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, ISPRS, Best Paper. 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,
 
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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.[Caiyou],
Huang, Y.[Yuteng],
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
Sawas, A.[Abdullah],
Abuolaim, A.[Abdullah],
Afifi, M.[Mahmoud],
Papagelis, M.[Manos],
A versatile computational framework for group pattern mining of
pedestrian trajectories,
GeoInfo(23), No. 4, October 2019, pp. 501-531.
WWW Link.
1911
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.[Zhicheng],
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
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.[Liqian],
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,
Computer vision
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,
Computer vision, 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.[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 .