Rigoll, G.[Gerhard],
Breit, H.[Harald],
Wallhoff, F.[Frank],
Robust tracking of persons in real-world scenarios using a statistical
computer vision approach,
IVC(22), No. 7, July 2004, pp. 571-582.
Elsevier DOI
0405
BibRef
Rigoll, G.[Gerhard],
Winterstein, B.,
Müller, S.[Stefan],
Robust Person Tracking in Real Scenarios with Non-Stationary Background
Using a Statistical Computer Vision Approach,
VS99(xx-yy).
BibRef
9900
And: A1, A3, A2:
Robust Person Tracking with Non-Stationary Background Using a Combined
Pseudo-2D-HMM and Kalman-Filter Approach,
ICIP99(IV:242-246).
IEEE DOI
BibRef
Rigoll, G.[Gerhard],
Eickeler, S.[Stefan],
Müller, S.[Stefan],
Person Tracking in Real-World Scenarios Using Statistical Methods,
AFGR00(342-347).
IEEE DOI
0003
BibRef
And: A3, A2, A1:
Crane Gesture Recognition using Pseudo 3-D Hidden Markov Models,
AFGR00(398-402).
IEEE DOI
0003
BibRef
Earlier: A3, A2, A1:
Pseudo 3-D HMMs for Image Sequence Recognition,
ICIP99(IV:237-241).
IEEE DOI
BibRef
Eickeler, S.[Stefan],
Rigoll, G.[Gerhard],
Hidden Markov Model Based Continuous Online Gesture Recognition,
ICPR98(Vol II: 1206-1208).
IEEE DOI
9808
BibRef
Breit, H.,
Rigoll, G.,
A flexible multimodal object tracking system,
ICIP03(III: 133-136).
IEEE DOI
0312
BibRef
Earlier:
Improved Person Tracking Using a Combined Pseudo-2D-HMM and
Kalman Filter Approach with Automatic Background State Adaptation,
ICIP01(II: 53-56).
IEEE DOI
0108
Sports?
BibRef
Wachter, S.,
Nagel, H.H.,
Tracking Persons in Monocular Image Sequences,
CVIU(74), No. 3, June 1999, pp. 174-192.
DOI Link
BibRef
9906
Brill, F.Z.[Frank Z.],
Olson, T.J.[Thomas J.],
Method of dealing with occlusion when tracking multiple
objects and people in video sequences,
US_Patent6,542,621, Apr 1, 2003
WWW Link.
BibRef
0304
Lerdsudwichai, C.[Charay],
Abdel-Mottaleb, M.[Mohamed],
Ansari, A.N.[A-Nasser],
Tracking multiple people with recovery from partial and total occlusion,
PR(38), No. 7, July 2005, pp. 1059-1070.
Elsevier DOI
0505
BibRef
Kang, H.G.[Hee-Gu],
Kim, D.J.[Dai-Jin],
Real-time multiple people tracking using competitive condensation,
PR(38), No. 7, July 2005, pp. 1045-1058.
Elsevier DOI
0505
BibRef
Earlier:
Add A3:
Bang, S.Y.[Sung Yang],
ICPR02(I: 413-416).
IEEE DOI
0211
BibRef
And:
ICIP02(III: 325-328).
IEEE DOI
0210
BibRef
Khan, Z.[Zia],
Balch, T.[Tucker],
Dellaert, F.[Frank],
MCMC-Based Particle Filtering for Tracking a Variable Number of
Interacting Targets,
PAMI(27), No. 11, November 2005, pp. 1805-1918.
IEEE DOI
0510
BibRef
Earlier:
A Rao-Blackwellized particle filter for eigentracking,
CVPR04(II: 980-986).
IEEE DOI
0408
BibRef
And:
An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets,
ECCV04(Vol IV: 279-290).
Springer DOI
0405
Markov-Chain Monte Carlo.
Deal with problems that arise when the tracking is lost.
See also EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation.
BibRef
Lanz, O.[Oswald],
Approximate Bayesian Multibody Tracking,
PAMI(28), No. 9, September 2006, pp. 1436-1449.
IEEE DOI
0608
BibRef
Lanz, O.[Oswald],
Manduchi, R.[Roberto],
Hybrid Joint-Separable Multibody Tracking,
CVPR05(I: 413-420).
IEEE DOI
0507
BibRef
Lanz, O.[Oswald],
Hu, T.[Tao],
Dynamic resource allocation for probabilistic tracking via attentive
sensing and sampling,
RAWNETS11(432-434).
IEEE DOI
1111
BibRef
Rui, Y.[Yong],
Chen, Y.Q.[Yun-Qiang],
Automatic detection and tracking of multiple individuals
using multiple cues,
US_Patent7,130,446, Oct 31, 2006
WWW Link.
BibRef
0610
And:
US_Patent7,151,843, Dec 19, 2006
WWW Link.
BibRef
And:
US_Patent7,171,025, Jan 30, 2007
WWW Link.
BibRef
And:
US_Patent7,428,315, Sep 23, 200
WWW Link.
BibRef
Zhao, T.[Tao],
Aggarwal, M.[Manoj],
Germano, T.[Thomas],
Roth, I.[Ian],
Knowles, A.[Alexandar],
Kumar, R.[Rakesh],
Sawhney, H.[Harpreet],
Samarasekera, S.[Supun],
Toward a sentient environment:
Real-time wide area multiple human tracking with identities,
MVA(19), No. 5-6, October 2008, pp. xx-yy.
Springer DOI
0810
BibRef
Min, J.H.[Jung-Hye],
Kasturi, R.[Rangachar],
Camps, O.I.[Octavia I.],
Extraction and Temporal Segmentation of Multiple Motion Trajectories in
Human Motion,
IVC(26), No. 12, 1 December 2008, pp. 1621-1635.
Elsevier DOI
0810
BibRef
Earlier: A1, A2, Only:
EventVideo04(118).
IEEE DOI
0502
Activity recognition; Motion trajectories; Motion tracking; Motion
segmentation; Motion detection; Temporal segmentation
BibRef
Min, J.H.[Jung-Hye],
Park, J.H.[Jin Hyeong],
Kasturi, R.[Rangachar],
Extraction of Multiple Motion Trajectories in Human Motion,
SCIA03(1050-1057).
Springer DOI
0310
BibRef
Yu, Y.[Yang],
Harwood, D.[David],
Yoon, K.[Kyongil],
Davis, L.S.[Larry S.],
Human appearance modeling for matching across video sequences,
MVA(18), No. 3-4, August 2007, pp. 139-149.
Springer DOI
0706
BibRef
Haritaoglu, I.,
Harwood, D.[David],
Davis, L.S.[Larry S.],
An Appearance-based Body Model for Multiple People Tracking,
ICPR00(Vol IV: 184-187).
IEEE DOI
0009
BibRef
And:
Hydra: Multiple People Detection and Tracking Using Silhouettes,
CIAP99(280-285).
IEEE DOI
9909
BibRef
And:
VS99(xx-yy).
See also Ghost: A Human Body Part Labeling System Using Silhouettes. Ghost system
BibRef
Davis, L.S.,
Philomin, V.,
Duraiswami, R.,
Tracking Humans from a Moving Platform,
ICPR00(Vol IV: 171-178).
IEEE DOI
0009
BibRef
Tran, S.D.[Son D.],
Lin, Z.[Zhe],
Harwood, D.[David],
Davis, L.S.[Larry S.],
UMD_VDT, an Integration of Detection and Tracking Methods for Multiple
Human Tracking,
MTPH07(xx-yy).
Springer DOI
0705
BibRef
Lin, Z.[Zhe],
Davis, L.S.[Larry S.],
Shape-Based Human Detection and Segmentation via Hierarchical
Part-Template Matching,
PAMI(32), No. 4, April 2010, pp. 604-618.
IEEE DOI
1003
BibRef
Earlier:
A Pose-Invariant Descriptor for Human Detection and Segmentation,
ECCV08(IV: 423-436).
Springer DOI
0810
BibRef
And:
Learning Pairwise Dissimilarity Profiles for Appearance Recognition in
Visual Surveillance,
ISVC08(I: 23-34).
Springer DOI
0812
Combine local part based and global shape based schemes for detection
and segmentation. Match a part template tree hiarchically to images.
Adaptive extraction of features in learning. (SVM classifier).
BibRef
Qiu, Q.A.[Qi-Ang],
Jiang, Z.L.[Zhuo-Lin],
Chellappa, R.[Rama],
Sparse dictionary-based representation and recognition of action
attributes,
ICCV11(707-714).
IEEE DOI
1201
BibRef
Jiang, Z.L.[Zhuo-Lin],
Lin, Z.[Zhe],
Davis, L.S.[Larry S.],
Recognizing Human Actions by Learning and Matching Shape-Motion
Prototype Trees,
PAMI(34), No. 3, March 2012, pp. 533-547.
IEEE DOI
1201
BibRef
Earlier: A2, A1, A3:
Recognizing Actions by Shape-motion Prototype Trees,
ICCV09(444-451).
IEEE DOI
0909
BibRef
Jiang, Z.L.[Zhuo-Lin],
Lin, Z.[Zhe],
Davis, L.S.[Larry S.],
Class consistent k-means: Application to face and action recognition,
CVIU(116), No. 6, June 2012, pp. 730-741.
Elsevier DOI
1204
Action recognition; Face recognition; Supervised clustering; Class
consistent k-means;
Discriminative tree classifier
BibRef
Jiang, Z.L.[Zhuo-Lin],
Lin, Z.[Zhe],
Davis, L.S.[Larry S.],
A unified tree-based framework for joint action localization,
recognition and segmentation,
CVIU(117), No. 10, 2013, pp. 1345-1355.
Elsevier DOI
1309
Action recognition
BibRef
Lin, Z.[Zhe],
Davis, L.S.[Larry S.],
Doermann, D.S.[David S.],
DeMenthon, D.F.[Daniel F.],
Hierarchical Part-Template Matching for Human Detection and
Segmentation,
ICCV07(1-8).
IEEE DOI
0710
BibRef
And:
An Interactive Approach to Pose-Assisted and Appearance-based
Segmentation of Humans,
ICV07(1-8).
IEEE DOI
0710
BibRef
Earlier:
Simultaneous Appearance Modeling and Segmentation for Matching People
Under Occlusion,
ACCV07(II: 404-413).
Springer DOI
0711
BibRef
Capellades, M.B.,
Doermann, D.S.,
DeMenthon, D.F.,
Chellappa, R.,
An appearance based approach for human and object tracking,
ICIP03(II: 85-88).
IEEE DOI
0312
BibRef
Wang, J.[Jing],
Yin, Y.F.[Ya-Feng],
Man, H.[Hong],
Multiple Human Tracking Using Particle Filter with Gaussian Process
Dynamical Model,
JIVP(2008), No. 2008, pp. xx-yy.
DOI Link
0804
BibRef
And: A1, A3, A2:
Tracking human body by using particle filter Gaussian process
Markov-switching model,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Earlier: A1, A3, A2:
Multitarget tracking using Gaussian Process Dynamical Model particle
filter,
ICIP08(1580-1583).
IEEE DOI
0810
BibRef
Yin, Y.F.[Ya-Feng],
Man, H.[Hong],
Wang, J.[Jing],
Yang, G.[Guang],
Human Motion Change Detection by Hierarchical Gaussian Process
Dynamical Model with Particle Filter,
AVSS10(307-314).
IEEE DOI
1009
BibRef
Hu, W.M.[Wei-Ming],
Zhou, X.[Xue],
Hu, M.[Min],
Maybank, S.J.[Steve J.],
Occlusion Reasoning for Tracking Multiple People,
CirSysVideo(19), No. 1, January 2009, pp. 114-121.
IEEE DOI
0902
BibRef
Wang, K.[Kan],
Ding, C.X.[Chang-Xing],
Maybank, S.J.[Stephen J.],
Tao, D.C.[Da-Cheng],
CDPM: Convolutional Deformable Part Models for Semantically Aligned
Person Re-Identification,
IP(29), 2020, pp. 3416-3428.
IEEE DOI
2002
Person re-identification, alignment-robust recognition,
part-based model, multi-task learning
BibRef
Ma, Y.Q.[Yun-Qian],
Yu, Q.[Qian],
Cohen, I.[Isaac],
Target tracking with incomplete detection,
CVIU(113), No. 4, April 2009, pp. 580-587.
Elsevier DOI
0903
BibRef
Earlier:
Multiple Hypothesis Target Tracking Using Merge and Split of Graph's
Nodes,
ISVC06(I: 783-792).
Springer DOI
PDF File.
0611
Multiple target tracking; Split and merge of detected regions; Maximum
a posteriori
BibRef
Yu, Q.[Qian],
Medioni, G.[Gerard],
Multiple-Target Tracking by Spatiotemporal Monte Carlo Markov Chain
Data Association,
PAMI(31), No. 12, December 2009, pp. 2196-2210.
IEEE DOI
0911
BibRef
Earlier:
Integrated Detection and Tracking for Multiple Moving Objects using
Data-Driven MCMC Data Association,
Motion08(1-8).
IEEE DOI
PDF File.
0801
BibRef
Earlier:
Map-Enhanced Detection and Tracking from a Moving Platform with Local
and Global Data Association,
Motion07(3-3).
IEEE DOI
PDF File.
0702
Apply espceciall to people tracking, but also vehicles.
BibRef
Dinh, T.B.[Thang Ba],
Vo, N.N.[Nam N.],
Medioni, G.[Gerard],
Context tracker:
Exploring supporters and distracters in unconstrained environments,
CVPR11(1177-1184).
IEEE DOI
1106
BibRef
Dinh, T.B.[Thang Ba],
Vo, N.N.[Nam N.],
Medioni, G.[Gerard],
High resolution face sequences from a PTZ network camera,
FG11(531-538).
IEEE DOI
1103
BibRef
Cai, Y.H.[Ying-Hao],
Medioni, G.[Gérard],
Persistent people tracking and face capture using a PTZ camera,
MVA(27), No. 3, April 2016, pp. 397-413.
Springer DOI
1604
BibRef
Earlier: A2, A1:
Persistent People Tracking and Face Capture over a Wide Area,
LTDT14(714-715)
IEEE DOI
1409
BibRef
Earlier: A1, A2:
Exploring context information for inter-camera multiple target
tracking,
WACV14(761-768)
IEEE DOI
1406
Cameras; Color; Context;Histograms; Image color analysis; Target tracking
BibRef
Cai, Y.H.[Ying-Hao],
Medioni, G.,
Dinh, T.B.[Thang Ba],
Towards a practical PTZ face detection and tracking system,
WACV13(31-38).
IEEE DOI
1303
BibRef
Dinh, T.B.[Thang Ba],
Medioni, G.[Gerard],
Co-training framework of generative and discriminative trackers with
partial occlusion handling,
WMVC11(642-649).
IEEE DOI
1101
BibRef
Dinh, T.B.[Thang Ba],
Yu, Q.[Qian],
Medioni, G.[Gérard],
Real time tracking using an active pan-tilt-zoom network camera,
IROS09(3786-3793).
PDF File.
BibRef
0900
Yu, Q.[Qian],
Dinh, T.B.[Thang Ba],
Medioni, G.[Gérard],
Online Tracking and Reacquisition Using Co-trained Generative and
Discriminative Trackers,
ECCV08(II: 678-691).
Springer DOI
0810
BibRef
Yu, Q.[Qian],
Medioni, G.[Gerard],
Cohen, I.[Isaac],
Multiple Target Tracking Using Spatio-Temporal Markov Chain Monte Carlo
Data Association,
CVPR07(1-8).
IEEE DOI
PDF File.
0706
BibRef
Yu, Q.[Qian],
Cohen, I.[Isaac],
Medioni, G.[Gerard],
Wu, B.[Bo],
Boosted Markov Chain Monte Carlo Data Association for Multiple Target
Detection and Tracking,
ICPR06(II: 675-678).
IEEE DOI
PDF File.
0609
BibRef
Sherrah, J.[Jamie],
Ristic, B.[Branko],
Redding, N.J.[Nicholas J.],
Particle filter to track multiple people for visual surveillance,
IET-CV(5), No. 4, 2011, pp. 192-200.
DOI Link
1107
BibRef
Earlier:
Evaluation of a Particle Filter to Track People for Visual Surveillance,
DICTA09(96-102).
IEEE DOI
0912
See also Online Tracking of People through a Camera Network.
BibRef
de Laet, T.[Tinne],
Bruyninckx, H.[Herman],
de Schutter, J.[Joris],
Shape-Based Online Multitarget Tracking and Detection for Targets
Causing Multiple Measurements: Variational Bayesian Clustering and
Lossless Data Association,
PAMI(33), No. 12, December 2011, pp. 2477-2491.
IEEE DOI
1110
Multiple measurements for target. High level target position and shape,
low-level clusters of measurements.
Apply to video and laser date for tracking people and ants.
BibRef
Huo, F.F.[Fei-Fei],
Hendriks, E.A.[Emile A.],
Multiple people tracking and pose estimation with occlusion estimation,
CVIU(116), No. 5, May 2012, pp. 634-647.
Elsevier DOI
1203
Multiple people tracking; Pose estimation; Occlusion estimation
BibRef
Li, L.,
Yan, S.,
Yu, X.,
Tan, Y.K.,
Li, H.,
Robust Multiperson Detection and Tracking for Mobile Service and Social
Robots,
SMC-B(42), No. 5, October 2012, pp. 1398-1412.
IEEE DOI
1209
BibRef
Shen, Y.[Yuan],
Miao, Z.J.[Zhen-Jiang],
Multi-human tracking from sparse detection responses,
IET-CV(6), No. 6, 2012, pp. 590-602.
DOI Link
1301
BibRef
Shen, Y.[Yuan],
Miao, Z.J.[Zhen-Jiang],
Wang, Z.F.[Zhi-Fei],
A cost function approach for multi-human tracking,
ICIP11(481-484).
IEEE DOI
1201
BibRef
Sun, L.[Li],
Liu, G.Z.[Gui-Zhong],
Liu, Y.Q.[Yi-Qing],
Multiple pedestrians tracking algorithm by incorporating histogram of
oriented gradient detections,
IET-IPR(7), No. 7, October 2013, pp. 653-659.
DOI Link
1312
gradient methods
BibRef
Brscic, D.,
Kanda, T.,
Ikeda, T.,
Miyashita, T.,
Person Tracking in Large Public Spaces Using 3-D Range Sensors,
HMS(43), No. 6, 2013, pp. 522-534.
IEEE DOI
1312
Cameras
BibRef
Madrigal, F.[Francisco],
Hayet, J.B.[Jean-Bernard],
Lerasle, F.[Frédéric],
Improving multiple pedestrians tracking with semantic information,
SIViP(8), No. S1, December 2014, pp. 113-123.
Springer DOI
1411
BibRef
Earlier:
Intention-Aware Multiple Pedestrian Tracking,
ICPR14(4122-4127)
IEEE DOI
1412
Dynamics; Force; Proposals; Semantics; Target tracking; Trajectory
BibRef
Madrigal, F.[Francisco],
Maurice, C.[Camille],
Lerasle, F.[Frédéric],
Hyper-parameter optimization tools comparison for multiple object
tracking applications,
MVA(30), No. 2, March 2019, pp. 269-289.
WWW Link.
1904
BibRef
Earlier: A2, A1, A3:
Hyper-Optimization tools comparison for parameter tuning applications,
AVSS17(1-6)
IEEE DOI
1806
object tracking, optimisation,
video signal processing, hyperparameters optimization tools,
nTuning
BibRef
Maurice, C.[Camille],
Madrigal, F.[Francisco],
Lerasle, F.[Frédéric],
Late Fusion of Bayesian and Convolutional Models for Action
Recognition,
ICPR21(3296-3303)
IEEE DOI
2105
learning, Video sequences, Neural networks, Performance gain,
Bayes methods
BibRef
Zuriarrain, I.,
Lerasle, F.,
Arana, N.,
Devy, M.,
An MCMC-based particle filter for multiple person tracking,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Ayala-Ramirez, V.,
Parra, C.,
Devy, M.,
Active Tracking Based on Hausdorff Matching,
ICPR00(Vol IV: 706-709).
IEEE DOI
0009
BibRef
Gomez, D.G.[David Gerónimo],
Lerasle, F.[Frédéric],
López Peńa, A.M.[Antonio M.],
State-Driven Particle Filter for Multi-person Tracking,
ACIVS12(467-478).
Springer DOI
1209
BibRef
Cancela, B.,
Ortega, M.,
Penedo, M.G.,
Multiple human tracking system for unpredictable trajectories,
MVA(25), No. 2, February 2014, pp. 511-527.
WWW Link.
1402
BibRef
Shen, Y.[Yuan],
Miao, Z.J.[Zhen-Jiang],
Multihuman Tracking Based on a Spatial-Temporal Appearance Match,
CirSysVideo(24), No. 3, March 2014, pp. 361-373.
IEEE DOI
1404
Bayes methods
BibRef
Lee, D.H.[Dong-Hoon],
Hwang, I.[Inhwan],
Oh, S.H.[Song-Hwai],
OPTIMUS: Online Persistent Tracking and Identification of Many Users
for Smart Spaces,
MVA(25), No. 4, May 2014, pp. 901-917.
WWW Link.
1404
BibRef
Wang, L.[Lu],
Yung, N.H.C.[Nelson H.C.],
Xu, L.S.[Li-Sheng],
Multiple-Human Tracking by Iterative Data Association and Detection
Update,
ITS(15), No. 5, October 2014, pp. 1886-1899.
IEEE DOI
1410
BibRef
Earlier: A1, A2, Only:
Detection Based Low Frame Rate Human Tracking,
ICPR10(3529-3532).
IEEE DOI
1008
feature extraction
BibRef
Peng, P.X.[Pei-Xi],
Tian, Y.H.[Yong-Hong],
Wang, Y.W.[Yao-Wei],
Li, J.[Jia],
Huang, T.J.[Tie-Jun],
Robust multiple cameras pedestrian detection with multi-view Bayesian
network,
PR(48), No. 5, 2015, pp. 1760-1772.
Elsevier DOI
1502
BibRef
Earlier: A1, A2, A3, A5, Only:
Multi-camera Pedestrian Detection with Multi-view Bayesian Network
Model,
BMVC12(69).
DOI Link
1301
Pedestrian detection
BibRef
Xu, T.[Teng],
Peng, P.X.[Pei-Xi],
Fang, X.Y.[Xiao-Yu],
Su, C.[Chi],
Wang, Y.W.[Yao-Wei],
Tian, Y.H.[Yong-Hong],
Zeng, W.[Wei],
Huang, T.J.[Tie-Jun],
Single and Multiple View Detection, Tracking and Video Analysis in
Crowded Environments,
AVSS12(494-499).
IEEE DOI
1211
BibRef
Chen, L.[Lili],
Wang, W.[Wei],
Panin, G.,
Knoll, A.,
Hierarchical Grid-based Multi-People Tracking-by-Detection With
Global Optimization,
IP(24), No. 11, November 2015, pp. 4197-4212.
IEEE DOI
1509
image sequences
BibRef
McLaughlin, N.[Niall],
del Rincon, J.M.[Jesus Martinez],
Miller, P.[Paul],
Dense Multiperson Tracking with Robust Hierarchical Linear Assignment,
Cyber(45), No. 7, July 2015, pp. 1276-1288.
IEEE DOI
1506
BibRef
Earlier:
Enhancing Linear Programming with Motion Modeling for Multi-target
Tracking,
WACV15(71-77)
IEEE DOI
1503
BibRef
Earlier:
Online multiperson tracking with occlusion reasoning and unsupervised
track motion model,
AVSS13(37-42)
IEEE DOI
1311
Cost function.
Detectors
Computational modeling
BibRef
Li, Y.[Yuke],
Shen, W.M.[Wei-Ming],
Inter-Person Occlusion Handling with Social Interaction for Online
Multi-Pedestrian Tracking,
IEICE(E99-D), No. 12, December 2016, pp. 3165-3171.
WWW Link.
1612
BibRef
Earlier:
Social interaction based handling inter-person occlusion for online
multi-pedestrian tracking,
AVSS15(1-6)
IEEE DOI
1511
image motion analysis
BibRef
Ba, S.O.[Sileye O.],
Alameda-Pineda, X.[Xavier],
Xompero, A.[Alessio],
Horaud, R.[Radu],
An on-line variational Bayesian model for multi-person tracking from
cluttered scenes,
CVIU(153), No. 1, 2016, pp. 64-76.
Elsevier DOI
1612
Multi-person tracking
BibRef
Ban, Y.T.[Yu-Tong],
Ba, S.O.[Sileye O.],
Alameda-Pineda, X.[Xavier],
Horaud, R.[Radu],
Tracking Multiple Persons Based on a Variational Bayesian Model,
MOTC16(II: 52-67).
Springer DOI
1611
BibRef
Babaee, M.,
You, Y.,
Rigoll, G.[Gerhard],
Combined segmentation, reconstruction, and tracking of multiple
targets in multi-view video sequences,
CVIU(154), No. 1, 2017, pp. 166-181.
Elsevier DOI
1612
Superpixels
BibRef
Hofmann, M.[Martin],
Haag, M.,
Rigoll, G.[Gerhard],
Unified hierarchical multi-object tracking using global data
association,
PETS13(22-28)
IEEE DOI
1411
data handling
BibRef
Hofmann, M.[Martin],
Wolf, D.[Daniel],
Rigoll, G.[Gerhard],
Hypergraphs for Joint Multi-view Reconstruction and Multi-object
Tracking,
CVPR13(3650-3657)
IEEE DOI
1309
hypergraphs; multi-object; multi-view; surveillance; tracking
BibRef
Hofmann, M.[Martin],
Rigoll, G.[Gerhard],
Huang, T.S.[Thomas S.],
Dense spatio-temporal motion segmentation for tracking multiple
self-occluding people,
SISM10(9-14).
IEEE DOI
1006
BibRef
Wang, B.[Bing],
Wang, G.[Gang],
Chan, K.L.[Kap Luk],
Wang, L.[Li],
Tracklet Association by Online Target-Specific Metric Learning and
Coherent Dynamics Estimation,
PAMI(39), No. 3, March 2017, pp. 589-602.
IEEE DOI
1702
BibRef
Earlier:
Tracklet Association with Online Target-Specific Metric Learning,
CVPR14(1234-1241)
IEEE DOI
1409
Dynamics.
long-term multi-person tracking.
BibRef
Das, A.[Abir],
Panda, R.[Rameswar],
Roy-Chowdhury, A.K.[Amit K.],
Continuous adaptation of multi-camera person identification models
through sparse non-redundant representative selection,
CVIU(156), No. 1, 2017, pp. 66-78.
Elsevier DOI
1702
BibRef
Earlier:
Active image pair selection for continuous person re-identification,
ICIP15(4263-4267)
IEEE DOI
1512
Redundancy reduction.
Active learning; Attributes; Person re-identification
BibRef
Ma, X.L.[Xiao-Long],
Zhu, X.T.[Xia-Tian],
Gong, S.G.[Shao-Gang],
Xie, X.D.[Xu-Dong],
Hu, J.M.[Jian-Ming],
Lam, K.M.[Kin-Man],
Zhong, Y.S.[Yi-Sheng],
Person re-identification by unsupervised video matching,
PR(65), No. 1, 2017, pp. 197-210.
Elsevier DOI
1702
Person re-identification
BibRef
Lan, X.[Xu],
Zhu, X.T.[Xia-Tian],
Gong, S.G.[Shao-Gang],
Unsupervised Cross-Domain Person Re-Identification by Instance and
Distribution Alignment,
PR(124), 2022, pp. 108514.
Elsevier DOI
2203
Unsupervise person re-identification, Domain adaptation
BibRef
Li, Q.[Qilei],
Gong, S.G.[Shao-Gang],
Mitigate Domain Shift by Primary-Auxiliary Objectives Association for
Generalizing Person ReID,
WACV24(393-402)
IEEE DOI
2404
Deep learning, Training, Pedestrians, Limiting, Noise measurement,
Task analysis, Algorithms, Image recognition and understanding,
Video recognition and understanding
BibRef
Li, W.[Wei],
Zhu, X.T.[Xia-Tian],
Gong, S.G.[Shao-Gang],
Scalable Person Re-Identification by Harmonious Attention,
IJCV(128), No. 6, June 2020, pp. 1635-1653.
Springer DOI
2006
BibRef
Li, M.X.[Min-Xian],
Zhu, X.T.[Xia-Tian],
Gong, S.G.[Shao-Gang],
Unsupervised Tracklet Person Re-Identification,
PAMI(42), No. 7, July 2020, pp. 1770-1782.
IEEE DOI
2006
Cameras, Data models, Deep learning, Labeling, Adaptation models,
Unsupervised learning, Training data, Person re-identification,
multi-task deep learning
BibRef
Wu, L.[Lin],
Shen, C.H.[Chun-Hua],
van den Hengel, A.J.[Anton J.],
Deep linear discriminant analysis on fisher networks:
A hybrid architecture for person re-identification,
PR(65), No. 1, 2017, pp. 238-250.
Elsevier DOI
1702
Linear discriminant analysis
BibRef
Zhang, L.[Le],
Shi, Z.L.[Zeng-Lin],
Zhou, J.T.Y.[Joey Tian-Yi],
Cheng, M.M.[Ming-Ming],
Liu, Y.[Yun],
Bian, J.W.[Jia-Wang],
Zeng, Z.[Zeng],
Shen, C.H.[Chun-Hua],
Ordered or Orderless: A Revisit for Video Based Person
Re-Identification,
PAMI(43), No. 4, April 2021, pp. 1460-1466.
IEEE DOI
2103
Cameras, Feature extraction, Task analysis, Visualization,
Video sequences, Aggregates, Bridges, Deep learning,
video based person re-identification
BibRef
Yan, C.[Cheng],
Pang, G.S.[Guan-Song],
Jiao, J.[Jile],
Bai, X.[Xiao],
Feng, X.T.[Xue-Tao],
Shen, C.H.[Chun-Hua],
Occluded Person Re-Identification with Single-scale Global
Representations,
ICCV21(11855-11864)
IEEE DOI
2203
Shape, Computational modeling, Pose estimation, Benchmark testing,
Cameras, Data models, Image and video retrieval,
Representation learning
BibRef
Karanam, S.[Srikrishna],
Li, Y.[Yang],
Radke, R.J.[Richard J.],
Person re-identification with block sparse recovery,
IVC(60), No. 1, 2017, pp. 75-90.
Elsevier DOI
1704
BibRef
Earlier:
Particle dynamics and multi-channel feature dictionaries for robust
visual tracking,
BMVC15(xx-yy).
DOI Link
1601
Person re-identification
BibRef
Karanam, S.[Srikrishna],
Gou, M.R.[Meng-Ran],
Wu, Z.Y.[Zi-Yan],
Rates-Borras, A.[Angels],
Camps, O.I.[Octavia I.],
Radke, R.J.[Richard J.],
A Systematic Evaluation and Benchmark for Person Re-Identification:
Features, Metrics, and Datasets,
PAMI(41), No. 3, March 2019, pp. 523-536.
IEEE DOI
1902
Feature extraction, Measurement, Cameras, Benchmark testing, Probes,
Image color analysis, Histograms, Person re-identification,
benchmark
BibRef
Zheng, M.,
Karanam, S.[Srikrishna],
Radke, R.J.[Richard J.],
RPIfield: A New Dataset for Temporally Evaluating Person
Re-identification,
WiCV18(1974-19742)
IEEE DOI
1812
Cameras, Probes, Benchmark testing, Measurement,
Economic indicators, Legged locomotion
BibRef
Narayan, N.,
Sankaran, N.,
Setlur, S.,
Govindaraju, V.,
Re-identification for Online Person Tracking by Modeling Space-Time
Continuum,
Joint18(1519-151909)
IEEE DOI
1812
Cameras, Feature extraction, Target tracking, Logic gates,
Trajectory, Hidden Markov models
BibRef
Madrigal, F.[Francisco],
Hayet, J.B.[Jean-Bernard],
Rivera, M.[Mariano],
Motion priors for multiple target visual tracking,
MVA(26), No. 2-3, April 2015, pp. 141-160.
WWW Link.
1504
BibRef
Earlier: A1, A3, A2:
Learning and Regularizing Motion Models for Enhancing Particle
Filter-Based Target Tracking,
PSIVT11(II: 287-298).
Springer DOI
1111
See also Evaluation of multiple motion models for multiple pedestrian visual tracking.
BibRef
Madrigal, F.[Francisco],
Hayet, J.B.[Jean-Bernard],
Motion priors based on goals hierarchies in pedestrian tracking
applications,
MVA(28), No. 3-4, May 2017, pp. 341-359.
WWW Link.
1704
BibRef
Earlier:
Evaluation of multiple motion models for multiple pedestrian visual
tracking,
AVSS13(31-36)
IEEE DOI
1311
BibRef
Earlier:
Multiple view, multiple target tracking with principal axis-based data
association,
AVSBS11(185-190).
IEEE DOI
1111
Bayes methods
See also Learning and Regularizing Motion Models for Enhancing Particle Filter-Based Target Tracking.
BibRef
Yin, J.H.[Jia-Hang],
Wu, A.[Ancong],
Zheng, W.S.[Wei-Shi],
Fine-Grained Person Re-identification,
IJCV(128), No. 6, June 2020, pp. 1654-1672.
Springer DOI
2006
BibRef
Peng, Y.X.[Yi-Xing],
Li, Y.Y.X.[Yuan-Yan-Xun],
Zheng, W.S.[Wei-Shi],
Revisiting Person Re-Identification by Camera Selection,
PAMI(46), No. 5, May 2024, pp. 2692-2708.
IEEE DOI Code:
HTML Version.
2404
Cameras, Visualization, Surveillance, Measurement, Topology, Training,
Task analysis, Camera selection, person re-identification, visual surveillance
BibRef
Zhu, X.,
Wu, B.,
Huang, D.,
Zheng, W.S.,
Fast Open-World Person Re-Identification,
IP(27), No. 5, May 2018, pp. 2286-2300.
IEEE DOI
1804
image matching, image representation,
learning (artificial intelligence),
open search space
BibRef
Zhang, Z.Y.[Zong-Yan],
Zhao, C.R.[Cai-Rong],
Miao, D.Q.[Duo-Qian],
Wang, X.[Xuekuan],
Lai, Z.H.[Zhi-Hui],
Yang, J.[Jian],
Saliency-Based Person Re-identification by Probability Histogram,
HIS16(III: 315-329).
Springer DOI
1704
BibRef
Wu, L.[Lin],
Wang, Y.[Yang],
Gao, J.B.[Jun-Bin],
Li, X.[Xue],
Deep adaptive feature embedding with local sample distributions for
person re-identification,
PR(73), No. 1, 2018, pp. 275-288.
Elsevier DOI
1709
BibRef
And:
Expression of concern:
PR(121), 2022, pp. 108133.
Elsevier DOI
2109
Deep feature embedding
BibRef
Wu, L.[Lin],
Wang, Y.[Yang],
Ge, Z.Y.[Zong-Yuan],
Hu, Q.C.[Qi-Chang],
Li, X.[Xue],
Structured deep hashing with convolutional neural networks for fast
person re-identification,
CVIU(167), 2018, pp. 63-73.
Elsevier DOI
1804
Person re-identification, Convolutional neural networks,
Deep hashing, Structured embedding
BibRef
Wu, L.[Lin],
Wang, Y.[Yang],
Li, X.[Xue],
Gao, J.B.[Jun-Bin],
What-and-where to match: Deep spatially multiplicative integration
networks for person re-identification,
PR(76), No. 1, 2018, pp. 727-738.
Elsevier DOI
1801
BibRef
And:
Expression of concern:
PR(121), 2022, pp. 108134.
Elsevier DOI
2109
Multiplicative integration gating
BibRef
Li, S.Q.[Shuang-Qun],
Ma, H.D.[Hua-Dong],
A Siamese inception architecture network for person re-identification,
MVA(28), No. 7, October 2017, pp. 725-736.
WWW Link.
1710
BibRef
Zhao, Z.C.[Zhi-Cheng],
Zhao, B.L.[Bin-Lin],
Su, F.[Fei],
Person re-identification via integrating patch-based metric learning
and local salience learning,
PR(75), No. 1, 2018, pp. 90-98.
Elsevier DOI
1712
Person re-identification
BibRef
Ren, Y.T.[Yu-Tao],
Li, X.L.[Xue-Long],
Lu, X.Q.[Xiao-Qiang],
Feedback mechanism based iterative metric learning for person
re-identification,
PR(75), No. 1, 2018, pp. 99-111.
Elsevier DOI
1712
Person re-identification
BibRef
Sun, B.Y.[Bang-Yong],
Ren, Y.T.[Yu-Tao],
Lu, X.Q.[Xiao-Qiang],
Semisupervised Consistent Projection Metric Learning for Person
Reidentification,
Cyber(52), No. 2, February 2022, pp. 738-747.
IEEE DOI
2202
Measurement, Training, Estimation, Image color analysis,
Training data, Feature extraction, Data models, Discriminative,
person reidentification
BibRef
Jiang, Z.Q.[Zheng-Qiang],
Huynh, D.Q.[Du Q.],
Multiple Pedestrian Tracking From Monocular Videos in an Interacting
Multiple Model Framework,
IP(27), No. 3, March 2018, pp. 1361-1375.
IEEE DOI
1801
Adaptation models, Computational modeling, Histograms,
Image color analysis, Target tracking, Videos, Munkres' algorithm,
visual tracking
BibRef
Jiang, Z.Q.[Zheng-Qiang],
Huynh, D.Q.[Du Q.],
Moran, W.[William],
Challa, S.[Subhash],
Spadaccini, N.[Nick],
Multiple Pedestrian Tracking Using Colour and Motion Models,
DICTA10(328-334).
IEEE DOI
1012
BibRef
Jiang, Z.Q.[Zheng-Qiang],
Huynh, D.Q.[Du Q.],
Moran, W.[William],
Challa, S.[Subhash],
Tracking pedestrians using smoothed colour histograms in an interacting
multiple model framework,
ICIP11(2313-2316).
IEEE DOI
1201
BibRef
Zhou, S.,
Wang, J.,
Shi, R.,
Hou, Q.,
Gong, Y.,
Zheng, N.,
Large Margin Learning in Set-to-Set Similarity Comparison for Person
Reidentification,
MultMed(20), No. 3, March 2018, pp. 593-604.
IEEE DOI
1802
Cameras, Feature extraction, Learning systems, Machine learning,
Measurement, Neural networks, Robustness, Person re-identification,
set to set similarity comparison
BibRef
Lejbřlle, A.R.[Aske R.],
Nasrollahi, K.[Kamal],
Moeslund, T.B.[Thomas B.],
Enhancing person re-identification by late fusion of low-, mid- and
high-level features,
IET-Bio(7), No. 2, March 2018, pp. 125-135.
DOI Link
1802
BibRef
Barbosa, I.B.[Igor Barros],
Cristani, M.[Marco],
Caputo, B.[Barbara],
Rognhaugen, A.[Aleksander],
Theoharis, T.[Theoharis],
Looking beyond appearances: Synthetic training data for deep CNNs in
re-identification,
CVIU(167), 2018, pp. 50-62.
Elsevier DOI
1804
Re-identification, Deep learning, Training set,
Automated training dataset generation, Re-identification photorealistic dataset
BibRef
Tian, Y.[Ying],
Zeng, M.Y.[Ming-Yong],
Lu, A.H.[Ai-Hong],
Gao, B.[Bin],
Luo, Z.K.[Zhang-Kai],
Improving Person Re-Identification by Efficient Pairwise-Specific CRC
Coding in the XQDA Subspace,
IEICE(E101-D), No. 4, April 2018, pp. 1209-1212.
WWW Link.
1804
BibRef
Feng, Z.,
Lai, J.,
Xie, X.,
Learning View-Specific Deep Networks for Person Re-Identification,
IP(27), No. 7, July 2018, pp. 3472-3483.
IEEE DOI
1805
Benchmark testing, Cameras, Computational modeling, Dictionaries,
Feature extraction, Machine learning, Measurement,
view-specific deep networks
BibRef
Chen, Y.[Ying],
Yuan, J.[Jin],
Li, Z.Y.[Zhi-Yong],
Wu, Y.Q.[Yi-Qiang],
Nouioua, M.[Mourad],
Xie, G.Q.[Guo-Qi],
Person re-identification based on re-ranking with expanded
k-reciprocal nearest neighbors,
JVCIR(58), 2019, pp. 486-494.
Elsevier DOI
1901
Person re-identification, Re-ranking,
Expanded k-reciprocal neighbors, Rank list similarity
BibRef
Lv, J.Y.[Jing-Yi],
Li, Z.Y.[Zhi-Yong],
Nai, K.[Ke],
Chen, Y.[Ying],
Yuan, J.[Jin],
Person re-identification with expanded neighborhoods distance
re-ranking,
IVC(95), 2020, pp. 103875.
Elsevier DOI
2004
Person re-identification, Re-ranking,
Expanded neighborhoods distance, Two-level neighborhoods
BibRef
Li, Z.Y.[Zhi-Yong],
Lv, J.Y.[Jing-Yi],
Chen, Y.[Ying],
Yuan, J.[Jin],
Person Re-Identification with Part Prediction Alignment,
CVIU(205), 2021, pp. 103172.
Elsevier DOI
2103
Person re-identification, Part-level feature,
Prediction alignment, Global-local feature
BibRef
Lisanti, G.[Giuseppe],
Martinel, N.[Niki],
Micheloni, C.[Christian],
del Bimbo, A.[Alberto],
Foresti, G.L.[Gian Luca],
From person to group re-identification via unsupervised transfer of
sparse features,
IVC(83-84), 2019, pp. 29-38.
Elsevier DOI
1904
BibRef
Earlier: A1, A2, A4, A5, Only:
Group Re-identification via Unsupervised Transfer of Sparse Features
Encoding,
ICCV17(2468-2477)
IEEE DOI
1802
Group Re-Identification, Dictionary learning, Encoding.
feature extraction, group theory, image coding, image matching,
image representation, image sensors, unsupervised learning, Re,
Visualization
BibRef
Martinel, N.[Niki],
Foresti, G.L.[Gian Luca],
Micheloni, C.[Christian],
Distributed person re-identification through network-wise rank fusion
consensus,
PRL(124), 2019, pp. 63-73.
Elsevier DOI
1906
Re-Identification, Distributed, Camera network, Rank fusion, Consensus
BibRef
Lu, J.,
He, Y.,
Liu, T.,
Chen, X.,
Centralized and Clustered Features for Person Re-Identification,
SPLetters(26), No. 6, June 2019, pp. 933-937.
IEEE DOI
1906
Feature extraction, Training, Reliability,
Signal processing algorithms, Unsupervised learning,
penalty term
BibRef
Lei, J.,
Niu, L.,
Fu, H.,
Peng, B.,
Huang, Q.,
Hou, C.,
Person Re-Identification by Semantic Region Representation and
Topology Constraint,
CirSysVideo(29), No. 8, August 2019, pp. 2453-2466.
IEEE DOI
1908
Measurement, Feature extraction, Semantics, Image color analysis,
Reliability, Probes, Topology, Person re-identification,
topological relationship
BibRef
Wang, Z.,
Jiang, J.,
Yu, Y.,
Satoh, S.,
Incremental Re-Identification by Cross-Direction and Cross-Ranking
Adaption,
MultMed(21), No. 9, September 2019, pp. 2376-2386.
IEEE DOI
1909
History, Cameras, Task analysis, Optimization, Trajectory,
Video surveillance, Image retrieval, Person re-identification, log,
cross-ranking
BibRef
Zeng, Z.,
Wang, Z.,
Wang, Z.,
Zheng, Y.,
Chuang, Y.Y.,
Satoh, S.,
Illumination-Adaptive Person Re-Identification,
MultMed(22), No. 12, December 2020, pp. 3064-3074.
IEEE DOI
2011
Lighting, Cameras, Task analysis, Training, Feature extraction,
Testing, Generators, Person re-identification,
feature disentanglement
BibRef
Baharani, M.[Mohammadreza],
Mohan, S.[Shrey],
Tabkhi, H.[Hamed],
Real-Time Person Re-identification at the Edge:
A Mixed Precision Approach,
ICIAR19(II:27-39).
Springer DOI
1909
BibRef
Jiang, L.,
Liang, C.,
Xu, D.,
Huang, W.,
Multi-Similarity Re-Ranking for Person Re-Identification,
ICIP19(1212-1216)
IEEE DOI
1910
contextual similarity, graph-based similarity, re-ranking,
diffusion, person re-identification
BibRef
Lu, Y.,
Hong, Z.,
Liu, B.,
Li, W.,
Yu, N.,
Dhff: Robust Multi-Scale Person Search by Dynamic Hierarchical
Feature Fusion,
ICIP19(3935-3939)
IEEE DOI
1910
Person Search, Person Re-Identification, Multi-Scale
BibRef
Zhang, Y.S.[Yu-Sheng],
Zhou, Z.H.[Zhi-Heng],
Li, B.[Bo],
Huang, Y.[Yu],
Huang, J.C.[Jun-Chu],
Chen, Z.Q.[Zeng-Qun],
Improving Slice-Based Model for Person Re-ID with Multi-Level
Representation and Triplet-Center Loss,
IEICE(E102-D), No. 11, November 2019, pp. 2230-2237.
WWW Link.
1912
BibRef
Wang, J.B.[Jia-Bao],
Li, Y.[Yang],
Jiao, S.S.[Shan-Shan],
Miao, Z.[Zhuang],
Zhang, R.[Rui],
Grafted network for person re-identification,
SP:IC(80), 2020, pp. 115674.
Elsevier DOI
1912
Person re-identification, Feature representation,
Multi-level feature, Part-based feature, Grafting
BibRef
Kim, K.[Kikyung],
Byeon, M.[Moonsub],
Choi, J.Y.[Jin Young],
Re-ranking with ranking-reflected similarity for person
re-identification,
PRL(128), 2019, pp. 326-332.
Elsevier DOI
1912
Person re-identification, Re-ranking, Similarity metric
BibRef
Cheng, D.[De],
Li, Z.H.[Zhi-Hui],
Gong, Y.H.[Yi-Hong],
Zhang, D.W.[Ding-Wen],
Fusion of Multiple Person Re-id Methods With Model and Data-Aware
Abilities,
Cyber(50), No. 2, February 2020, pp. 561-571.
IEEE DOI
1912
Measurement, Task analysis, Robustness, Learning systems,
Visualization, Benchmark testing, Computational modeling, Fusion,
person reidentification (person re-id)
BibRef
Liu, Z.[Zheng],
Wang, Y.H.[Yun-Hong],
Li, A.[Annan],
Hierarchical Integration of Rich Features for Video-Based Person
Re-Identification,
CirSysVideo(29), No. 12, December 2019, pp. 3646-3659.
IEEE DOI
1912
Feature extraction, Legged locomotion, Semantics,
Optical computing, Optical imaging, Visualization,
multi-model ensemble
BibRef
Cao, J.L.[Jia-Le],
Pang, Y.W.[Yan-Wei],
Han, J.G.[Jun-Gong],
Gao, B.L.[Bo-Lin],
Li, X.L.[Xue-Long],
Taking a Look at Small-Scale Pedestrians and Occluded Pedestrians,
IP(29), 2020, pp. 3143-3152.
IEEE DOI
2002
Small-scale pedestrians, occluded pedestrians,
location bootstrap, semantic transition
BibRef
Zhang, S.[Shun],
Huang, J.B.[Jia-Bin],
Lim, J.W.[Jong-Woo],
Gong, Y.H.[Yi-Hong],
Wang, J.J.[Jin-Jun],
Ahuja, N.[Narendra],
Yang, M.H.[Ming-Hsuan],
Tracking Persons-of-Interest via Unsupervised Representation Adaptation,
IJCV(128), No. 1, January 2020, pp. 96-120.
Springer DOI
2002
BibRef
Earlier: A1, A3, A4, A2, A5, A6, A7:
Tracking Persons-of-Interest via Adaptive Discriminative Features,
ECCV16(V: 415-433).
Springer DOI
1611
BibRef
Wei, L.[Long],
Wei, Z.Y.[Zhen-Yong],
Jin, Z.M.[Zhong-Ming],
Yu, Z.X.[Zheng-Xu],
Huang, J.Q.[Jian-Qiang],
Cai, D.[Deng],
He, X.F.[Xiao-Fei],
Hua, X.S.[Xian-Sheng],
SIF: Self-Inspirited Feature Learning for Person Re-Identification,
IP(29), 2020, pp. 4942-4951.
IEEE DOI
2003
Feature extraction, Training, Optimization, Task analysis,
Computational modeling, Data mining, Semantics, image retrieval.
BibRef
Wan, C.Q.[Chao-Qun],
Wu, Y.[Yue],
Tian, X.M.[Xin-Mei],
Huang, J.Q.[Jian-Qiang],
Hua, X.S.[Xian-Sheng],
Concentrated Local Part Discovery With Fine-Grained Part
Representation for Person Re-Identification,
MultMed(22), No. 6, June 2020, pp. 1605-1618.
IEEE DOI
2005
Feature extraction, Visualization, Cameras,
Convolutional neural networks, Head, Torso, Legged locomotion,
fine-grained representation
BibRef
He, T.Y.[Tian-Yu],
Shen, X.[Xu],
Huang, J.Q.[Jian-Qiang],
Chen, Z.B.[Zhi-Bo],
Hua, X.S.[Xian-Sheng],
Partial Person Re-identification with Part-Part Correspondence
Learning,
CVPR21(9101-9111)
IEEE DOI
2111
Measurement, Degradation, Deep learning,
Image recognition, Benchmark testing, Pattern recognition
BibRef
Lyu, C.J.[Cheng-Jin],
Heyer-Wollenberg, P.[Patrick],
Platisa, L.[Ljiljana],
Goossens, B.[Bart],
Veelaert, P.[Peter],
Philips, W.[Wilfried],
Clip-level Feature Aggregation: A Key Factor for Video-based Person
Re-identification,
ACIVS20(179-191).
Springer DOI
2003
BibRef
Zheng, D.Y.[Ding-Yuan],
Xiao, J.[Jimin],
Huang, K.Z.[Kai-Zhu],
Zhao, Y.[Yao],
Segmentation mask guided end-to-end person search,
SP:IC(86), 2020, pp. 115876.
Elsevier DOI
2006
Person search, Re-identification, Pedestrian detection,
Segmentation masks, Background clutters
BibRef
Lan, L.[Long],
Wang, X.C.[Xin-Chao],
Hua, G.[Gang],
Huang, T.S.[Thomas S.],
Tao, D.C.[Da-Cheng],
Semi-online Multi-people Tracking by Re-identification,
IJCV(128), No. 7, July 2020, pp. 1937-1955.
Springer DOI
2007
BibRef
Wang, K.[Kan],
Wang, P.F.[Peng-Fei],
Ding, C.X.[Chang-Xing],
Tao, D.C.[Da-Cheng],
Batch Coherence-Driven Network for Part-Aware Person
Re-Identification,
IP(30), 2021, pp. 3405-3418.
IEEE DOI
2103
Feature extraction, Training, Coherence, Visualization,
Computational efficiency, Tools, Semantics,
channel attention
BibRef
Jin, X.[Xin],
Lan, C.L.[Cui-Ling],
Zeng, W.J.[Wen-Jun],
Chen, Z.B.[Zhi-Bo],
Global Distance-distributions Separation for Unsupervised Person
Re-identification,
ECCV20(VII:735-751).
Springer DOI
2011
BibRef
Zhang, Z.Z.[Zhi-Zheng],
Lan, C.L.[Cui-Ling],
Zeng, W.J.[Wen-Jun],
Jin, X.[Xin],
Chen, Z.B.[Zhi-Bo],
Relation-Aware Global Attention for Person Re-Identification,
CVPR20(3183-3192)
IEEE DOI
2008
Semantics, Convolutional codes, Feature extraction,
Benchmark testing, Aggregates, Stacking, Task analysis
BibRef
Zhou, J.M.[Jie-Ming],
Roy, S.K.[Soumava Kumar],
Fang, P.F.[Peng-Fei],
Harandi, M.[Mehrtash],
Petersson, L.[Lars],
Cross-Correlated Attention Networks for Person Re-Identification,
IVC(100), 2020, pp. 103931.
Elsevier DOI
2008
Attention, Feature extraction, Cross correlation,
Person Re-Identification, Surveillance
BibRef
Fang, P.F.[Peng-Fei],
Ji, P.[Pan],
Petersson, L.[Lars],
Harandi, M.[Mehrtash],
Set Augmented Triplet Loss for Video Person Re-Identification,
WACV21(464-473)
IEEE DOI
2106
Measurement, Prototypes,
Benchmark testing, Task analysis, Standards
BibRef
Xu, F.R.[Fu-Rong],
Ma, B.P.[Bing-Peng],
Chang, H.[Hong],
Shan, S.G.[Shi-Guang],
Isosceles Constraints for Person Re-Identification,
IP(29), 2020, pp. 8930-8943.
IEEE DOI
2009
Feature extraction, Training, Measurement, Robustness,
Machine learning, Probes, Task analysis, Person re-identification,
quadruplet
BibRef
Xu, F.[Furong],
Ma, B.P.[Bing-Peng],
Chang, H.[Hong],
Shan, S.G.[Shi-Guang],
PRDP: Person Reidentification With Dirty and Poor Data,
Cyber(52), No. 10, October 2022, pp. 11014-11026.
IEEE DOI
2209
Training, Noise measurement, Data models, Task analysis,
Training data, Predictive models, Heuristic algorithms, Dirty, poor
BibRef
Hou, R.B.[Rui-Bing],
Chang, H.[Hong],
Ma, B.P.[Bing-Peng],
Huang, R.[Rui],
Shan, S.G.[Shi-Guang],
BiCnet-TKS: Learning Efficient Spatial-Temporal Representation for
Video Person Re-Identification,
CVPR21(2014-2023)
IEEE DOI
2111
Visualization, Codes, Computational modeling,
Benchmark testing, Pattern recognition, Computational efficiency
BibRef
Hou, R.B.[Rui-Bing],
Chang, H.[Hong],
Ma, B.P.[Bing-Peng],
Shan, S.G.[Shi-Guang],
Chen, X.L.[Xi-Lin],
Temporal Complementary Learning for Video Person Re-identification,
ECCV20(XXV:388-405).
Springer DOI
2011
BibRef
Deng, X.S.[Xue-Song],
Ma, B.P.[Bing-Peng],
Chang, H.[Hong],
Shan, S.G.[Shi-Guang],
Chen, X.L.[Xi-Lin],
Deep Second-Order Siamese Network for Pedestrian Re-identification,
ACCV16(II: 321-337).
Springer DOI
1704
BibRef
Xu, X.,
Ma, B.P.[Bing-Peng],
Chang, H.[Hong],
Chen, X.L.[Xi-Lin],
Siamese recurrent architecture for visual tracking,
ICIP17(1152-1156)
IEEE DOI
1803
Recurrent neural networks,
Target tracking, Training, Videos, Visualization,
visual tracking
BibRef
Song, W.[Wanru],
Chen, C.H.[Chang-Hong],
Zhao, Q.Q.[Qing-Qing],
Liu, F.[Feng],
Spatial-temporal representation for video re-identification via key
images,
IET-CV(14), No. 6, September 2020, pp. 399-406.
DOI Link
2010
BibRef
Sumari, F.O.[Felix O.],
Machaca, L.[Luigy],
Huaman, J.[Jose],
Clua, E.W.G.[Esteban W.G.],
Guérin, J.[Joris],
Towards practical implementations of person re-identification from
full video frames,
PRL(138), 2020, pp. 513-519.
Elsevier DOI
1806
Security application, Person re-identification, Pedestrian detection
BibRef
Behera, N.K.S.[Nayan Kumar Subhashis],
Sa, P.K.[Pankaj Kumar],
Bakshi, S.[Sambit],
Person re-identification for smart cities:
State-of-the-art and the path ahead,
PRL(138), 2020, pp. 282-289.
Elsevier DOI
1806
Person re-identification, Automated surveillance,
People analysis, Smart city applications
BibRef
Zhang, J.F.[Jian-Fu],
Niu, L.[Li],
Zhang, L.Q.[Li-Qing],
Person Re-Identification With Reinforced Attribute Attention
Selection,
IP(30), 2021, pp. 603-616.
IEEE DOI
2012
Annotations, Task analysis, Noise measurement, Training,
Image color analysis, Cameras, Feature extraction,
sequential decision making
BibRef
Yao, H.T.[Han-Tao],
Xu, C.S.[Chang-Sheng],
Joint Person Objectness and Repulsion for Person Search,
IP(30), 2021, pp. 685-696.
IEEE DOI
2012
Probes, Search problems, Detectors, Proposals, Visualization,
Noise measurement, Transforms, Detection-Matching person search,
person re-identification
BibRef
Wang, W.,
Pei, W.,
Cao, Q.,
Liu, S.,
Lu, G.,
Tai, Y.W.,
Push for Center Learning via Orthogonalization and Subspace Masking
for Person Re-Identification,
IP(30), 2021, pp. 907-920.
IEEE DOI
2012
Correlation, Task analysis, Optimization, Training, Learning systems,
Semantics, Lighting, Person re-identification,
max pooling
BibRef
Hu, X.Q.[Xiao-Qiang],
Wei, D.[Dan],
Wang, Z.Y.[Zi-Yang],
Shen, J.L.[Jiang-Lin],
Ren, H.J.[Hong-Juan],
Hypergraph video pedestrian re-identification based on posture
structure relationship and action constraints,
PR(111), 2021, pp. 107688.
Elsevier DOI
2012
Pedestrian re-identification, Structural relationship,
Action hypergraph, Saliency score
BibRef
Gong, Y.X.[Yu-Xiu],
Wang, R.G.[Rong-Gui],
Yang, J.[Juan],
Xue, L.X.[Li-Xia],
Hu, M.[Min],
Person Re-identification with Global-Local Background Bias Net,
JVCIR(74), 2021, pp. 102961.
Elsevier DOI
2101
Person Re-identification, Body misalignment,
Foreground features, Background information
BibRef
Yuan, J.[Jing],
Zhang, S.M.[Sheng-Ming],
Sun, Q.X.[Qin-Xuan],
Liu, G.D.[Gang-Dun],
Cai, J.X.[Jing-Xin],
Laser-Based Intersection-Aware Human Following With a Mobile Robot in
Indoor Environments,
SMCS(51), No. 1, January 2021, pp. 354-369.
IEEE DOI
2101
Legged locomotion, Lasers, Target tracking, Robot sensing systems,
Indoor environments, Human following, indoor environments, mobile robot
BibRef
Lin, W.,
Li, Y.,
Xiao, H.,
See, J.,
Zou, J.,
Xiong, H.,
Wang, J.,
Mei, T.,
Group Reidentification with Multigrained Matching and Integration,
Cyber(51), No. 3, March 2021, pp. 1478-1492.
IEEE DOI
2102
Layout, Feature extraction, Cameras, Task analysis, Reliability,
Probes, Heuristic algorithms, Group reidentification (Re-ID),
Re-ID
BibRef
Li, P.,
Pan, P.,
Liu, P.,
Xu, M.,
Yang, Y.,
Hierarchical Temporal Modeling With Mutual Distance Matching for
Video Based Person Re-Identification,
CirSysVideo(31), No. 2, February 2021, pp. 503-511.
IEEE DOI
2102
Feature extraction, Video sequences, Task analysis, Convolution,
Probes, Distance measurement, Training, probe-gallery mutual distance
BibRef
Yaghoubi, E.[Ehsan],
Kumar, A.[Aruna],
Proença, H.[Hugo],
SSS-PR: A short survey of surveys in person re-identification,
PRL(143), 2021, pp. 50-57.
Elsevier DOI
2102
Survey, Re-Identification. Person re-identification, Privacy and security, Visual surveillance
BibRef
Yaghoubi, E.[Ehsan],
Borza, D.[Diana],
Aruna Kumar, S.V.,
Proença, H.[Hugo],
Person re-identification: Implicitly defining the receptive fields of
deep learning classification frameworks,
PRL(145), 2021, pp. 23-29.
Elsevier DOI
2104
Person re-identification, Data augmentation,
Explicit attention mechanism, Visual surveillance
BibRef
Han, K.[Ke],
Huang, Y.[Yan],
Song, C.F.[Chun-Feng],
Wang, L.[Liang],
Tan, T.N.[Tie-Niu],
Adaptive super-resolution for person re-identification with
low-resolution images,
PR(114), 2021, pp. 107682.
Elsevier DOI
2103
Person re-identification, Super-resolution, Body regions,
Adaptive feature integration
BibRef
Han, K.[Ke],
Huang, Y.[Yan],
Chen, Z.[Zerui],
Wang, L.[Liang],
Tan, T.N.[Tie-Niu],
Prediction and Recovery for Adaptive Low-resolution Person
Re-identification,
ECCV20(XXVI:193-209).
Springer DOI
2011
BibRef
Wang, Y.J.[Yong-Jie],
Zhang, W.[Wei],
Liu, Y.Y.[Yan-Yan],
Multi-Scale Feature Fusion Network for Person Re-Identification,
IET-IPR(14), No. 17, 24 December 2020, pp. 4614-4620.
DOI Link
2104
BibRef
Wang, Y.J.[Yong-Jie],
Zhang, W.[Wei],
Huang, D.X.[Dong-Xiao],
Liu, Y.Y.[Yan-Yan],
Multi-level feature fusion and multi-loss learning for person
Re-Identification,
SP:IC(94), 2021, pp. 116197.
Elsevier DOI
2104
Self-attention module, Relative weight, Multi-loss learning
BibRef
Sun, J.[Jia],
Li, Y.F.[Yan-Feng],
Chen, H.J.[Hou-Jin],
Zhang, B.[Bin],
Zhu, J.L.[Jin-Lei],
MEMF: Multi-level-attention embedding and multi-layer-feature fusion
model for person re-identification,
PR(116), 2021, pp. 107937.
Elsevier DOI
2106
Person re-identification, Feature expression, Convolutional neural network
BibRef
Wang, H.X.[Hong-Xia],
Chen, X.[Xiang],
Liu, C.[Chun],
Pose-guided part matching network via shrinking and reweighting for
occluded person re-identification,
IVC(111), 2021, pp. 104186.
Elsevier DOI
2106
Person re-identification, Pose estimation, Graph matching, Soft thresholding
BibRef
Zhang, X.K.[Xiao-Kang],
Yan, Y.[Yan],
Xue, J.H.[Jing-Hao],
Hua, Y.[Yang],
Wang, H.Z.[Han-Zi],
Semantic-Aware Occlusion-Robust Network for Occluded Person
Re-Identification,
CirSysVideo(31), No. 7, July 2021, pp. 2764-2778.
IEEE DOI
2107
Feature extraction, Semantics, Task analysis, Pose estimation,
Image segmentation, Clutter, Cameras, Person re-identification,
multi-task learning
BibRef
Zhu, J.[Ji],
Yang, H.[Hua],
Lin, W.Y.[Wei-Yao],
Liu, N.[Nian],
Wang, J.[Jia],
Zhang, W.J.[Wen-Jun],
Group Re-Identification With Group Context Graph Neural Networks,
MultMed(23), 2021, pp. 2614-2626.
IEEE DOI
2109
Layout, Feature extraction, Cameras, Task analysis, Kernel, Training,
Measurement, Group re-identification, spatial K-NN graph,
group context graph neural network
BibRef
Behera, N.K.S.[Nayan Kumar Subhashis],
Sa, P.K.[Pankaj Kumar],
Bakshi, S.[Sambit],
Padhy, R.P.[Ram Prasad],
Person re-identification: A taxonomic survey and the path ahead,
IVC(122), 2022, pp. 104432.
Elsevier DOI
2205
Survey, Re-Identification. Person re-identification, Visual surveillance, Computer vision
BibRef
Cheng, D.[De],
Zhou, J.Y.[Jing-Yu],
Wang, N.N.[Nan-Nan],
Gao, X.B.[Xin-Bo],
Hybrid Dynamic Contrast and Probability Distillation for Unsupervised
Person Re-Id,
IP(31), 2022, pp. 3334-3346.
IEEE DOI
2205
Training, Task analysis, Feature extraction, Clustering algorithms,
Cameras, Training data, Heuristic algorithms, Unsupervised,
probability distillation
BibRef
Yang, X.[Xi],
Liu, H.[Huanling],
Wang, N.N.[Nan-Nan],
Gao, X.B.[Xin-Bo],
Image-Level Adaptive Adversarial Ranking for Person Re-Identification,
IP(33), 2024, pp. 5172-5182.
IEEE DOI
2410
Perturbation methods, Pedestrians, Security, Measurement, Feature extraction,
Adaptation models, Robustness, adaptive weight confusion ranking (AWCR)
BibRef
Pascotti-Valem, L.[Lucas],
Guimarăes-Pedronette, D.C.[Daniel Carlos],
Person Re-ID through unsupervised hypergraph rank selection and
fusion,
IVC(123), 2022, pp. 104473.
Elsevier DOI
2206
Person Re-ID, Unsupervised, Hypergraph, Rank, Selection, Fusion
BibRef
Zhang, G.Q.[Guo-Qing],
Chen, C.[Chao],
Chen, Y.H.[Yu-Hao],
Zhang, H.W.[Hong-Wei],
Zheng, Y.H.[Yu-Hui],
Fine-grained-based multi-feature fusion for occluded person
re-identification,
JVCIR(87), 2022, pp. 103581.
Elsevier DOI
2208
Occluded person re-identification, Multi-granularity feature, Feature fusion
BibRef
Yang, L.[Lu],
Wang, Y.L.[Yun-Long],
Liu, L.Q.[Ling-Qiao],
Wang, P.[Peng],
Zhang, Y.N.[Yan-Ning],
Center Prediction Loss for Re-identification,
PR(132), 2022, pp. 108949.
Elsevier DOI
2209
Person re-identification, Loss, Deep metric learning
BibRef
Zhang, Z.Z.[Zhi-Zheng],
Lan, C.L.[Cui-Ling],
Zeng, W.J.[Wen-Jun],
Chen, Z.B.[Zhi-Bo],
Chang, S.F.[Shih-Fu],
Beyond Triplet Loss: Meta Prototypical N-Tuple Loss for Person
Re-identification,
MultMed(24), 2022, pp. 4158-4169.
IEEE DOI
2209
Optimization, Training, Measurement, Toy manufacturing industry,
Deep learning, Convolutional neural networks, Benchmark testing,
metric learning
BibRef
Yu, F.[Fufu],
Jiang, X.Y.[Xin-Yang],
Gong, Y.F.[Yi-Fei],
Zheng, W.S.[Wei-Shi],
Zheng, F.[Feng],
Sun, X.[Xing],
Conditional Feature Embedding by Visual Clue Correspondence Graph for
Person Re-Identification,
IP(31), 2022, pp. 6188-6199.
IEEE DOI
2210
Feature extraction, Visualization, Transformers, Semantics, Fuses,
Convolution, Sun, Person re-identification, dynamically adjust,
discrepancy-based GCN
BibRef
Yang, J.R.[Jin-Rui],
Zhang, J.W.[Jia-Wei],
Yu, F.[Fufu],
Jiang, X.Y.[Xin-Yang],
Zhang, M.D.[Meng-Dan],
Sun, X.[Xing],
Chen, Y.C.[Ying-Cong],
Zheng, W.S.[Wei-Shi],
Learning to Know Where to See: A Visibility-Aware Approach for
Occluded Person Re-identification,
ICCV21(11865-11874)
IEEE DOI
2203
Estimation error, Annotations, Computational modeling, Robustness,
Noise measurement, Image and video retrieval, Recognition and classification
BibRef
Yu, Z.X.[Zheng-Xu],
Zhao, Y.L.[Yi-Lun],
Hong, B.[Bin],
Jin, Z.M.[Zhong-Ming],
Huang, J.Q.[Jian-Qiang],
Cai, D.[Deng],
Hua, X.S.[Xian-Sheng],
Apparel-Invariant Feature Learning for Person Re-Identification,
MultMed(24), 2022, pp. 4482-4492.
IEEE DOI
2212
Proposals, Generators, Image color analysis,
Generative adversarial networks, Cameras, Visualization, transfer learning
BibRef
Lin, X.[Xin],
Zhu, L.[Li],
Yang, S.Y.[Shu-Yu],
Wang, Y.X.[Ya-Xiong],
Diff attention: A novel attention scheme for person re-identification,
CVIU(228), 2023, pp. 103623.
Elsevier DOI
2302
Person re-identification, Diff attention, Distance function, Deep learning
BibRef
Xu, R.[Ruyu],
Zheng, Y.Y.[Yue-Yang],
Wang, X.M.[Xiao-Ming],
Li, D.[Dong],
Person re-identification based on improved attention mechanism and
global pooling method,
JVCIR(94), 2023, pp. 103849.
Elsevier DOI
2306
Person re-identification, Feature representation,
Attention mechanism, Global pooling, Spatial transform
BibRef
Zahra, A.[Asmat],
Perwaiz, N.[Nazia],
Shahzad, M.[Muhammad],
Fraz, M.M.[Muhammad Moazam],
Person re-identification: A retrospective on domain specific open
challenges and future trends,
PR(142), 2023, pp. 109669.
Elsevier DOI
2307
Survey, Re-Identification. Person re-Identification, Literature survey, Deep learning,
Open challenges, Specific application-driven
BibRef
Liu, Y.F.[Yi-Fei],
Liang, Y.L.[Ya-Ling],
Wang, P.F.[Peng-Fei],
Chen, Z.H.[Zi-Heng],
Ding, C.X.[Chang-Xing],
GlobalAP: Global average precision optimization for person
re-identification,
PR(142), 2023, pp. 109682.
Elsevier DOI
2307
Person re-identification, Image retrieval, Average precision
BibRef
Seong, J.[Jin],
Online and real-time mask-guided multi-person tracking and
segmentation,
PRL(172), 2023, pp. 144-150.
Elsevier DOI
2309
Multi-object tracking, Multi-object tracking and segmentation,
Deep learning, Autonomous driving, Re-identification, Real-time tracking
BibRef
Yan, S.L.[Shuang-Lin],
Dong, N.[Neng],
Zhang, L.Y.[Li-Yan],
Tang, J.H.[Jin-Hui],
CLIP-Driven Fine-Grained Text-Image Person Re-Identification,
IP(32), 2023, pp. 6032-6046.
IEEE DOI
2311
BibRef
Wang, X.[Xuan],
Sun, Z.J.[Zhao-Jie],
Chehri, A.[Abdellah],
Jeon, G.G.[Gwang-Gil],
Song, Y.C.[Yong-Chao],
A Novel Attention-Driven Framework for Unsupervised Pedestrian
Re-identification with Clustering Optimization,
PR(146), 2024, pp. 110045.
Elsevier DOI
2311
Pattern recognition, Unsupervised pedestrian re-identification,
Improve pseudo-labels
BibRef
Zhong, X.[Xian],
Han, X.[Xiyu],
Jia, X.M.[Xue-Mei],
Huang, W.X.[Wen-Xin],
Liu, W.X.[Wen-Xuan],
Su, S.[Shuaipeng],
Yu, X.H.[Xiao-Han],
Ye, M.[Mang],
ICLR: Instance Credibility-Based Label Refinement for label noisy
person re-identification,
PR(148), 2024, pp. 110168.
Elsevier DOI Code:
WWW Link.
2402
Person re-identification, Label noise,
Label-Incredibility Optimization, Incredible Instance Re-Weight
BibRef
Li, Y.P.[Yan-Ping],
Miao, D.Q.[Duo-Qian],
Zhang, H.Y.[Hong-Yun],
Zhou, J.[Jie],
Zhao, C.R.[Cai-Rong],
Multi-granularity Cross Transformer Network for person
re-identification,
PR(150), 2024, pp. 110362.
Elsevier DOI
2403
Person re-identification, Cross transformer, Multi-granularity
BibRef
Peng, J.J.[Jin-Jia],
Yu, J.[Jiazuo],
Wang, C.J.[Cheng-Jun],
Wang, H.[Huibing],
Fu, X.P.[Xian-Ping],
Adapt only once: Fast unsupervised person re-identification via
relevance-aware guidance,
PR(150), 2024, pp. 110360.
Elsevier DOI Code:
WWW Link.
2403
Prototype-guided label learning, Label-flexible training,
Fast person re-identification
BibRef
Deng, T.H.[Teng-Hao],
Sun, Y.[Yan],
Recent advances in deterministic human motion prediction: A review,
IVC(143), 2024, pp. 104926.
Elsevier DOI
2403
Survey, Human Motion. Survey, Human motion prediction, Deep learning
BibRef
Zhang, Q.[Quan],
Lai, J.H.[Jian-Huang],
Feng, Z.X.[Zhan-Xiang],
Xie, X.H.[Xiao-Hua],
Uncertainty Modeling for Group Re-Identification,
IJCV(132), No. 8, August 2024, pp. 3046-3066.
Springer DOI
2408
BibRef
Sun, H.M.[Hai-Ming],
Ma, S.W.[Shi-Wei],
Pro-ReID: Producing reliable pseudo labels for unsupervised person
re-identification,
IVC(150), 2024, pp. 105244.
Elsevier DOI
2409
Unsupervised representation learning, Reidentification,
Contrastive learning, Noise labels learning
BibRef
Kansal, K.[Kajal],
Wong, Y.K.[Yong-Kang],
Kankanhalli, M.[Mohan],
Privacy-Enhancing Person Re-identification Framework:
A Dual-Stage Approach,
WACV24(8528-8537)
IEEE DOI
2404
Training, Measurement, Privacy, Differential privacy,
Computational modeling, Gaussian noise, Regulation, Applications,
Image recognition and understanding
BibRef
Maggiolino, G.[Gerard],
Ahmad, A.[Adnan],
Cao, J.[Jinkun],
Kitani, K.[Kris],
Deep OC-Sort: Multi-Pedestrian Tracking by Adaptive Re-Identification,
ICIP23(3025-3029)
IEEE DOI Code:
WWW Link.
2312
BibRef
Chen, C.[Cuiqun],
Ye, M.[Mang],
Jiang, D.[Ding],
Towards Modality-Agnostic Person Re-identification with Descriptive
Query,
CVPR23(15128-15137)
IEEE DOI
2309
BibRef
Lee, H.[Hyungtae],
Eum, S.[Sungmin],
Kwon, H.S.[Hee-Sung],
Negative Samples are at Large: Leveraging Hard-Distance Elastic Loss
for Re-identification,
ECCV22(XXIV:604-620).
Springer DOI
2211
BibRef
Song, W.F.[Wen-Feng],
Zhang, X.Y.[Xin-Yu],
Ye, Y.[Ying],
Gao, Y.[Yang],
Guo, Y.F.[Yi-Fan],
Hao, A.[Aimin],
Hou, X.[Xia],
Person Re-Identification in Panoramic Views Based on Bayesian
Transformers,
ICIP22(3778-3782)
IEEE DOI
2211
Semantics, Collaboration, Benchmark testing, Transformers,
Feature extraction, Distortion, Cameras, Bayesian Prior,
Panoramic View Image
BibRef
Zhang, X.Y.[Xin-Yu],
Li, D.D.[Dong-Dong],
Wang, Z.G.[Zhi-Gang],
Wang, J.[Jian],
Ding, E.[Errui],
Shi, J.Q.F.[Javen Qin-Feng],
Zhang, Z.X.[Zhao-Xiang],
Wang, J.D.[Jing-Dong],
Implicit Sample Extension for Unsupervised Person Re-Identification,
CVPR22(7359-7368)
IEEE DOI
2210
Training, Interpolation, Codes, Aerospace electronics,
Pattern recognition, Noise measurement, Recognition: detection
BibRef
Yan, C.[Cheng],
Pang, G.S.[Guan-Song],
Wang, L.[Lei],
Jiao, J.[Jile],
Feng, X.T.[Xue-Tao],
Shen, C.H.[Chun-Hua],
Li, J.J.[Jing-Jing],
BV-Person: A Large-scale Dataset for Bird-view Person
Re-identification,
ICCV21(10923-10932)
IEEE DOI
2203
Dataset, Re-Identification. Computational modeling, Benchmark testing, Cameras,
Video surveillance, Search problems, Birds,
Image and video retrieval
BibRef
Zheng, Y.[Yu],
Velipasalar, S.[Senem],
Part-Based Feature Squeezing To Detect Adversarial Examples in Person
Re-Identification Networks,
ICIP21(844-848)
IEEE DOI
2201
Deep learning, Image segmentation, Perturbation methods,
Object detection, Feature extraction, Person re-identification, DNN
BibRef
Coleiro, A.[Andre],
Scerri, D.[Daren],
Security Automation Through a Multi-processing Real-Time System for the
Re-Identification of Persons,
ISVC21(II:141-153).
Springer DOI
2112
BibRef
Gilroy, S.[Shane],
Glavin, M.[Martin],
Jones, E.[Edward],
Mullins, D.[Darragh],
Pedestrian Occlusion Level Classification using Keypoint Detection
and 2D Body Surface Area Estimation,
OVIS21(3826-3832)
IEEE DOI
2112
Annotations, Image edge detection, Semantics, Estimation, Benchmark testing
BibRef
Pu, N.[Nan],
Chen, W.[Wei],
Liu, Y.[Yu],
Bakker, E.M.[Erwin M.],
Lew, M.S.[Michael S.],
Lifelong Person Re-Identification via Adaptive Knowledge Accumulation,
CVPR21(7897-7906)
IEEE DOI
2111
Training, Visualization, Adaptation models, Codes,
Cognitive processes, Knowledge representation
BibRef
Choi, S.[Seokeon],
Kim, T.[Taekyung],
Jeong, M.[Minki],
Park, H.[Hyoungseob],
Kim, C.[Changick],
Meta Batch-Instance Normalization for Generalizable Person
Re-Identification,
CVPR21(3424-3434)
IEEE DOI
2111
Codes, Computational modeling, Pipelines,
Benchmark testing, Data models, Pattern recognition
BibRef
Zhang, X.[Xiao],
Ge, Y.X.[Yi-Xiao],
Qiao, Y.[Yu],
Li, H.S.[Hong-Sheng],
Refining Pseudo Labels with Clustering Consensus over Generations for
Unsupervised Object Re-identification,
CVPR21(3435-3444)
IEEE DOI
2111
Training, Annotations, Refining,
Pattern recognition, Noise measurement
BibRef
Zhang, A.[Anguo],
Gao, Y.M.[Yue-Ming],
Niu, Y.Z.[Yu-Zhen],
Liu, W.X.[Wen-Xi],
Zhou, Y.C.[Yong-Cheng],
Coarse-to-Fine Person Re-Identification with Auxiliary-Domain
Classification and Second-Order Information Bottleneck,
CVPR21(598-608)
IEEE DOI
2111
Image coding, Surveillance, Redundancy,
Neural networks, Feature extraction, Cameras
BibRef
Kraus, M.[Maximilian],
Azimi, S.M.[Seyed Majid],
Ercelik, E.[Emec],
Bahmanyar, R.[Reza],
Reinartz, P.[Peter],
Knoll, A.[Alois],
AerialMPTNet: Multi-Pedestrian Tracking in Aerial Imagery Using
Temporal and Graphical Features,
ICPR21(2454-2461)
IEEE DOI
2105
Image quality, Tracking, Atmospheric modeling, Neural networks,
Predictive models, Prediction algorithms, Trajectory
BibRef
Zheng, C.[Chong],
Wei, P.[Ping],
Zheng, N.N.[Nan-Ning],
A Duplex Spatiotemporal Filtering Network for Video-based Person
Re-identification,
ICPR21(7551-7557)
IEEE DOI
2105
Surveillance, Video sequences, Semantics, Benchmark testing,
Information filters, Feature extraction, Spatiotemporal phenomena
BibRef
Zhao, C.[Chao],
Zhang, Z.Y.[Zhen-Yu],
Yan, J.[Jian],
Yan, Y.[Yan],
Decoupled Self-attention Module for Person Re-identification,
ICPR21(7617-7624)
IEEE DOI
2105
Correlation, Semantics, Lighting, Information filters, Cameras,
Robustness
BibRef
Ni, X.Y.[Xing-Yang],
Fang, L.[Liang],
Huttunen, H.[Heikki],
Adaptive L2 Regularization in Person Re-Identification,
ICPR21(9601-9607)
IEEE DOI
2105
Training, Backpropagation, Adaptation models, Adaptive systems,
Neural networks, Image retrieval, Object detection
BibRef
Ai, M.J.[Ming-Jing],
Shan, G.Z.[Guo-Zhi],
Liu, B.[Bo],
Liu, T.Y.[Tian-Yang],
Rethinking ReID: Multi-Feature Fusion Person Re-identification Based
on Orientation Constraints,
ICPR21(1904-1911)
IEEE DOI
2105
Training, Computational modeling, Focusing,
Pattern recognition, Security, Videos, person re-identification,
orientation classifier
BibRef
Ding, W.J.[Wen-Jie],
Wei, X.[Xing],
Ji, R.R.[Rong-Rong],
Hong, X.P.[Xiao-Peng],
Gong, Y.H.[Yi-Hong],
Polynomial Universal Adversarial Perturbations for Person
Re-Identification,
ICPR21(1144-1151)
IEEE DOI
2105
Correlation coefficient, Additives, Perturbation methods,
Modulation, Pattern recognition
BibRef
Han, C.,
Gao, C.,
Sang, N.,
Keypoint-Based Feature Matching For Partial Person Re-Identification,
ICIP20(226-230)
IEEE DOI
2011
Feature extraction, Cameras, Task analysis, Pose estimation,
Training, Pipelines, Generative adversarial networks, Partial,
Re-ID
BibRef
Liu, M.,
Dai, Y.,
Wu, S.,
Bai, Y.,
Duan, L.Y.,
Extending Hashing Towards Fast Re-Identification,
ICIP20(1551-1555)
IEEE DOI
2011
Training, Binary codes, Protocols, Convergence, Visualization,
Quantization (signal), Entropy, Hashing, Re-Identification, Pooling
BibRef
Wang, G.[Guan'an],
Gong, S.G.[Shao-Gang],
Cheng, J.[Jian],
Hou, Z.G.[Zeng-Guang],
Faster Person Re-identification,
ECCV20(VIII:275-292).
Springer DOI
2011
BibRef
Zhao, S.Z.[Shi-Zhen],
Gao, C.X.[Chang-Xin],
Zhang, J.[Jun],
Cheng, H.[Hao],
Han, C.[Chuchu],
Jiang, X.Y.[Xin-Yang],
Guo, X.W.[Xiao-Wei],
Zheng, W.S.[Wei-Shi],
Sang, N.[Nong],
Sun, X.[Xing],
Do Not Disturb Me: Person Re-identification Under the Interference of
Other Pedestrians,
ECCV20(VI:647-663).
Springer DOI
2011
BibRef
Song, X.L.[Xiao-Lin],
Zhao, K.[Kaili],
Chu, W.S.[Wen-Sheng],
Zhang, H.G.[Hong-Gang],
Guo, J.[Jun],
Progressive Refinement Network for Occluded Pedestrian Detection,
ECCV20(XXIII:32-48).
Springer DOI
2011
BibRef
Li, M.,
Xu, H.,
Wang, J.,
Li, W.,
Sun, Y.,
Temporal Aggregation with Clip-level Attention for Video-based Person
Re-identification,
WACV20(3365-3373)
IEEE DOI
2006
Feature extraction, Training, Task analysis, Measurement, Robustness,
Computational modeling, Aggregates
BibRef
Lu, R.,
Ma, H.,
Occluded Pedestrian Detection with Visible IoU and Box Sign Predictor,
ICIP19(1640-1644)
IEEE DOI
1910
Occluded pedestrian detection, visible ratio, box sign predictor,
localization accuracy
BibRef
Ghorbel, M.[Mahmoud],
Ammar, S.[Sourour],
Kessentini, Y.[Yousri],
Jmaiel, M.[Mohamed],
Improving Person Re-identification by Background Subtraction Using
Two-Stream Convolutional Networks,
ICIAR19(I:345-356).
Springer DOI
1909
BibRef
Zamprogno, M.[Marco],
Passon, M.[Marco],
Martinel, N.[Niki],
Serra, G.[Giuseppe],
Lancioni, G.[Giuseppe],
Micheloni, C.[Christian],
Tasso, C.[Carlo],
Foresti, G.L.[Gian Luca],
Video-Based Convolutional Attention for Person Re-Identification,
CIAP19(I:3-14).
Springer DOI
1909
BibRef
Li, W.B.[Wen-Bo],
Chen, Z.[Ze],
Fu, Z.Y.[Zhen-Yong],
Lu, H.T.[Hong-Tao],
Multilevel Collaborative Attention Network for Person Search,
ACCV18(I:467-482).
Springer DOI
1906
pedestrian detection and person re-identification simultaneously.
BibRef
Ristani, E.,
Tomasi, C.,
Features for Multi-target Multi-camera Tracking and Re-identification,
CVPR18(6036-6046)
IEEE DOI
1812
Correlation, Cameras, Trajectory, Feature extraction, Training,
Detectors, Benchmark testing
BibRef
Babaee, M.,
Li, Z.,
Rigoll, G.,
Occlusion Handling in Tracking Multiple People Using RNN,
ICIP18(2715-2719)
IEEE DOI
1809
Target tracking, Training, Legged locomotion, Trajectory,
Video sequences, Tracking, Motion, Occlusion, RNN, Deep learning
BibRef
Huang, Z.X.[Zeng-Xi],
Feng, Z.H.[Zhen-Hua],
Yan, F.[Fei],
Kittler, J.V.[Josef V.],
Wu, X.J.[Xiao-Jun],
Robust Pedestrian Detection for Semi-automatic Construction of a
Crowded Person Re-Identification Dataset,
AMDO18(63-72).
Springer DOI
1807
BibRef
Zhang, P.[Peng],
Wu, Q.[Qiang],
Xu, J.S.[Jing-Song],
Zhang, J.[Jian],
Long-Term Person Re-identification Using True Motion from Videos,
WACV18(494-502)
IEEE DOI
1806
feature extraction, image coding, image matching,
image motion analysis, image representation, image sequences,
Videos
BibRef
Wojke, N.,
Bewley, A.,
Deep Cosine Metric Learning for Person Re-identification,
WACV18(748-756)
IEEE DOI
1806
feature extraction, image classification,
learning (artificial intelligence), query processing, vectors,
Trajectory
BibRef
Ustinova, E.,
Ganin, Y.,
Lempitsky, V.,
Multi-Region bilinear convolutional neural networks for person
re-identification,
AVSS17(1-6)
IEEE DOI
1806
convolution, image classification, image matching, neural nets,
bilinear pooling, bilinear-CNN architecture,
Streaming media
BibRef
Zhang, K.X.[Kai-Xuan],
Xu, Y.[Yang],
Sun, L.[Li],
Qiu, S.[Song],
Li, Q.L.[Qing-Li],
Person Re-id by Incorporating PCA Loss in CNN,
MMMod18(II:200-212).
Springer DOI
1802
BibRef
Peralta, B.[Billy],
Caro, L.[Luis],
Soto, A.[Alvaro],
Unsupervised Local Regressive Attributes for Pedestrian
Re-identification,
CIARP17(517-524).
Springer DOI
1802
BibRef
Earlier: A1, A3, Only:
Multi-target Tracking with Sparse Group Features and Position Using
Discrete-Continuous Optimization,
HIS14(680-694).
Springer DOI
1504
BibRef
Chahar, H.[Harendra],
Nain, N.[Neeta],
A Study on Deep Convolutional Neural Network Based Approaches for
Person Re-identification,
PReMI17(543-548).
Springer DOI
1711
BibRef
Xu, B.[Bolei],
Qiu, G.P.[Guo-Ping],
Unsupervised Person Re-identification via Graph-Structured Image
Matching,
HIS16(III: 301-314).
Springer DOI
1704
BibRef
Huynh, X.P.[Xuan-Phung],
Choi, I.H.[In-Ho],
Kim, Y.G.[Yong-Guk],
Tracking a Human Fast and Reliably Against Occlusion and Human-Crossing,
PSIVT15(461-472).
Springer DOI
1602
BibRef
Zhou, Z.[Zhi],
Wang, Y.[Yue],
Teoh, E.K.[Eam Khwang],
Double layer salient parts based multi-people tracking,
ICIP15(3067-3071)
IEEE DOI
1512
Multi-people tracking
BibRef
de Carvalho Prates, R.F.[Raphael Felipe],
Schwartz, W.R.[William Robson],
CBRA: Color-based ranking aggregation for person re-identification,
ICIP15(1975-1979)
IEEE DOI
1512
color features
BibRef
Si, J.L.[Jian-Lou],
Zhang, H.G.[Hong-Gang],
Li, C.G.[Chun-Guang],
Regularization in metric learning for person re-identification,
ICIP15(2309-2313)
IEEE DOI
1512
Metric Learning; Person Re-identification; Regularization
BibRef
Huang, S.[Shuai],
Gu, Y.[Yun],
Yang, J.[Jie],
Shi, P.F.[Peng-Fei],
Reranking of person re-identification by manifold-based approach,
ICIP15(4253-4257)
IEEE DOI
1512
manifold; re-identification; reranking
BibRef
Tsai, M.C.[Ming-Chia],
Wei, C.P.[Chia-Po],
Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Graph regularized low-rank matrix recovery for robust person
re-identification,
ICIP15(4654-4658)
IEEE DOI
1512
Graph Regularization; Low-Rank Matrix Recovery; Person Re-Identification
BibRef
Fagot-Bouquet, L.[Loďc],
Audigier, R.[Romaric],
Dhome, Y.[Yoann],
Lerasle, F.[Frédéric],
Improving Multi-frame Data Association with Sparse Representations for
Robust Near-online Multi-object Tracking,
ECCV16(VIII: 774-790).
Springer DOI
1611
BibRef
And:
Online multi-person tracking based on global sparse collaborative
representations,
ICIP15(2414-2418)
IEEE DOI
1512
BibRef
Earlier:
Collaboration and spatialization for an efficient multi-person
tracking via sparse representations,
AVSS15(1-6)
IEEE DOI
1511
Online tracking.
computer vision
BibRef
Ristani, E.[Ergys],
Tomasi, C.[Carlo],
Tracking Multiple People Online and in Real Time,
ACCV14(V: 444-459).
Springer DOI
1504
BibRef
Mansouri, N.,
Ben Jemaa, Y.,
Motamed, C.,
Pinti, A.,
Watelain, E.,
A new strategy based on spatiogram similarity association for
multi-pedestrian tracking,
IPTA14(1-6)
IEEE DOI
1503
image sequences
BibRef
Chen, J.H.[Jia-Hui],
Sheng, H.[Hao],
Zhang, Y.[Yang],
Xiong, Z.[Zhang],
Enhancing Detection Model for Multiple Hypothesis Tracking,
PETS17(2143-2152)
IEEE DOI
1709
Analytical models, Cameras, Correlation, Detectors, Target tracking, Trajectory
BibRef
Sheng, H.[Hao],
Liu, S.[Shukai],
Ji, H.S.[Heng-Shan],
Chen, J.H.[Jia-Hui],
Xiong, Z.[Zhang],
A Pedestrian-Pedestrian and Pedestrian-Vehicle Interaction Motion Model
for Pedestrians Tracking,
ISVC14(I: 270-280).
Springer DOI
1501
BibRef
Pham, V.Q.[Viet-Quoc],
Kozakaya, T.[Tatsuo],
Okada, R.[Ryuzo],
DIET:
Dynamic Integration of Extended Tracklets for Tracking Multiple Persons,
ICPR14(1206-1211)
IEEE DOI
1412
Cities and towns
BibRef
Yan, X.[Xu],
Cheriyadat, A.[Anil],
Shah, S.K.[Shishir K.],
Hierarchical Group Structures in Multi-person Tracking,
ICPR14(2221-2226)
IEEE DOI
1412
Dynamics
BibRef
Gudys, A.[Adam],
Rosner, J.[Jakub],
Segen, J.[Jakub],
Wojciechowski, K.[Konrad],
Kulbacki, M.[Marek],
Tracking People in Video Sequences by Clustering Feature Motion Paths,
ICCVG14(236-245).
Springer DOI
1410
BibRef
Henschel, R.[Roberto],
Leal-Taixé, L.[Laura],
Rosenhahn, B.[Bodo],
Solving Multiple People Tracking in a Minimum Cost Arborescence,
MTT15(71-72)
IEEE DOI
1503
BibRef
Earlier:
Efficient Multiple People Tracking Using Minimum Cost Arborescences,
GCPR14(265-276).
Springer DOI
1411
BibRef
Xiang, Y.,
Alahi, A.[Alexandre],
Savarese, S.[Silvio],
Learning to Track: Online Multi-object Tracking by Decision Making,
ICCV15(4705-4713)
IEEE DOI
1602
Decision making
BibRef
Leal-Taixe, L.[Laura],
Fenzi, M.[Michele],
Kuznetsova, A.[Alina],
Rosenhahn, B.[Bodo],
Savarese, S.[Silvio],
Learning an Image-Based Motion Context for Multiple People Tracking,
CVPR14(3542-3549)
IEEE DOI
1409
image-based motion context
BibRef
Wang, B.[Bing],
Wang, G.[Gang],
Chan, K.L.[Kap Luk],
Wang, L.[Li],
Tracklet Association in Detect-Then-Track Paradigm for Long-Term
Multi-person Tracking,
LTDT14(716-717)
IEEE DOI
1409
BibRef
Zhang, J.M.[Jian-Ming],
lo Presti, L.[Liliana],
Sclaroff, S.[Stan],
Online Multi-person Tracking by Tracker Hierarchy,
AVSS12(379-385).
IEEE DOI
1211
BibRef
Nie, W.Z.[Wei-Zhi],
Liu, A.A.[An-An],
Su, Y.T.[Yu-Ting],
Multiple Person Tracking by Spatiotemporal Tracklet Association,
AVSS12(481-486).
IEEE DOI
1211
BibRef
Jiang, X.Y.[Xiao-Yan],
Rodner, E.[Erik],
Denzler, J.[Joachim],
Multi-person Tracking-by-Detection Based on Calibrated Multi-camera
Systems,
ICCVG12(743-751).
Springer DOI
1210
BibRef
Piatkowska, E.[Ewa],
Kogler, J.,
Belbachir, A.N.[Ahmed Nabil],
Gelautz, M.[Margrit],
Improved Cooperative Stereo Matching for Dynamic Vision Sensors with
Ground Truth Evaluation,
ECVW17(370-377)
IEEE DOI
1709
BibRef
Earlier: A1, A3, A4, Only:
Asynchronous Stereo Vision for Event-Driven Dynamic Stereo Sensor
Using an Adaptive Cooperative Approach,
CDC4CV13(45-50)
IEEE DOI
1403
Biosensors, Cameras, Heuristic algorithms,
Voltage control.
BibRef
Piatkowska, E.[Ewa],
Belbachir, A.N.[Ahmed Nabil],
Schraml, S.[Stephan],
Gelautz, M.[Margrit],
Spatiotemporal multiple persons tracking using Dynamic Vision Sensor,
ECVW12(35-40).
IEEE DOI
1207
BibRef
Heili, A.[Alexandre],
Odobez, J.M.[Jean-Marc],
Parameter estimation and contextual adaptation for a multi-object
tracking CRF model,
PETS13(14-21)
IEEE DOI
1411
object detection
BibRef
Heili, A.[Alexandre],
Chen, C.[Cheng],
Odobez, J.M.[Jean-Marc],
Detection-based multi-human tracking using a CRF model,
VS11(1673-1680).
IEEE DOI
1201
BibRef
Luo, X.H.[Xing-Han],
Tan, R.T.[Robby T.],
Veltkamp, R.C.[Remco C.],
Multi-person tracking based on vertical reference lines and dynamic
visibility analysis,
ICIP11(1877-1880).
IEEE DOI
1201
BibRef
Thaler, M.[Marcus],
Bailer, W.[Werner],
Real-Time Person Detection and Tracking in Panoramic Video,
CVSports13(1027-1032)
IEEE DOI
1309
broadcast; detection; panoramic; sports; tracking
BibRef
Thaler, M.[Marcus],
Kaiser, R.[Rene],
Bailer, W.[Werner],
Kriechbaum, A.[Andreas],
Tracking Persons in Ultra-HD Panoramic Video,
MMMod12(633-635).
Springer DOI
1201
BibRef
Yeh, H.H.[Hsin-Ho],
Chen, J.Y.[Jiun-Yu],
Huang, C.R.[Chun-Rong],
Chen, C.S.[Chu-Song],
An adaptive approach for overlapping people tracking based on
foreground silhouettes,
ICIP10(3489-3492).
IEEE DOI
1009
BibRef
Galoogahi, H.K.,
Tracking Groups of People in Presence of Occlusion,
PSIVT10(438-443).
IEEE DOI
1011
BibRef
Zen, G.[Gloria],
Lanz, O.[Oswald],
Messelodi, S.[Stefano],
Ricci, E.[Elisa],
Tracking Multiple People with Illumination Maps,
ICPR10(3484-3487).
IEEE DOI
1008
BibRef
Bütepage, J.,
Black, M.J.,
Kragic, D.[Danica],
Kjellström, H.[Hedvig],
Deep Representation Learning for Human Motion Prediction and
Classification,
CVPR17(1591-1599)
IEEE DOI
1711
Computational modeling, Correlation, Decoding, Encoding,
Feature extraction, Hidden, Markov, models
BibRef
Kjellstrom, H.[Hedvig],
Kragic, D.[Danica],
Black, M.J.[Michael J.],
Tracking people interacting with objects,
CVPR10(747-754).
IEEE DOI
1006
BibRef
Breitenstein, M.D.[Michael D.],
Reichlin, F.[Fabian],
Leibe, B.[Bastian],
Koller-Meier, E.[Esther],
Van Gool, L.J.[Luc J.],
Robust tracking-by-detection using a detector confidence particle
filter,
ICCV09(1515-1522).
IEEE DOI
0909
multi-person tracking. Particle filter framework.
BibRef
Ishiguro, K.[Katsuhiko],
Yamada, T.[Takeshi],
Ueda, N.[Naonori],
Simultaneous clustering and tracking unknown number of objects,
CVPR08(1-8).
IEEE DOI
0806
BibRef
Apewokin, S.,
Valentine, B.,
Bales, M.R.[M. Ryan],
Wills, L.M.[Linda M.],
Wills, D.S.[D. Scott],
Tracking multiple pedestrians in real-time using kinematics,
EmbedCV08(1-6).
IEEE DOI
0806
BibRef
Boufama, B.,
Ali, M.A.,
Tracking Multiple People in the Context of Video Surveillance,
ICIAR07(581-592).
Springer DOI
0708
BibRef
Feldmann, T.[Tobias],
Scheuermann, B.[Björn],
Rosenhahn, B.[Bodo],
Wörner, A.[Annika],
N-View Human Silhouette Segmentation in Cluttered, Partially Changing
Environments,
DAGM10(363-372).
Springer DOI
1009
BibRef
Rosenhahn, B.[Bodo],
Klette, R.[Reinhard],
Sommer, G.[Gerald],
Silhouette Based Human Motion Estimation,
DAGM04(294-301).
Springer DOI
0505
BibRef
Girondel, V.,
Caplier, A.,
Bonnaud, L.,
Real time tracking of multiple persons by Kalman filtering and face
pursuit for multimedia applications,
Southwest04(201-205).
IEEE DOI
0411
BibRef
Mori, T.[Taketoshi],
Matsumoto, T.[Takashi],
Shimosaka, M.[Masamichi],
Noguchi, H.[Hiroshi],
Sato, T.[Tomomasa],
Multiple Persons Tracking with Data Fusion of Multiple Cameras and
Sensing Floor Using Particle Filters,
M2SFA208(xx-yy).
0810
BibRef
Harada, T.[Tatsuya],
Sato, T.[Tomomasa],
Mori, T.[Taketoshi],
Human Motion Tracking System Based on Skeleton and Surface Integration
Model Using Pressure Sensors Distribtuion Bed,
HUMO00(99-106).
IEEE Top Reference.
0010
BibRef
Roh, H.,
Kang, S.,
Lee, S.W.,
Multiple People Tracking Using Appearance Model Based on Temporal Color,
ICPR00(Vol IV: 643-646).
IEEE DOI
0009
BibRef
Bernier, O.J.[Olivier J.],
Collobert, M.,
Feraud, R.,
Lemaire, V.,
Viallet, J.E.,
Collobert, D.,
MULTRAK: a system for automatic multiperson localization and tracking
in real-time,
ICIP98(I: 136-140).
IEEE DOI
9810
BibRef
Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Tracking People, Re-Identification Issues, Occlusions .