking,
AICity23(5260-5269)
IEEE DOI
2309
BibRef
Zhu, D.K.[De-Kai],
Zhai, G.Y.[Guang-Yao],
Di, Y.[Yan],
Manhardt, F.[Fabian],
Berkemeyer, H.[Hendrik],
Tran, T.[Tuan],
Navab, N.[Nassir],
Tombari, F.[Federico],
Busam, B.[Benjamin],
IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for
Joint Multi-Agent Trajectory Prediction,
CVPR23(5507-5516)
IEEE DOI
2309
BibRef
Li, Z.Y.[Zong-Yi],
Wang, R.S.[Run-Sheng],
Li, H.[He],
Wei, B.[Bohao],
Shi, Y.X.[Yu-Xuan],
Ling, H.[Hefei],
Chen, J.Z.[Jia-Zhong],
Liu, B.Y.[Bo-Yuan],
Li, Z.Y.[Zhong-Yang],
Zheng, H.Q.[Han-Qing],
Hierarchical Clustering and Refinement for Generalized Multi-Camera
Person Tracking,
AICity23(5520-5529)
IEEE DOI
2309
BibRef
Medeiros, H.R.[Heitor Rapela],
Peña, F.A.G.[Fidel A. Guerrero],
Aminbeidokhti, M.[Masih],
Dubail, T.[Thomas],
Granger, E.[Eric],
Pedersoli, M.[Marco],
HalluciDet: Hallucinating RGB Modality for Person Detection Through
Privileged Information,
WACV24(1433-1442)
IEEE DOI Code:
WWW Link.
2404
Training, Adaptation models, Visualization, Pedestrians, Detectors,
Object detection, Image representation, Algorithms
BibRef
Dubail, T.[Thomas],
Peña, F.A.G.[Fidel Alejandro Guerrero],
Medeiros, H.R.[Heitor Rapela],
Aminbeidokhti, M.[Masih],
Granger, E.[Eric],
Pedersoli, M.[Marco],
Privacy-preserving Person Detection Using Low-resolution Infrared
Cameras,
RealWorld22(689-702).
Springer DOI
2304
BibRef
Simsek, F.E.[Fatih Emre],
Cigla, C.[Cevahir],
Kayabol, K.[Koray],
Sompt22: A Surveillance Oriented Multi-pedestrian Tracking Dataset,
RealWorld22(659-675).
Springer DOI
2304
BibRef
Asanomi, T.[Takanori],
Nishimura, K.[Kazuya],
Bise, R.[Ryoma],
Multi-Frame Attention with Feature-Level Warping for Drone Crowd
Tracking,
WACV23(1664-1673)
IEEE DOI
2302
Head, Codes, Annotations, Aggregates, Video surveillance,
Object tracking, visual reasoning)
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
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
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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, 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
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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
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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.G.[Yu-Gang],
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
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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
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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
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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
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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
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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
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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
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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 -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Tracking Several People .