17.1.3.4.3 Tracking Several People

Chapter Contents (Back)
Motion, Human. Tracking. Re-Identification.
See also Tracking People, Re-Identification Issues, Occlusions.
See also Tracking People with Stereo, or Depth.
See also Visible-Infrared Re-Identification, RGB-IR. Learning, CNN, etc.:
See also Tracking People, Re-Identification Issues, Learning.
See also Domain Adaption, Cross-Domain, Learning, Re-Identification Issues.
See also Re-Identification, Cloth-Changing, Clothes Changing.

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

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Feature extraction, Measurement, Cameras, Benchmark testing, Probes, Image color analysis, Histograms, Person re-identification, benchmark BibRef

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Cameras, Visualization, Surveillance, Measurement, Topology, Training, Task analysis, Camera selection, person re-identification, visual surveillance BibRef

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Wu, L.[Lin], Wang, Y.[Yang], Gao, J.B.[Jun-Bin], Li, X.[Xue],
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Person re-identification, Convolutional neural networks, Deep hashing, Structured embedding BibRef

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Person re-identification BibRef

Ren, Y.T.[Yu-Tao], Li, X.L.[Xue-Long], Lu, X.Q.[Xiao-Qiang],
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Person re-identification BibRef

Sun, B.Y.[Bang-Yong], Ren, Y.T.[Yu-Tao], Lu, X.Q.[Xiao-Qiang],
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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.],
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Adaptation models, Computational modeling, Histograms, Image color analysis, Target tracking, Videos, Munkres' algorithm, visual tracking BibRef

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Jiang, Z.Q.[Zheng-Qiang], Huynh, D.Q.[Du Q.], Moran, W.[William], Challa, S.[Subhash],
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Zhou, S., Wang, J., Shi, R., Hou, Q., Gong, Y., Zheng, N.,
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Cameras, Feature extraction, Learning systems, Machine learning, Measurement, Neural networks, Robustness, Person re-identification, set to set similarity comparison BibRef

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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],
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Feng, Z., Lai, J., Xie, X.,
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Benchmark testing, Cameras, Computational modeling, Dictionaries, Feature extraction, Machine learning, Measurement, view-specific deep networks BibRef

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Person re-identification, Re-ranking, Expanded k-reciprocal neighbors, Rank list similarity BibRef

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Person re-identification, Re-ranking, Expanded neighborhoods distance, Two-level neighborhoods BibRef

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Person re-identification, Part-level feature, Prediction alignment, Global-local feature BibRef

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Elsevier DOI 1904
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Group Re-Identification, Dictionary learning, Encoding. feature extraction, group theory, image coding, image matching, image representation, image sensors, unsupervised learning, Re, Visualization BibRef

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Elsevier DOI 1906
Re-Identification, Distributed, Camera network, Rank fusion, Consensus BibRef

Lu, J., He, Y., Liu, T., Chen, X.,
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IEEE DOI 1906
Feature extraction, Training, Reliability, Signal processing algorithms, Unsupervised learning, penalty term BibRef

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IEEE DOI 1908
Measurement, Feature extraction, Semantics, Image color analysis, Reliability, Probes, Topology, Person re-identification, topological relationship BibRef

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IEEE DOI 1909
History, Cameras, Task analysis, Optimization, Trajectory, Video surveillance, Image retrieval, Person re-identification, log, cross-ranking BibRef

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Lighting, Cameras, Task analysis, Training, Feature extraction, Testing, Generators, Person re-identification, feature disentanglement BibRef

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Springer DOI 1909
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Jiang, L., Liang, C., Xu, D., Huang, W.,
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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.,
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ICIP19(3935-3939)
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Person Search, Person Re-Identification, Multi-Scale BibRef

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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],
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Elsevier DOI 1912
Person re-identification, Re-ranking, Similarity metric BibRef

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IEEE DOI 1912
Measurement, Task analysis, Robustness, Learning systems, Visualization, Benchmark testing, Computational modeling, Fusion, person reidentification (person re-id) BibRef

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IEEE DOI 1912
Feature extraction, Legged locomotion, Semantics, Optical computing, Optical imaging, Visualization, multi-model ensemble BibRef

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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],
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Measurement, Degradation, Deep learning, Image recognition, Benchmark testing, Pattern recognition BibRef

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Zhang, Z.Z.[Zhi-Zheng], Lan, C.L.[Cui-Ling], Zeng, W.J.[Wen-Jun], Jin, X.[Xin], Chen, Z.B.[Zhi-Bo],
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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],
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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)
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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],
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IEEE DOI 2209
Training, Noise measurement, Data models, Task analysis, Training data, Predictive models, Heuristic algorithms, Dirty, poor BibRef

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Elsevier DOI 1806
Person re-identification, Automated surveillance, People analysis, Smart city applications BibRef

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Yao, H.T.[Han-Tao], Xu, C.S.[Chang-Sheng],
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Probes, Search problems, Detectors, Proposals, Visualization, Noise measurement, Transforms, Detection-Matching person search, person re-identification BibRef

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Pedestrian re-identification, Structural relationship, Action hypergraph, Saliency score BibRef

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Layout, Feature extraction, Cameras, Task analysis, Reliability, Probes, Heuristic algorithms, Group reidentification (Re-ID), Re-ID BibRef

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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
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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
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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
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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
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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


Khaldi, K.[Khadija], Nguyen, V.D.[Vuong D.], Mantini, P.[Pranav], Shah, S.[Shishir],
Unsupervised Person Re-Identification in Aerial Imagery,
RWSurvil24(260-269)
IEEE DOI 2404
Image analysis, Surveillance, Self-supervised learning, Feature extraction, Cameras 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
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Chen, C.[Cuiqun], Ye, M.[Mang], Jiang, D.[Ding],
Towards Modality-Agnostic Person Re-identification with Descriptive Query,
CVPR23(15128-15137)
IEEE DOI 2309
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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
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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
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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
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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,
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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,
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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
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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
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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).
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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
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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).
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Peralta, B.[Billy], Caro, L.[Luis], Soto, A.[Alvaro],
Unsupervised Local Regressive Attributes for Pedestrian Re-identification,
CIARP17(517-524).
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Earlier: A1, A3, Only:
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HIS14(680-694).
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Chahar, H.[Harendra], Nain, N.[Neeta],
A Study on Deep Convolutional Neural Network Based Approaches for Person Re-identification,
PReMI17(543-548).
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Xu, B.[Bolei], Qiu, G.P.[Guo-Ping],
Unsupervised Person Re-identification via Graph-Structured Image Matching,
HIS16(III: 301-314).
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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).
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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 .


Last update:Oct 22, 2024 at 22:09:59