16.6.2.12.5 Target Tracking Techniques, Occlusions, Clutter

Chapter Contents (Back)
Target Tracking. Clutter. Occlusions. Missing data, reacquisition. Multi-Object Occlusion issues:
See also Target Tracking, Multi-Object Tracking, Occlusions.

Legters, Jr., G.R., and Young, T.Y.,
A Mathematical Model for Computer Image Tracking,
PAMI(4), No. 6, November 1982, pp. 583-594. Kalman Filter. Operates on synthetic binary images, operators for translation and rotation describe the motion and a predictive Kalman filter is used to track motion through occlusions (2-d rigid motion only). Differences between pairs of scenes (images) give a positive and negative image. From the error function, the adjustment in (Dx, Dy) can be computed (iterate!). The gradient of the error is computed to aid in this (change is in the direction of the gradient). There may be something in the formulation that implies that perfect differences can give proper results. BibRef 8211

Ichida, L.F.[Le_Roy F.], Sturla, R.A.[Robert A.], Sacks, J.M.[Jacob M.],
Scene tracker using multiple independent correlators,
US_Patent4,220,967, 09/02/1980.
HTML Version. Correlation tracking. BibRef 8009

Sacks, J.M.[Jack M.], Coleman, G.B.[Guy B.],
Target acquisition system and method,
US_Patent4,739,401, 04/19/1988.
HTML Version. BibRef 8804

Lo, T.K.[Thomas K.], Banh, N.D.[Nam D.], Bohn, T.T.[Timothy T.], Sacks, J.M.[Jack M.],
Apparatus and method for tracking a target,
US_Patent5,062,056, Oct 29, 1991
WWW Link. BibRef 9110

Kokaram, A.C., Morris, R.D., Fitzgerald, W.J., Rayner, P.J.W.,
Detection of Missing Data in Image Sequences,
IP(4), No. 11, November 1995, pp. 1496-1508.
IEEE DOI BibRef 9511
And:
Interpolation of Missing Data in Image Sequences,
IP(4), No. 11, November 1995, pp. 1509-1519.
IEEE DOI BibRef

Jauffret, C., Pillon, D.,
Observability in Passive Target Motion Analysis,
AeroSys(32), No. 4, October 1996, pp. 1290-1300. 9611
BibRef

Kirubarajan, T., Bar-Shalom, Y.,
Low Observable Target Motion Analysis Using Amplitude Information,
AeroSys(32), No. 4, October 1996, pp. 1367-1384. 9611
BibRef

Perlovsky, L.I.,
Cramer-Rao Bound for Tracking in Clutter and Tracking Multiple Objects,
PRL(18), No. 3, March 1997, pp. 283-288. 9706
BibRef

Veenman, C.J.[Cor J.], Reinders, M.J.T.[Marcel J.T.], Backer, E.[Eric],
Resolving Motion Correspondence for Densely Moving Points,
PAMI(23), No. 1, January 2001, pp. 54-72.
IEEE DOI 0101
With lots of points, there is always ambiguity in the resulting match. Track to deal with occlusions, crossings, etc. BibRef

Veenman, C.J., Hendriks, E.A., Reinders, M.J.T.,
A fast and robust point tracking algorithm,
ICIP98(III: 653-657).
IEEE DOI 9810
BibRef

Jurie, F.[Frédéric], Dhome, M.[Michel],
Real time tracking of 3D objects: an efficient and robust approach,
PR(35), No. 2, February 2002, pp. 317-328.
Elsevier DOI 0201
BibRef

Masson, L.[Lucie], Jurie, F.[Frédéric], Dhome, M.[Michel],
Tracking 3D Object using Flexible Models,
BMVC05(xx-yy).
HTML Version. 0509
BibRef
Earlier: A1, A2, A3:
Contour/Texture Approach for Visual Tracking,
SCIA03(661-668).
Springer DOI 0310
BibRef
Earlier: A1, A3, A2:
Robust real time tracking of 3D objects,
ICPR04(IV: 252-255).
IEEE DOI 0409
BibRef

Duculty, F., Dhome, M., Jurie, F.,
Real time 3D face tracking from appearance,
ICIP02(I: 581-584).
IEEE DOI 0210
BibRef
Earlier:
Tracking of 3D Objects from Appearance,
SCIA01(O-Tu5). 0206
BibRef

Jurie, F.[Frédéric], Dhome, M.[Michel],
Hyperplane Approximation for Template Matching,
PAMI(24), No. 7, July 2002, pp. 996-1000.
IEEE Abstract. 0207
BibRef
Earlier:
Real Time 3D Template Matching,
CVPR01(I:791-796).
IEEE DOI 0110
BibRef
And:
Real Time Tracking of 3d Objects with Occultations,
ICIP01(I: 413-416).
IEEE DOI 0108
3-D Pose. And Tracking. BibRef

Jurie, F.[Frederic], Dhome, M.[Michel],
Real Time Robust Template Matching,
BMVC02(Matching/Recognition). 0208
BibRef
Earlier:
A Simple and Efficient Template Matching Algorithm,
ICCV01(II: 544-549).
IEEE DOI 0106
BibRef

Iannizzotto, G.[Giancarlo], Vita, L.[Lorenzo],
On-line Object Tracking for Colour Video Analysis,
RealTimeImg(8), No. 2, April 2002, pp. 145-155.
DOI Link 0208
Using contours, but not snake approach, track object with occlusions.
See also Fast and Accurate Edge-Based Segmentation with No Contour Smoothing in 2-D Real Images. BibRef

Iannizzotto, G.,
Real-time Object Tracking with Movels and Affine Transformations,
ICIP00(Vol I: 316-318).
IEEE DOI 0008
BibRef

Strens, M.J.A.[Malcolm J. A.], Gregory, I.N.[Ian N.],
Tracking in cluttered images,
IVC(21), No. 9, September 2003, pp. 891-911.
Elsevier DOI 0308
BibRef

Sanchez, O.[Olivia], Dibos, F.[Françoise],
Displacement Following of Hidden Objects in a Video Sequence,
IJCV(57), No. 2, May 2004, pp. 91-105.
DOI Link 0402
Recovery of the most likely motion of the occluded object using optical flow (at time of occlusion). BibRef

Jonchery, C.[Claire], Dibos, F.[Françoise], Koepfler, G.[Georges],
Camera Motion Estimation Through Planar Deformation Determination,
JMIV(32), No. 1, September 2008, pp. xx-yy.
Springer DOI 0804
BibRef

Dibos, F.[Françoise], Jonchery, C.[Claire], Koepfler, G.[Georges],
Iterative Camera Motion and Depth Estimation in a Video Sequence,
CAIP09(1028-1035).
Springer DOI 0909
BibRef

Gentile, C.[Camillo], Camps, O.I.[Octavia I.], and Sznaier, M.[Mario],
Segmentation for Robust Tracking in the Presence of Severe Occlusion,
IP(13), No. 2, February 2004, pp. 166-178.
IEEE DOI 0404
BibRef
Earlier: CVPR01(II:483-489).
IEEE DOI 0110
Maintain the object when it passes behind another. Apply to traffic. BibRef

Xiong, F.[Fei], Camps, O.I.[Octavia I.], Sznaier, M.[Mario],
Dynamic Context for Tracking behind Occlusions,
ECCV12(V: 580-593).
Springer DOI 1210
BibRef

Lim, H.[Hwasup], Camps, O.I.[Octavia I.], Sznaier, M.[Mario], Morariu, V.I.[Vlad I.],
Dynamic Appearance Modeling for Human Tracking,
CVPR06(I: 751-757).
IEEE DOI 0606
BibRef

Lim, H.[Hwasup], Camps, O.I.[Octavia I.], Sznaier, M.[Mario],
A Caratheodory-Fejer Approach to Dynamic Appearance Modeling,
CVPR05(I: 301-307).
IEEE DOI 0507
BibRef

Fransen, B.R.[Benjamin R.], Camps, O.I.[Octavia I.], Sznaier, M.[Mario],
Robust Structure from Motion and Identified Dynamics,
ICCV05(I: 772-777).
IEEE DOI 0510
Estimate dynamics from previous frames. BibRef

Camps, O.I., Lim, H.[Hwasup], Mazzaro, C., Sznaier, M.,
A Caratheodory-Fejer Approach to Robust Multiframe Tracking,
ICCV03(1048-1055).
IEEE DOI 0311
Model the dynmaics as an unknown operator that satisfies certain interpolation conditions. BibRef

Nguyen, H.T.[Hieu T.], Smeulders, A.W.M.[Arnold W.M.],
Fast Occluded Object Tracking by a Robust Appearance Filter,
PAMI(26), No. 8, August 2004, pp. 1099-1104.
IEEE Abstract. 0407
BibRef
Earlier:
Template tracking using color invariant pixel features,
ICIP02(I: 569-572).
IEEE DOI 0210
Learn the object to handle aspect changes. Template matching based, with template updated by Kalman filter. BibRef

Nguyen, H.T.[Hieu T.], Smeulders, A.W.M.[Arnold W.M.],
Robust Tracking Using Foreground-Background Texture Discrimination,
IJCV(69), No. 3, September 2006, pp. 277-293.
Springer DOI 0606
BibRef
Earlier:
Tracking Aspects of the Foreground against the Background,
ECCV04(Vol II: 446-456).
Springer DOI 0405
BibRef

Nguyen, H.T.[Hieu T.], Ji, Q.A.[Qi-Ang], Smeulders, A.W.M.[Arnold W.M.],
Spatio-Temporal Context for Robust Multitarget Tracking,
PAMI(29), No. 1, January 2007, pp. 52-64.
IEEE DOI 0701
BibRef
Earlier:
Robust multi-target tracking using spatio-temporal context,
CVPR06(I: 578-585).
IEEE DOI 0606
Use context to maintain tracking with occlusions. Spatial context of background and nearby targets and the temporal context. BibRef

Yilmaz, A.[Alper], Li, X.[Xin], and Shah, M.[Mubarak],
Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras,
PAMI(26), No. 11, November 2004, pp. 1531-1536.
IEEE Abstract. 0410
BibRef

Shu, G.[Guang], Dehghan, A.[Afshin], Oreifej, O.[Omar], Hand, E.[Emily], Shah, M.[Mubarak],
Part-based multiple-person tracking with partial occlusion handling,
CVPR12(1815-1821).
IEEE DOI 1208

See also UCF Parking Lot Tracking.
See also Simultaneous Video Stabilization and Moving Object Detection in Turbulence. BibRef

Loutas, E., Pitas, I., Nikou, C.,
Entropy-based metrics for the analysis of partial and total occlusion in video object tracking,
VISP(151), No. 6, December 2004, pp. 487-497.
IEEE Abstract. 0501
BibRef
Earlier: A1, A3, A2:
Information theory-based analysis of partial and total occlusion in object tracking,
ICIP02(II: 309-312).
IEEE DOI 0210
BibRef

Zoidi, O.[Olga], Nikolaidis, N.[Nikos], Tefas, A.[Anastasios], Pitas, I.[Ioannis],
Stereo object tracking with fusion of texture, color and disparity information,
SP:IC(29), No. 5, 2014, pp. 573-589.
Elsevier DOI 1406
Stereo object tracking BibRef

Papachristou, K.[Konstantinos], Tefas, A.[Anastasios], Nikolaidis, N.[Nikos], Pitas, I.[Ioannis],
Stereoscopic video shot clustering into semantic concepts based on visual and disparity information,
ICIP14(5472-5476)
IEEE DOI 1502
Image color analysis BibRef

Zoidi, O.[Olga], Nikolaidis, N.[Nikos], Pitas, I.[Ioannis],
Exploiting disparity information in visual object tracking,
3DTV12(1-4).
IEEE DOI 1212
BibRef

Loutas, E., Nikolaidis, N.[Nikos], Pitas, I.[Ioannis],
Evaluation of tracking reliability metrics based on information theory and normalized correlation,
ICPR04(IV: 653-656).
IEEE DOI 0409
BibRef

Pang, C.C.C., Lam, W.W.L., Yung, N.H.C.,
A novel method for resolving vehicle occlusion in a monocular traffic-image sequence,
ITS(5), No. 3, September 2004, pp. 129-141.
IEEE Abstract. 0501
BibRef

Cavallaro, A.P., Steiger, O., Ebrahimi, T.,
Tracking Video Objects in Cluttered Background,
CirSysVideo(15), No. 4, April 2005, pp. 575-584.
IEEE Abstract. 0501
BibRef

Greenhill, D.R., Renno, J.R., Orwell, J., Jones, G.A.,
Learning the Semantic Landscape: Embedding scene knowledge in object tracking,
RealTimeImg(11), No. 3, June 2005, pp. 186-203.
Elsevier DOI 0508
BibRef

Greenhill, D.R., Renno, J.R., Orwell, J., Jones, G.A.,
Occlusion Analysis: Learning and Utilising Depth Maps in Object Tracking,
IVC(26), No. 3, 3 March 2008, pp. 430-441.
Elsevier DOI 0801
BibRef
Earlier: BMVC04(xx-yy).
HTML Version. 0508
Object tracking; Occlusion analysis; Depth map BibRef

Ren, J.P., Orwell, J., Jones, G.A.,
Towards plug-and-play visual surveillance: learning tracking models,
ICIP02(III: 453-456).
IEEE DOI 0210
BibRef

Gao, J.[Jean], Kosaka, A.[Akio], Kak, A.C.[Avinash C.],
A multi-Kalman filtering approach for video tracking of human-delineated objects in cluttered environments,
CVIU(99), No. 1, July 2005, pp. 1-57.
Elsevier DOI
PDF File. 0506
BibRef
And: Erratum: CVIU(102), No. 3, June 2006, pp. 259.
Elsevier DOI BibRef
And: Corrected version: CVIU(102), No. 3, June 2006, pp. 260-316.
Elsevier DOI Kalman filtering; Normalized cross-correlation; Recursive motion estimation; Boundary extraction; Region growing; Human-in-the-loop segmentation 0605
BibRef

Yoon, Y.R.[Young-Rock], Kosaka, A.[Akio], Kak, A.C.[Avinash C.],
A New Kalman-Filter-Based Framework for Fast and Accurate Visual Tracking of Rigid Objects,
RA(24), No. 5, October 2008, pp. xx-yy.
PDF File. BibRef 0810

Kwon, H., Yoon, Y.R., Park, J.B., and Kak, A.C.,
Person Tracking with a Mobile Robot using Two Uncalibrated Independently Moving Cameras,
CRA05(xx-yy).
PDF File. BibRef 0500

Yoon, Y.R., Kosaka, A., Park, J.B., and Kak, A.C.,
A New Approach to the Use of Edge Extremities for Model-based Object Tracking,
CRA05(xx-yy).
PDF File. BibRef 0500

Chattopadhyay, S.[Somrita], Roros, C.J.[Constantine J.], Kak, A.C.[Avinash C.],
A Collaborative Algorithmic Framework to Track Objects and Events,
ICIP19(4000-4004)
IEEE DOI 1910
Tracking, Kalman Filter, CNN BibRef

Gao, J.[Jean],
Self-occlusion immune video tracking of objects in cluttered environments,
AVSBS03(79-84).
IEEE DOI 0310
BibRef

Senior, A.W.[Andrew W.], Hampapur, A.[Arun], Tian, Y.L.[Ying-Li], Brown, L.M.[Lisa M.], Pankanti, S.[Sharath], Bolle, R.M.[Ruud M.],
Appearance Models for Occlusion Handling,
IVC(24), No. 11, 1 November 2006, pp. 1233-1243.
Elsevier DOI 0610
BibRef
Earlier: PETS01(xx-yy). 0110
Probabilistic color appearance models; Visual vehicle and people tracking; Occlusion resolution; Moving object segmentation; Surveillance BibRef

Brown, L.M., Lu, M.[Max], Shu, C.F.[Chiao-Fe], Tian, Y.L.[Ying-Li], Hampapur, A.,
Improving Performance via Post Track Analysis,
PETS05(341-347).
IEEE DOI 0602
BibRef

Xu, M.[Ming], Ellis, T.[Tim],
Augmented tracking with incomplete observation and probabilistic reasoning,
IVC(24), No. 11, 1 November 2006, pp. 1202-1217.
Elsevier DOI 0610
Kalman filtering; Occlusion; Partial observation; BAYESIAN network BibRef

Xu, M.[Ming], Ellis, T.[Tim], Godsill, S.J., Jones, G.A.,
Visual tracking of partially observable targets with suboptimal filtering,
IET-CV(5), No. 1, March 2011, pp. 1-13.
DOI Link 1101
Award, IET CV Premium. BibRef

Khan, Z.[Zia], Balch, T.[Tucker], Dellaert, F.[Frank],
MCMC Data Association and Sparse Factorization Updating for Real Time Multitarget Tracking with Merged and Multiple Measurements,
PAMI(28), No. 12, December 2006, pp. 1960-1972.
IEEE DOI 0611
BibRef
Earlier:
Multitarget Tracking with Split and Merged Measurements,
CVPR05(I: 605-610).
IEEE DOI 0507
BibRef

Schindler, G.[Grant], Dellaert, F.[Frank],
A Rao-Blackwellized Parts-Constellation Tracker,
WDV06(178-189).
Springer DOI 0705
BibRef

Magarey, J.F.A.[Julian Frank Andrew],
Video feature tracking with loss-of-track detection,
US_Patent7,177,446, Feb 13, 2007
WWW Link. BibRef 0702

Lin, W.C.[Wen-Chieh], Liu, Y.X.[Yan-Xi],
A Lattice-Based MRF Model for Dynamic Near-Regular Texture Tracking,
PAMI(29), No. 5, May 2007, pp. 777-792.
IEEE DOI 0704
BibRef
Earlier:
Tracking Dynamic Near-Regular Texture Under Occlusion and Rapid Movements,
ECCV06(II: 44-55).
Springer DOI 0608
BibRef

Lin, W.C.[Wen-Chieh],
A Lattice-based MRF model for Dynamic Near-regular Texture Tracking and Manipulation,
CMU-RI-TR-05-58, December, 2005. BibRef 0512 Ph.D.Thesis.
WWW Link. BibRef

Shang, L.M.[Li-Min], Jasiobedzki, P.[Piotr], Greenspan, M.[Michael],
Model-Based Tracking by Classification in a Tiny Discrete Pose Space,
PAMI(29), No. 6, June 2007, pp. 976-989.
IEEE DOI 0704
BibRef
Earlier:
Discrete pose space estimation to improve ICP-based tracking,
3DIM05(523-530).
IEEE DOI 0508
BibRef
Earlier: A3, A1, A2:
Efficient Tracking with the Bounded Hough Transform,
CVPR04(I: 520-527).
IEEE DOI 0408
Use known motion limits to reduce match search space. Hybrid method combines with ICP for better results. BibRef

Shang, L.M.[Li-Min], Greenspan, M.[Michael],
Real-time Object Recognition in Sparse Range Images Using Error Surface Embedding,
IJCV(89), No. 2-3, September 2010, pp. xx-yy.
Springer DOI 1006
BibRef

Shang, L.M.[Li-Min], Greenspan, M.[Michael],
Pose Determination by Potential Well Space Embedding,
3DIM07(297-304).
IEEE DOI 0708
BibRef

Bennett, B.[Brandon], Magee, D.R.[Derek R.], Cohn, A.G.[Anthony G.], Hogg, D.C.[David C.],
Enhanced tracking and recognition of moving objects by reasoning about spatio-temporal continuity,
IVC(26), No. 1, 1 January 2008, pp. 67-81.
Elsevier DOI 0711
Visual surveillance; Spatial reasoning; Temporal reasoning; Resolving ambiguity; Continuity BibRef

Tavanai, A.[Aryana], Sridhar, M.[Muralikrishna], Chinellato, E.[Eris], Cohn, A.G.[Anthony G.], Hogg, D.C.[David C.],
Joint Tracking and Event Analysis for Carried Object Detection,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Tavanai, A.[Aryana], Sridhar, M.[Muralikrishna], Gu, F.[Feng], Cohn, A.G.[Anthony G.], Hogg, D.C.[David C.],
Context Aware Detection and Tracking,
ICPR14(2197-2202)
IEEE DOI 1412
Context BibRef

Ross, D.A.[David A.], Lim, J.W.[Jong-Woo], Lin, R.S.[Ruei-Sung], Yang, M.H.[Ming-Hsuan],
Incremental Learning for Robust Visual Tracking,
IJCV(77), No. 1-3, May 2008, pp. 125-141.
Springer DOI 0803
BibRef
Earlier: A1, A2, A4, Only:
Adaptive Probabilistic Visual Tracking with Incremental Subspace Update,
ECCV04(Vol II: 470-482).
Springer DOI 0405
Deal with lighting changes. BibRef

Yang, M.H.[Ming-Hsuan], Lin, R.S.[Ruei-Sung], Lim, J.W.[Jong-Woo], Ross, D.A.[David A.],
Adaptive discriminative generative model and application to visual tracking,
US_Patent7,369,682, May 6, 2008
WWW Link. BibRef 0805

Zhu, L.[Lin], Zhou, J.[Jie], Song, J.Y.[Jing-Yan],
Tracking multiple objects through occlusion with online sampling and position estimation,
PR(41), No. 8, August 2008, pp. 2447-2460.
Elsevier DOI 0805
Multiple objects tracking; Occlusion; Online sampling; Position estimation BibRef

Imai, J.I.[Jun-Ichi], Kaneko, M.[Masahide],
Visual Tracking in Occlusion Environments by Autonomous Switching of Targets,
IEICE(E91-D), No. 1, January 2008, pp. 86-95.
DOI Link 0801
BibRef

Hotta, K.[Kazuhiro],
A Robust Object Tracking Method under Pose Variation and Partial Occlusion,
IEICE(E89-D), No. 7, July 2006, pp. 2132-2141.
DOI Link 0607
BibRef

Hotta, K.[Kazuhiro],
Adaptive Weighting of Local Classifiers by Particle Filter,
ICPR06(II: 610-613).
IEEE DOI 0609
BibRef

Yang, T.[Tao], Li, J.[Jing], Pan, Q.[Quan], Cheng, Y.M.[Yong-Mei],
Visual Tracking With Automatic Confident Region Extraction,
IJIG(8), No. 3, July 2008, pp. 369-381. 0807
BibRef

Yang, T.[Tao], Li, S.Z.[Stan Z.], Pan, Q.[Quan], Li, J.[Jing],
Real-Time Multiple Objects Tracking with Occlusion Handling in Dynamic Scenes,
CVPR05(I: 970-975).
IEEE DOI 0507
BibRef

Asadi, M., Monti, F.[Francesco], Regazzoni, C.S.[Carlo S.],
Feature Classification for Robust Shape-Based Collaborative Tracking and Model Updating,
JIVP(2008), No. 2008, pp. xx-yy.
DOI Link 0811
BibRef

Asadi, M.[Majid], Regazzoni, C.S.[Carlo S.],
A Comparison of Different Approaches to Nonlinear Shift Estimation for Object Tracking,
ICIP07(III: 221-224).
IEEE DOI 0709
BibRef

Dore, A.[Alessio], Soto, M., Regazzoni, C.S.[Carlo S.],
Bayesian Tracking for Video Analytics,
SPMag(27), No. 5, 2010, pp. 46-55.
IEEE DOI 1003
BibRef

Dore, A.[Alessio], Asadi, M.[Majid], Regazzoni, C.S.[Carlo S.],
Online Discriminative Feature Selection in a Bayesian Framework using Shape and Appearance,
VS08(xx-yy). 0810
BibRef

Asadi, M.[Majid], Regazzoni, C.S.[Carlo S.],
A Probabilistic Bayesian Framework for Model-Based Object Tracking Using Undecimated Wavelet Packet Descriptors,
AVSBS08(108-115).
IEEE DOI 0809

See also Commentary Paper 2 on A Probabilistic Bayesian Framework for Model-Based Object Tracking Using Undecimated Wavelet Packet Descriptors.
See also Commentary Paper 1 on A Probabilistic Bayesian Framework for Model-Based Object Tracking Using Undecimated Wavelet Packet Descriptors. BibRef

Beoldo, A.[Andrea], Dore, A.[Alessio], Regazzoni, C.S.[Carlo S.],
Extraction of contextual information for automotive applications,
ICIP09(1153-1156).
IEEE DOI 0911
To detect other vehicles in traffic. BibRef

Dore, A.[Alessio], Beoldo, A.[Andrea], Regazzoni, C.S.[Carlo S.],
Multitarget tracking with a corner-based particle filter,
VS09(1251-1258).
IEEE DOI 0910
BibRef
Earlier:
Multiple cue adaptive tracking of deformable objects with Particle Filter,
ICIP08(237-240).
IEEE DOI 0810
BibRef

Asadi, M., Dore, A., Beoldo, A., Regazzoni, C.S.,
Tracking by using dynamic shape model learning in the presence of occlusion,
AVSBS07(230-235).
IEEE DOI 0709
BibRef

Asadi, M., Beoldo, A., Regazzoni, C.S.,
A Nonlinear-Shift Approach to Object Tracking Based on Shape Information,
CIAP07(311-316).
IEEE DOI 0709
Apply to vehicles also. BibRef

Piva, S., Comes, L., Asadi, M., Regazzoni, C.S.,
Grouped-People Splitting Based on Face Detection and Body Proportion Constraints,
AVSBS06(24-24).
IEEE DOI 0611
BibRef

Papadakis, N.[Nicolas], Bugeau, A.[Aurelie],
Tracking with Occlusions via Graph Cuts,
PAMI(33), No. 1, January 2011, pp. 144-157.
IEEE DOI 1011

See also Multi-label Depth Estimation for Graph Cuts Stereo Problems.
See also Track and Cut: Simultaneous Tracking and Segmentation of Multiple Objects with Graph Cuts. Deal with occlusions. BibRef

Nakhmani, A.[Arie], Tannenbaum, A.[Allen],
Particle Filtering with Region-Based Matching for Tracking of Partially Occluded and Scaled Targets,
SIIMS(4), No. 1, 2011, pp. 220-242.
DOI Link visual tracking; particle filtering; normalized cross-correlation; occlusions; scale changes BibRef 1100

Lee, J.[Jehoon], Lankton, S.[Shawn], Tannenbaum, A.[Allen],
Object Tracking and Target Reacquisition Based on 3-D Range Data for Moving Vehicles,
IP(20), No. 10, October 2011, pp. 2912-2924.
IEEE DOI 1110
BibRef

Lankton, S.[Shawn], Malcolm, J.[James], Nakhmani, A.[Arie], Tannenbaum, A.[Allen],
Tracking through changes in scale,
ICIP08(241-244).
IEEE DOI
PDF File. 0810
BibRef

Lee, J.[Jehoon], Karasev, P.[Peter], Tannenbaum, A.[Allen],
Range based object tracking and segmentation,
ICIP10(4641-4644).
IEEE DOI 1009
BibRef

Karasev, P.[Peter], Malcolm, J.[James], Tannenbaum, A.[Allen],
Kernel-based high-dimensional histogram estimation for visual tracking,
ICIP08(2728-2731).
IEEE DOI 0810
BibRef

Malcolm, J.[James], Rathi, Y.[Yogesh], Tannenbaum, A.[Allen],
Label Space: A Multi-object Shape Representation,
IWCIA08(xx-yy).
Springer DOI 0804
BibRef
Earlier:
Multi-Object Tracking Through Clutter Using Graph Cuts,
NRTL07(1-5).
IEEE DOI 0710
BibRef
And:
Tracking Through Clutter Using Graph Cuts,
BMVC07(xx-yy).
PDF File. 0709

See also Graph Cut Segmentation with Nonlinear Shape Priors. BibRef

Keck, Jr., M.A.[Mark A.], Davis, J.W.[James W.],
Recovery and Reasoning About Occlusions in 3D Using Few Cameras with Applications to 3D Tracking,
IJCV(95), No. 3, December 2011, pp. 240-264.
WWW Link. 1109
BibRef
Earlier:
3D occlusion recovery using few cameras,
CVPR08(1-8).
IEEE DOI 0806
Location of static occlusions and dynamic occlusion. Effectively track objects. BibRef

Tyagi, A.[Ambrish], Davis, J.W.[James W.], Keck, Jr., M.A.[Mark A.],
Multiview fusion for canonical view generation based on homography constraints,
VSSN06(61-70).
WWW Link. 0701
BibRef

Keck, Jr., M.A.[Mark A.], Davis, J.W.[James W.], Tyagi, A.[Ambrish],
Tracking mean shift clustered point clouds for 3D surveillance,
VSSN06(187-194).
WWW Link. 0701
BibRef

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Object tracking; Classification; Boosting; Feature spaces; Particle filtering BibRef

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Continuously tracking and see-through occlusion based on a new hybrid synthetic aperture imaging model,
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Spatiotemporal Oriented Energy Features for Visual Tracking,
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Zarezade, A., Rabiee, H.R., Soltani-Farani, A., Khajenezhad, A.,
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Makris, A., Prieur, C.,
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Yang, T.Y.[Tian-Yu], Li, B.[Baopu], Meng, M.Q.H.,
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Yang, Y.C.[Yan-Chao], Sundaramoorthi, G.[Ganesh],
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Self-Occlusions and Disocclusions in Causal Video Object Segmentation,
ICCV15(4408-4416)
IEEE DOI 1602
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Meshgi, K.[Kourosh], Ishii, S.[Shin],
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IEICE(E98-D), No. 7, July 2015, pp. 1260-1274.
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MVA15(475-479)
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Meshgi, K.[Kourosh], Maeda, S.I.[Shin-Ichi], Oba, S.[Shigeyuki], Skibbe, H.[Henrik], Li, Y.Z.[Yu-Zhe], Ishii, S.[Shin],
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Meshgi, K., Mirzaei, M.S., Oba, S.,
Information-Maximizing Sampling to Promote Tracking-By-Detection,
ICIP18(2700-2704)
IEEE DOI 1809
Target tracking, Tracking loops, Robustness, Visualization, Clutter, Support vector machines, visual tracking, structured sample learning BibRef

Meshgi, K.[Kourosh], Mirzaei, M.S., Oba, S., Ishii, S.[Shin],
Active collaborative ensemble tracking,
AVSS17(1-6)
IEEE DOI 1806
image classification, learning (artificial intelligence), object tracking, active collaborative ensemble tracking, Visualization BibRef

Meshgi, K.[Kourosh], Oba, S., Ishii, S.[Shin],
Active discriminative tracking using collective memory,
MVA17(374-377)
DOI Link 1708
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Robust discriminative tracking via query-by-bagging,
AVSS16(8-14)
IEEE DOI 1611
Adaptation models Detectors, Labeling, Robustness, Target tracking BibRef

Bouachir, W.[Wassim], Bilodeau, G.A.[Guillaume-Alexandre],
Exploiting structural constraints for visual object tracking,
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Elsevier DOI 1512
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Structure-aware keypoint tracking for partial occlusion handling,
WACV14(877-884)
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Object tracking. Context BibRef

Li, Z.X.[Zhen-Xi], Bilodeau, G.A.[Guillaume-Alexandre], Bouachir, W.[Wassim],
Multi-branch Siamese Networks with Online Selection for Object Tracking,
ISVC18(309-319).
Springer DOI 1811
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Attari, M., Luo, Z., Habibi, S.,
An SVSF-Based Generalized Robust Strategy for Target Tracking in Clutter,
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IEEE DOI 1605
Clutter BibRef

Derue, F.X.[François-Xavier], Bilodeau, G.A.[Guillaume-Alexandre], Bergevin, R.[Robert],
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Springer DOI 1801
Part-based tracker. Add keypoints for discrimination. BibRef

Liu, C., Liu, P., Zhao, W., Tang, X.,
Robust Tracking and Redetection: Collaboratively Modeling the Target and Its Context,
MultMed(20), No. 4, April 2018, pp. 889-902.
IEEE DOI 1804
Adaptation models, Context modeling, Correlation, Detectors, Robustness, Target tracking, Collaborative modeling, Re-detection, target tracking BibRef

Zhao, S., Zhang, S., Zhang, L.,
Towards Occlusion Handling: Object Tracking With Background Estimation,
Cyber(48), No. 7, July 2018, pp. 2086-2100.
IEEE DOI 1806
Adaptation models, Cameras, Estimation, Object tracking, Target tracking, Transmission line matrix methods, occlusion handling BibRef

Liu, M.J.[Ming-Jie], Jin, C.B.[Cheng-Bin], Yang, B.[Bin], Cui, X.N.[Xue-Nan], Kim, H.[Hakil],
Occlusion-robust object tracking based on the confidence of online selected hierarchical features,
IET-IPR(12), No. 11, November 2018, pp. 2023-2029.
DOI Link 1810
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Jiang, K., Qian, F., Song, C., Zhang, B.,
An Approach to Overcome Occlusions in Visual Tracking: By Occlusion Estimating Agency and Self-Adapting Learning Rate for Filter's Training,
SPLetters(25), No. 12, December 2018, pp. 1890-1894.
IEEE DOI 1812
convolution, image colour analysis, image filtering, learning (artificial intelligence), object detection, visual tracking BibRef

Hu, Z.T.[Zhen-Tao], Zhou, L.[Lin], Yang, Y.N.[Ya-Nan], Liu, X.X.[Xian-Xing], Jin, Y.[Yong],
Anti-occlusion tracking algorithm of video target based on prediction and re-matching strategy,
JVCIR(57), 2018, pp. 176-182.
Elsevier DOI 1812
Anti-occlusion, Mean Shift algorithm, Kalman filter, Normalized cross correlation matching BibRef

Liu, X.B.[Xiao-Bai], Xu, Q.[Qian], Chau, T.[Thuan], Mu, Y.D.[Ya-Dong], Zhu, L.[Lei], Yan, S.C.[Shui-Cheng],
Revisiting Jump-Diffusion Process for Visual Tracking: A Reinforcement Learning Approach,
CirSysVideo(29), No. 8, August 2019, pp. 2431-2441.
IEEE DOI 1908
Estimating visibility statuses of objects while tracking them in videos. Videos, Markov processes, Visualization, Task analysis, Proposals, Learning (artificial intelligence) BibRef

Liu, C., Huynh, D.Q., Reynolds, M.,
Toward Occlusion Handling in Visual Tracking via Probabilistic Finite State Machines,
Cyber(50), No. 4, April 2020, pp. 1726-1738.
IEEE DOI 2003
Target tracking, Correlation, Visualization, visual tracking Partitioning algorithms, Probabilistic logic, Adaptation models, BibRef

Cavagna, A.[Andrea], Melillo, S.[Stefania], Parisi, L.[Leonardo], Ricci-Tersenghi, F.[Federico],
SpaRTA Tracking Across Occlusions via Partitioning of 3D Clouds of Points,
PAMI(43), No. 4, April 2021, pp. 1394-1403.
IEEE DOI 2103
Cameras, Target tracking, Radar tracking, Trajectory, clouds of points BibRef

Qin, X.Y.[Xu-Yang], Xuan, S.B.[Shi-Bin], Wang, L.[Li], Chen, Y.[Yun],
Target tracking method based on interference detection,
IET-IPR(16), No. 6, 2022, pp. 1709-1723.
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Yamada, T.[Tetsutaro], Gocho, M.[Masato], Akama, K.[Kei], Yataka, R.[Ryoma], Kameda, H.[Hiroshi],
Multiple Hypothesis Tracking with Merged Bounding Box Measurements Considering Occlusion,
IEICE(E105-D), No. 8, August 2022, pp. 1456-1463.
WWW Link. 2207
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Nasseri, M.H.[Mohammad Hossein], Babaee, M.[Mohammadreza], Moradi, H.[Hadi], Hosseini, R.[Reshad],
Online relational tracking with camera motion suppression,
JVCIR(90), 2023, pp. 103750.
Elsevier DOI 2301
Multiple object tracking, Geometric interaction model, Camera motion suppression, Occlusion handling, Cascade association BibRef

Han, D.C.[Dong-Chen], Liu, W.F.[Wei-Feng], Zou, M.C.[Ming-Chen], Liu, B.[Baodi],
Non-Contrastive Nearest Neighbor Identity-Guided Method for Unsupervised Object Re-Identification,
CirSysVideo(33), No. 6, June 2023, pp. 2713-2723.
IEEE DOI 2306
Training, Task analysis, Noise measurement, Cameras, Artificial neural networks, Visualization, Semantics, unsupervised learning BibRef

Yang, W.Y.[Wen-Yu], Jiang, Y.[Yong], Wen, S.[Shuai], Fan, Y.[Yong],
Online multiple object tracking with enhanced Re-identification,
IET-CV(17), No. 6, 2023, pp. 676-686.
DOI Link 2310
computer vision, object tracking BibRef

Liang, M.C.[Ming-Chao], Kropfreiter, T.[Thomas], Meyer, F.[Florian],
A BP Method for Track-Before-Detect,
SPLetters(30), 2023, pp. 1137-1141.
IEEE DOI 2310
Belief Propagation (BP). Many, low-observable. BibRef


van Hoorick, B.[Basile], Tokmakov, P.[Pavel], Stent, S.[Simon], Li, J.[Jie], Vondrick, C.[Carl],
Tracking Through Containers and Occluders in the Wild,
CVPR23(13802-13812)
IEEE DOI 2309
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Zhou, X.[Xiao], Zhong, Y.J.[Yu-Jie], Cheng, Z.[Zhen], Liang, F.[Fan], Ma, L.[Lin],
Adaptive Sparse Pairwise Loss for Object Re-Identification,
CVPR23(19691-19701)
IEEE DOI 2309
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Yao, Y.[Yue], Gedeon, T.[Tom], Zheng, L.[Liang],
Large-scale Training Data Search for Object Re-identification,
CVPR23(15568-15578)
IEEE DOI 2309
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Xu, C.[Cheng], Pei, L.[Ling], Zhang, Z.[Zhengde],
MWNET: A Tracking Method for Frequently Occluded Scenes Based on Matter Waves,
ICIP22(936-940)
IEEE DOI 2211
Quantum mechanics, Object detection, Detectors, Maintenance engineering, Matter waves, Feature extraction, quantum evolution BibRef

Wu, J.L.[Jin-Lin], He, L.X.[Ling-Xiao], Liu, W.[Wu], Yang, Y.[Yang], Lei, Z.[Zhen], Mei, T.[Tao], Li, S.Z.[Stan Z.],
CAViT: Contextual Alignment Vision Transformer for Video Object Re-identification,
ECCV22(XIV:549-566).
Springer DOI 2211
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Sadafi, A., Katsageorgiou, V., Huang, H., Papaleo, F., Murino, V., Sona, D.,
Multiple Mice Tracking: Occlusions Disentanglement using a Gaussian Mixture Model,
ICPR18(2433-2437)
IEEE DOI 1812
Mice, Kalman filters, Gaussian distribution, Gaussian mixture model, BibRef

Niu, X., Qiao, Y.,
Context-based occlusion detection for robust visual tracking,
ICIP17(3655-3659)
IEEE DOI 1803
Correlation, Detectors, Robustness, Strain, Target tracking, Visualization, Visual tracking, background tracker, template update BibRef

Li, C., Zhou, Y., Cut, B., Hou, C.,
Depth-weighted correlation method for visual tracking with occlusion detection,
ICIP17(3660-3664)
IEEE DOI 1803
Color, Correlation, Object tracking, Robustness, Support vector machines, Target tracking, Visualization, SVM, spatio-temporal context BibRef

Meshgi, K.[Kourosh], Oba, S.[Shigeyuki], Ishii, S.[Shin],
Efficient Version-Space Reduction for Visual Tracking,
CRV17(139-146)
IEEE DOI 1804
image classification, learning (artificial intelligence), object detection, object tracking, target tracking, visual tracking BibRef

Meshgi, K.[Kourosh], Maeda, S.I.[Shin-Ichi], Oba, S.[Shigeyuki], Ishii, S.[Shin],
Data-Driven Probabilistic Occlusion Mask to Promote Visual Tracking,
CRV16(178-185)
IEEE DOI 1612
Observation Mask; Occlusion; Visual Tracking BibRef

Bogun, I.[Ivan], Ribeiro, E.[Eraldo],
MBest Struct: M-Best diverse sampling for structured tracker,
BMVC16(xx-yy).
HTML Version. 1805
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And:
Object-aware tracking,
ICPR16(1695-1700)
IEEE DOI 1705
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And:
RobStruck: Improving occlusion handling of structured tracking-by-detection using robust Kalman filter,
ICIP16(3479-3483)
IEEE DOI 1610
Adaptation models, Computational modeling, Density measurement, Detectors, Image edge detection, Robustness, Support vector machines.
See also Struck: Structured Output Tracking with Kernels. BibRef

Girdhar, R., Fouhey, D.F., Kitani, K.M., Gupta, A., Hebert, M.,
Cutting through the clutter: Task-relevant features for image matching,
WACV16(1-9)
IEEE DOI 1606
Clutter BibRef

Zheng, J.[Jin], Li, B.[Bo], Tian, P.[Peng], Luo, G.[Gang],
Robust Object Tracking Using Valid Fragments Selection,
MMMod16(I: 738-751).
Springer DOI 1601
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Liu, K.J.[Ke-Jia], Liu, B.[Bin], Chen, C.[Chang], Chen, C.W.[Chang Wen],
A hierarchical anti-occlusion tracking algorithm based on DMPF and ORB,
ICIP15(2979-2983)
IEEE DOI 1512
DMPF; ORB; occlusion; target tracking BibRef

Khan, M.H.[Muhammad Haris], Valstar, M.F.[Michel F.], Pridmore, T.P.[Tony P.],
MTS: A Multiple Temporal Scale Tracker Handling Occlusion and Abrupt Motion Variation,
ACCV14(V: 476-492).
Springer DOI 1504
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Marpuc, T.[Tughan], Alatan, A.A.[A. Aydin],
Occlusion-aware HMM-based tracking by learning,
ICIP14(4922-4926)
IEEE DOI 1502
Adaptation models BibRef

Luczak, A.[Adam], Mackowiak, S.[Slawomir], Siast, J.[Jakub],
Depth Map's 2D Histogram Assisted Occlusion Handling in Video Object Tracking,
ICCVG14(400-408).
Springer DOI 1410
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Zhang, T.Z.[Tian-Zhu], Jia, K.[Kui], Xu, C.S.[Chang-Sheng], Ma, Y.[Yi], Ahuja, N.[Narendra],
Partial Occlusion Handling for Visual Tracking via Robust Part Matching,
CVPR14(1258-1265)
IEEE DOI 1409
visual tracking BibRef

Zhang, L.[Lu], Dibeklioglu, H.[Hamdi], van der Maaten, L.[Laurens],
On Fast Trackers that are Robust to Partial Occlusions,
LTDT14(718-719)
IEEE DOI 1409
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Hua, Y.[Yang], Alahari, K.[Karteek], Schmid, C.[Cordelia],
Online Object Tracking with Proposal Selection,
ICCV15(3092-3100)
IEEE DOI 1602
BibRef
Earlier:
Occlusion and Motion Reasoning for Long-Term Tracking,
ECCV14(VI: 172-187).
Springer DOI 1408
Detectors BibRef

Halupka, K.J.[Kerry J.], Wiederman, S.D.[Steven D.], Cazzolato, B.S.[Benjamin S.], O'Carroll, D.C.[David C.],
Bio-inspired feature extraction and enhancement of targets moving against visual clutter during closed loop pursuit,
ICIP13(4098-4102)
IEEE DOI 1402
Target tracking BibRef

Xu, Y.K.[Ying-Kun], Qin, L.[Lei], Li, G.R.[Guo-Rong], Huang, Q.M.[Qing-Ming],
An efficient occlusion detection method to improve object trackers,
ICIP13(2445-2449)
IEEE DOI 1402
Visual tracking;object tracker;occlusion detection BibRef

Fragkiadaki, K.[Katerina], Zhang, W.Y.[Wei-Yu], Zhang, G.[Geng], Shi, J.B.[Jian-Bo],
Two-Granularity Tracking: Mediating Trajectory and Detection Graphs for Tracking under Occlusions,
ECCV12(V: 552-565).
Springer DOI 1210
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Angelopoulou, A.[Anastassia], Psarrou, A.[Alexandra], Garcia Rodriguez, J.[José], Gupta, G.[Gaurav],
Active-GNG: model acquisition and tracking in cluttered backgrounds,
VNBA08(17-22).
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Collins, R.T.[Robert T.], Carr, P.[Peter],
Hybrid Stochastic / Deterministic Optimization for Tracking Sports Players and Pedestrians,
ECCV14(II: 298-313).
Springer DOI 1408
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Bao, C.L.[Cheng-Long], Wu, Y.[Yi], Ling, H.B.[Hai-Bin], Ji, H.[Hui],
Real time robust L1 tracker using accelerated proximal gradient approach,
CVPR12(1830-1837).
IEEE DOI 1208
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Sharma, V.[Vinay],
A blob representation for tracking robust to merging and fragmentation,
WACV12(161-168).
IEEE DOI 1203
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Hong, S.[Seunghoon], Han, B.H.[Bo-Hyung],
Visual Tracking by Sampling Tree-Structured Graphical Models,
ECCV14(I: 1-16).
Springer DOI 1408
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Nam, H.[Hyeonseob], Han, B.H.[Bo-Hyung],
Learning Multi-domain Convolutional Neural Networks for Visual Tracking,
CVPR16(4293-4302)
IEEE DOI 1612
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Nam, H.[Hyeonseob], Hong, S.[Seunghoon], Han, B.H.[Bo-Hyung],
Online Graph-Based Tracking,
ECCV14(V: 112-126).
Springer DOI 1408
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Hong, S.[Seunghoon], Kwak, S.[Suha], Han, B.H.[Bo-Hyung],
Orderless Tracking through Model-Averaged Posterior Estimation,
ICCV13(2296-2303)
IEEE DOI 1403
Bayesian model averaging; offline tracking BibRef

Kwak, S.[Suha], Nam, W.H.[Woon-Hyun], Han, B.H.[Bo-Hyung], Han, J.H.[Joon Hee],
Learning occlusion with likelihoods for visual tracking,
ICCV11(1551-1558).
IEEE DOI 1201
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Panda, D.K.[Deepak Kumar], Meher, S.[Sukadev],
Robust real-time object tracking under background clutter,
ICIIP11(1-6).
IEEE DOI 1112
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Zhu, S.Q.[Sen-Qiang], Wang, D.W.[Dan-Wei],
Cooperative ground target tracking with input constraints,
ICARCV10(1051-1056).
IEEE DOI 1109
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Abramson, H.[Haggai], Avidan, S.[Shai],
Tracking through scattered occlusion,
MLVMA11(1-8).
IEEE DOI 1106
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Wirkert, S.[Sebastian], Dellandrea, E.[Emmanuel], Chen, L.M.[Li-Ming],
Bayesian GOETHE Tracking,
ICPR10(2077-2080).
IEEE DOI 1008
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Grabner, H.[Helmut], Matas, J.G.[Jiri G.], Van Gool, L.J.[Luc J.], Cattin, P.C.[Philippe C.],
Tracking the invisible: Learning where the object might be,
CVPR10(1285-1292).
IEEE DOI Video of talk:
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Bibby, C.[Charles], Reid, I.D.[Ian D.],
Real-time tracking of multiple occluding objects using level sets,
CVPR10(1307-1314).
IEEE DOI 1006
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Smith, A.W.B.[Andrew W.B.], Lovell, B.C.[Brian C.],
Self Occlusions and Graph Based Edge Measurement Schemes for Visual Tracking Applications,
DICTA09(416-423).
IEEE DOI 0912
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Earlier:
Measurement Function Design for Visual Tracking Applications,
ICPR06(I: 789-792).
IEEE DOI 0609
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Meng, G.[Gang], Jiang, Z.G.[Zhi-Guo], Zhao, D.[Danpei], Ye, K.[Keren],
Real-Time Illumination Robust Maneuvering Target Tracking Based on Color Invariance,
CISP09(1-5).
IEEE DOI 0910
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Bianchi, L.[Luca], Dondi, P.[Piercarlo], Gatti, R.[Riccardo], Lombardi, L.[Luca], Lombardi, P.[Paolo],
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CIAP09(797-806).
Springer DOI 0909
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Bianchi, L.[Luca], Gatti, R.[Riccardo], Lombardi, L.[Luca], Lombardi, P.[Paolo],
Tracking without Background Model for Time-of-Flight Cameras,
PSIVT09(726-737).
Springer DOI 0901
Different problem -- have the depth to deal with occlusions, etc. Separate the object by depth, then track. BibRef

Lipton, A.J.[Alan J.],
Commentary Paper 1 on 'A Probabilistic Bayesian Framework for Model-Based Object Tracking Using Undecimated Wavelet Packet Descriptors',
AVSBS08(116-116).
IEEE DOI 0809

See also Probabilistic Bayesian Framework for Model-Based Object Tracking Using Undecimated Wavelet Packet Descriptors, A. BibRef

Malik, H.[Hafiz],
Commentary Paper 2 on 'A Probabilistic Bayesian Framework for Model-Based Object Tracking Using Undecimated Wavelet Packet Descriptors',
AVSBS08(117-118).
IEEE DOI 0809

See also Probabilistic Bayesian Framework for Model-Based Object Tracking Using Undecimated Wavelet Packet Descriptors, A. BibRef

Keitler, P.[Peter], Schlegel, M.[Michael], Klinker, G.[Gudrun],
Indirect Tracking to Reduce Occlusion Problems,
ISVC08(II: 224-235).
Springer DOI 0812
BibRef

Campbell-West, F., Wang, H.B.[Hong-Bin], Miller, P.,
Where is It? Object Reacquisition in Surveillance Video,
IMVIP08(182-187).
IEEE DOI 0809
BibRef

Joshi, N.[Neel], Avidan, S.[Shai], Matusik, W.[Wojciech], Kriegman, D.J.[David J.],
Synthetic Aperture Tracking: Tracking through Occlusions,
ICCV07(1-8).
IEEE DOI 0710
BibRef

Huang, Z.Q.[Zhuan Qing], Jiang, Z.H.[Zhu-Han],
Target Positioning with Dominant Feature Elements,
CAIP07(69-76).
Springer DOI 0708
BibRef

Zhou, Y.[Yan], Hu, B.[Bo], Zhang, J.Q.[Jian-Qiu],
Occlusion Detection and Tracking Method Based on Bayesian Decision Theory,
PSIVT06(474-482).
Springer DOI 0612
BibRef

Yin, Z.Z.[Zhao-Zheng], Collins, R.T.[Robert T.],
Shape constrained figure-ground segmentation and tracking,
CVPR09(731-738).
IEEE DOI 0906
BibRef
Earlier:
Object tracking and detection after occlusion via numerical hybrid local and global mode-seeking,
CVPR08(1-8).
IEEE DOI 0806
BibRef
Earlier:
Spatial Divide and Conquer with Motion Cues for Tracking through Clutter,
CVPR06(I: 570-577).
IEEE DOI 0606
BibRef

Bunyak, F.[Filiz], Subramanya, S.R.,
Maintaining Trajectories of Salient Objects for Robust Visual Tracking,
ICIAR05(820-827).
Springer DOI 0509
BibRef

Guha, P.[Prithwijit], Mukerjee, A.[Amitabha], Subramanian, V.K.,
Formulation, detection and application of occlusion states (Oc-7) in the context of multiple object tracking,
AVSBS11(191-196).
IEEE DOI 1111
BibRef

Guha, P.[Prithwijit], Mukerjee, A.[Amitabha], Venkatesh, K.S.,
Efficient occlusion handling for multiple agent tracking by reasoning with surveillance event primitives,
PETS05(49-56).
IEEE DOI 0602
BibRef

Bartesaghi, A., Sapiro, G.,
Tracking of Moving Objects Under Severe and Total Occlusions,
ICIP05(I: 301-304).
IEEE DOI 0512
BibRef

Mittrapiyanuruk, P.[Pradit], de Souza, G.N.[Guilherme N.], Kak, A.C.[Avinash C.],
Accurate 3D Tracking of Rigid Objects with Occlusion Using Active Appearance Models,
Motion05(II: 90-95).
IEEE DOI
PDF File. 0502
BibRef

Apostoloff, N.[Nicholas], Fitzgibbon, A.W.[Andrew W.],
Learning Spatiotemporal T-Junctions for Occlusion Detection,
CVPR05(II: 553-559).
IEEE DOI 0507
BibRef

Huang, Y.[Yan], Essa, I.A.[Irfan A.],
Tracking Multiple Objects through Occlusions,
CVPR05(II: 1051-1058).
IEEE DOI
WWW Link. Dataset, Actions. 0507
BibRef
And: CVPR05(II: 1182).
IEEE DOI 0507

See also Georgia Tech. BibRef

Tsukamoto, Y.[Yoshihiko], Matsumoto, Y.[Yusuke], Wada, T.[Toshikazu],
Tracking a firefly: A stable likelihood estimation for variable appearance object tracking-,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Sakagaito, J.Y.[Jun-Ya], Wada, T.[Toshikazu],
Nearest First Traversing Graph for Simultaneous Object Tracking and Recognition,
CVPR07(1-7).
IEEE DOI 0706
BibRef

Wada, T.[Toshikazu], Sakagaito, J.Y.[Jun-Ya], Kato, T.[Takekazu],
Simultaneous Object Tracking and Recognition by Nearest Neighbor Traversing Graph,
CREST05(154-161).
WWW Link. 0505
BibRef

Stauffer, C.[Chris],
Learning to Track Objects Through Unobserved Regions,
Motion05(II: 96-102).
IEEE DOI 0502
BibRef

Stauffer, C.[Chris],
Learning a Factorized Segmental Representation of Far-Field Tracking Data,
EventVideo04(115).
IEEE DOI 0502
BibRef

Moustakas, K., Tzovaras, D., Strintzis, M.G.,
A non causal bayesian framework for object tracking and occlusion handling for the synthesis of stereoscopic video,
3DPVT04(147-154).
IEEE DOI 0412
Occlusions for gait. BibRef

Kang, J.M.[Jin-Man], Cohen, I., Medioni, G.,
Object reacquisition using invariant appearance model,
ICPR04(IV: 759-762).
IEEE DOI 0409
BibRef

Vo, B., Ma, W.K.,
Joint detection and tracking of multiple maneuvering targets in clutter using random finite sets,
ICARCV04(II: 1485-1490).
IEEE DOI 0412
BibRef

Takada, C.[Chika], Sugaya, Y.[Yasuyuki],
Detecting Incorrect Feature Tracking by Affine Space Fitting,
PSIVT09(191-202).
Springer DOI 0901
BibRef

Sugaya, Y.[Yasuyuki], Kanatani, K.[Kenichi],
Extending Interrupted Feature Point Tracking for 3-D Affine Reconstruction,
ECCV04(Vol I: 310-321).
Springer DOI 0405
Deal with occlusions in tracking.
See also Optimizing a Triangular Mesh for Shape Reconstruction from Images. BibRef

Wildenauer, H., Melzer, T., Bischof, H.,
A gradient-based eigenspace approach to dealing with occlusions and non-gaussian noise,
ICPR02(II: 977-980).
IEEE DOI 0211
BibRef

Gao, J., Kak, A.,
Multi-frame based motion estimation for semantic object tracking in the presence of occlusion,
ICIP02(III: 881-884).
IEEE DOI 0210
BibRef

Marcenaro, L., Ferrari, M., Marcbesotti, L., Regazzoni, C.S.,
Multiple object tracking under heavy occlusions by using kalman filters based on shape matching,
ICIP02(III: 341-344).
IEEE DOI 0210
BibRef

Luo, Z.X.[Zhong-Xiang], Zhuang, Y.T.[Yue-Ting], Liu, F.[Feng], Pan, Y.H.[Yun-He],
Incomplete motion feature tracking algorithm in video sequences,
ICIP02(III: 617-620).
IEEE DOI 0210
BibRef

Loutas, E., Diamantaras, K.I., Pitas, I.,
Occlusion Resistant Object Tracking,
ICIP01(II: 65-68).
IEEE DOI 0108
BibRef

Brill, F.Z., Martin, W.N., Olson, T.J.,
Markers Elucidated and Applied in Local 3-Space,
SCV95(49-54).
IEEE DOI U. of Virginia. Markers -- objects being tracked, which may be occluded. BibRef 9500

Oberti, F.[Franco], Regazzoni, C.S.[Carlo S.],
Adaptive Tracking of Multiple Non Rigid Objects in Cluttered Scenes,
ICPR00(Vol III: 1096-1099).
IEEE DOI 0009
BibRef

Sherrah, J.[Jamie], Gong, S.G.[Shao-Gang],
Resolving Visual Uncertainty and Occlusion through Probabilistic Reasoning,
BMVC00(xx-yy).
PDF File. 0009
BibRef
And:
Tracking Discontinuous Motion using Bayesian Inference,
ECCV00(II: 150-166).
Springer DOI 0003
BibRef

Gidas, B., Robertson, C., de Almeida, M.P.[Murilo Pereira],
Tracking of Moving Objects in Cluttered Environments Via Monte Carlo Filter,
ICPR00(Vol I: 175-178).
IEEE DOI 0009
BibRef

Baker, K.D., Grove, T.[Tom], Tan, T.N.,
Colour Based Object Tracking,
ICPR98(Vol II: 1442-1444).
IEEE DOI 9808
Track using color histogram, not blobs. BibRef

Bonnaud, L., and Labit, C.,
Multiple Occluding Objects Tracking Using a Non-Redundant Boundary-Based Representation for Image Sequence Interpolation After Decoding,
ICIP97(II: 426-429).
IEEE DOI BibRef 9700

Mae, Y.S.[Yasu-Shi], Shirai, Y.[Yoshiaki],
Tracking Moving Object in 3-D Space Based on Optical Flow and Edges,
ICPR98(Vol II: 1439-1441).
IEEE DOI 9808
BibRef

Mae, Y., Shirai, Y., Miura, J., Kuno, Y.,
Object Tracking in Cluttered Background Based on Optical Flow and Edges,
ICPR96(I: 196-200).
IEEE DOI 9608
(Osaka Univ., J) BibRef

Cooper, P.R.[Paul R.], Birnbaum, L.A.[Lawrence A.], Halabe, D.[Daniel], Brand, M.[Matthew], Prokopowicz, P.N.[Peter N.],
Divided we fall: Resolving occlusions using causal reasoning,
ECCV94(A:535-540).
Springer DOI 9405
BibRef

Gordon, G.L.,
On the Tracking of Featureless Objects with Occlusion,
Motion89(13-20). Objects (blobs) are tracked using size for matching. BibRef 8900

Ward, M.O., Chien, Y.T.,
Occlusion Analysis in Time-Varying Imagery,
PRIP81(504-507). BibRef 8100

Thompson, W.B., Whillock, R.P.,
Occlusion-Sensitive Matching,
ICCV88(285-289).
IEEE DOI BibRef 8800

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Target Tracking Techniques, Particle Filter Techniques .


Last update:Mar 16, 2024 at 20:36:19