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Maintain the object when it passes behind another.
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See also UCF Parking Lot Tracking.
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PETS01(xx-yy).
0110
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Kalman filtering; Occlusion; Partial observation; BAYESIAN network
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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.
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Shang, L.M.[Li-Min],
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Elsevier DOI
0711
Visual surveillance; Spatial reasoning; Temporal reasoning;
Resolving ambiguity; Continuity
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Tavanai, A.[Aryana],
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ICPR14(2197-2202)
IEEE DOI
1412
Context
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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.
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Yang, M.H.[Ming-Hsuan],
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Zhu, L.[Lin],
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Tracking multiple objects through occlusion with online sampling and
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PR(41), No. 8, August 2008, pp. 2447-2460.
Elsevier DOI
0805
Multiple objects tracking; Occlusion; Online sampling; Position estimation
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Imai, J.I.[Jun-Ichi],
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Visual Tracking in Occlusion Environments by Autonomous Switching of
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IEICE(E91-D), No. 1, January 2008, pp. 86-95.
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0801
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IEICE(E89-D), No. 7, July 2006, pp. 2132-2141.
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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
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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
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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
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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
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AVSBS06(24-24).
IEEE DOI
0611
BibRef
Papadakis, N.[Nicolas],
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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
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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],
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ICIP08(241-244).
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0810
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Lee, J.[Jehoon],
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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
Jahandide, H.[Hamidreza],
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Abrishami Moghaddam, H.[Hamid],
A hybrid motion and appearance prediction model for robust visual
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PRL(33), No. 16, 1 December 2012, pp. 2192-2197.
Elsevier DOI
1210
Visual object tracking; Occlusion; Appearance prediction; Adaptive
Kalman filter
BibRef
Reilly, V.[Vladimir],
Solmaz, B.[Berkan],
Shah, M.[Mubarak],
Shadow Casting Out Of Plane (SCOOP) Candidates for Human and Vehicle
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IJCV(101), No. 2, January 2013, pp. 350-366.
WWW Link.
PDF File.
1302
BibRef
Earlier:
Geometric Constraints for Human Detection in Aerial Imagery,
ECCV10(VI: 252-265).
Springer DOI
1009
BibRef
Reilly, V.[Vladimir],
Idrees, H.[Haroon],
Shah, M.[Mubarak],
Detection and Tracking of Large Number of Targets in Wide Area
Surveillance,
ECCV10(III: 186-199).
Springer DOI
1009
BibRef
Ali, S.[Saad],
Reilly, V.[Vladimir],
Shah, M.[Mubarak],
Motion and Appearance Contexts for Tracking and Re-Acquiring Targets in
Aerial Videos,
CVPR07(1-6).
IEEE DOI
0706
BibRef
Ali, S.[Saad],
Sheikh, Y.A.[Yaser A.],
Hu, M.[Min],
Scovanner, P.[Paul],
COCOA: Alignment, Object Detection, Object Tracking and
Indexing of Aerial Videos,
Online2006,
HTML Version.
HTML Version.
See also Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras.
BibRef
0600
Ali, S.[Saad],
Shah, M.[Mubarak],
COCOA: Tracking in Aerial Imagery,
SPIE(nnnn), March 2006, Airborne Intelligence, Surveillance,
Reconnaissance (ISR) Systems and Applications, Orlando.
PDF File. And for more details on the project.
BibRef
0603
Penne, T.[Thomas],
Tilmant, C.[Christophe],
Chateau, T.[Thierry],
Barra, V.[Vincent],
Markov Chain Monte Carlo Modular Ensemble Tracking,
IVC(31), No. 6-7, June-July 2013, pp. 434-447.
Elsevier DOI
1306
BibRef
Earlier:
Modular Ensemble Tracking,
IPTA10(363-368).
IEEE DOI
1007
Object tracking; Classification; Boosting; Feature spaces;
Particle filtering
BibRef
Chateau, T.[Thierry],
Gay-Belille, V.[Vincent],
Chausse, F.[Frederic],
Lapresté, J.T.[Jean-Thierry],
Real-Time Tracking with Classifiers,
WDV06(218-231).
Springer DOI
0705
BibRef
Chateau, T.,
Lapreste, J.T.,
Real time tracking with occlusion and illumination variations,
ICPR04(IV: 763-766).
IEEE DOI
0409
BibRef
Chateau, T.,
Jurie, F.,
Dhome, M.,
Clady, X.,
Real-Time Tracking Using Wavelet Representation,
DAGM02(523 ff.).
Springer DOI
0303
BibRef
Mei, X.[Xue],
Ling, H.B.[Hai-Bin],
Wu, Y.[Yi],
Blasch, E.P.[Erik P.],
Bai, L.[Li],
Efficient Minimum Error Bounded Particle Resampling L1 Tracker
with Occlusion Detection,
IP(22), No. 7, 2013, pp. 2661-2675.
IEEE DOI
BibRef
1300
Earlier:
Minimum error bounded efficient L1 tracker with occlusion detection,
CVPR11(1257-1264).
IEEE DOI
1106
least squares approximations; particle filtering (numerical methods);
target tracking; BPR-L1 tracker; error bound calculation;
ground mounted perspectives; hallway monitoring; occlusion detection;
pedestrians; wide area motion imagery; visual tracking
1307
BibRef
Liang, P.P.[Peng-Peng],
Blasch, E.P.[Erik P.],
Ling, H.B.[Hai-Bin],
Encoding color information for visual tracking:
Algorithms and benchmark,
IP(24), No. 12, December 2015, pp. 5630-5644.
IEEE DOI
1512
computer vision
BibRef
Wu, Y.[Yi],
Ling, H.B.[Hai-Bin],
Blasch, E.P.[Erik P.],
Bai, L.[Li],
Chen, G.[Genshe],
Visual Tracking Based on Log-Euclidean Riemannian Sparse Representation,
ISVC11(I: 738-747).
Springer DOI
1109
BibRef
Yang, T.[Tao],
Zhang, Y.N.[Yan-Ning],
Tong, X.M.[Xiao-Min],
Zhang, X.Q.[Xiao-Qiang],
Yu, R.[Rui],
A New Hybrid Synthetic Aperture Imaging Model for Tracking and Seeing
People Through Occlusion,
CirSysVideo(23), No. 9, 2013, pp. 1461-1475.
IEEE DOI
1309
BibRef
Earlier:
Continuously tracking and see-through occlusion based on a new hybrid
synthetic aperture imaging model,
CVPR11(3409-3416).
IEEE DOI
1106
Apertures
BibRef
Zhang, X.Q.[Xiao-Qiang],
Zhang, Y.N.[Yan-Ning],
Yang, T.[Tao],
Yang, Y.H.[Yee-Hong],
Synthetic aperture photography using a moving camera-IMU system,
PR(62), No. 1, 2017, pp. 175-188.
Elsevier DOI
1705
Synthetic aperture photography
See also novel multi-object detection method in complex scene using synthetic aperture imaging, A.
BibRef
Ablavsky, V.[Vitaly],
Sclaroff, S.[Stan],
Layered Graphical Models for Tracking Partially Occluded Objects,
PAMI(33), No. 9, September 2011, pp. 1758-1775.
IEEE DOI
1109
BibRef
Earlier:
Add A2:
Thangali, A.[Ashwin],
CVPR08(1-8).
IEEE DOI
0806
BibRef
Cannons, K.J.[Kevin J.],
Wildes, R.P.[Richard P.],
The Applicability of Spatiotemporal Oriented Energy Features to
Region Tracking,
PAMI(36), No. 4, April 2014, pp. 784-796.
IEEE DOI
1404
BibRef
Earlier:
Spatiotemporal Oriented Energy Features for Visual Tracking,
ACCV07(I: 532-543).
Springer DOI
0711
Track oriented features. Especially in clutter.
Histograms
BibRef
Cannons, K.J.[Kevin J.],
Gryn, J.M.[Jacob M.],
Wildes, R.P.[Richard P.],
Visual Tracking Using a Pixelwise Spatiotemporal Oriented Energy
Representation,
ECCV10(IV: 511-524).
Springer DOI
1009
BibRef
Wildes, R.P.,
Bergen, J.,
Qualitative Spatiotemporal Analysis Using an Oriented Energy
Representation,
ECCV00(II: 768-784).
Springer DOI
0003
BibRef
Kim, S.H.[Sung-Ho],
Lee, J.[Joohyoung],
Scale invariant small target detection by optimizing
signal-to-clutter ratio in heterogeneous background for infrared
search and track,
PR(45), No. 1, 2012, pp. 393-406.
Elsevier DOI
1410
Small target
BibRef
Zarezade, A.,
Rabiee, H.R.,
Soltani-Farani, A.,
Khajenezhad, A.,
Patchwise Joint Sparse Tracking With Occlusion Detection,
IP(23), No. 10, October 2014, pp. 4496-4510.
IEEE DOI
1410
Markov processes
BibRef
Makris, A.,
Prieur, C.,
Bayesian Multiple-Hypothesis Tracking of Merging and Splitting
Targets,
GeoRS(52), No. 12, December 2014, pp. 7684-7694.
IEEE DOI
1410
Bayes methods
BibRef
Yang, T.Y.[Tian-Yu],
Li, B.[Baopu],
Meng, M.Q.H.,
Robust Object Tracking With Reacquisition Ability Using Online
Learned Detector,
Cyber(44), No. 11, November 2014, pp. 2134-2142.
IEEE DOI
1411
image matching
BibRef
Yang, Y.C.[Yan-Chao],
Sundaramoorthi, G.[Ganesh],
Shape Tracking with Occlusions via Coarse-to-Fine Region-Based
Sobolev Descent,
PAMI(37), No. 5, May 2015, pp. 1053-1066.
IEEE DOI
1504
BibRef
Earlier:
Modeling Self-Occlusions in Dynamic Shape and Appearance Tracking,
ICCV13(201-208)
IEEE DOI
1403
Joints.
dis-occlusions
BibRef
Yang, Y.C.[Yan-Chao],
Sundaramoorthi, G.[Ganesh],
Soatto, S.[Stefano],
Self-Occlusions and Disocclusions in Causal Video Object Segmentation,
ICCV15(4408-4416)
IEEE DOI
1602
Computer vision
BibRef
Meshgi, K.[Kourosh],
Ishii, S.[Shin],
The State-of-the-Art in Handling Occlusions for Visual Object Tracking,
IEICE(E98-D), No. 7, July 2015, pp. 1260-1274.
WWW Link.
1508
BibRef
And:
Expanding histogram of colors with gridding to improve tracking
accuracy,
MVA15(475-479)
IEEE DOI
1507
Histograms
BibRef
Meshgi, K.[Kourosh],
Maeda, S.I.[Shin-Ichi],
Oba, S.[Shigeyuki],
Skibbe, H.[Henrik],
Li, Y.Z.[Yu-Zhe],
Ishii, S.[Shin],
An occlusion-aware particle filter tracker to handle complex and
persistent occlusions,
CVIU(150), No. 1, 2016, pp. 81-94.
Elsevier DOI
1608
Particle filter tracker
BibRef
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
BibRef
Earlier:
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,
IVC(43), No. 1, 2015, pp. 39-49.
Elsevier DOI
1512
BibRef
Earlier:
Structure-aware keypoint tracking for partial occlusion handling,
WACV14(877-884)
IEEE DOI
1406
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
BibRef
Attari, M.,
Luo, Z.,
Habibi, S.,
An SVSF-Based Generalized Robust Strategy for Target Tracking in
Clutter,
ITS(17), No. 5, May 2016, pp. 1381-1392.
IEEE DOI
1605
Clutter
BibRef
Derue, F.X.[François-Xavier],
Bilodeau, G.A.[Guillaume-Alexandre],
Bergevin, R.[Robert],
SPiKeS: Superpixel-Keypoints structure for robust visual tracking,
MVA(29), No. 1, January 2018, pp. 175-186.
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
BibRef
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.
DOI Link
2204
Interference from similar targets, occlusions, size changes.
BibRef
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
BibRef
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
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
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
BibRef
Yao, Y.[Yue],
Gedeon, T.[Tom],
Zheng, L.[Liang],
Large-scale Training Data Search for Object Re-identification,
CVPR23(15568-15578)
IEEE DOI
2309
BibRef
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
BibRef
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
BibRef
And:
Object-aware tracking,
ICPR16(1695-1700)
IEEE DOI
1705
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
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1408
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CVPR12(1830-1837).
IEEE DOI
1208
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A blob representation for tracking robust to merging and fragmentation,
WACV12(161-168).
IEEE DOI
1203
BibRef
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Han, B.H.[Bo-Hyung],
Visual Tracking by Sampling Tree-Structured Graphical Models,
ECCV14(I: 1-16).
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1408
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Nam, H.[Hyeonseob],
Han, B.H.[Bo-Hyung],
Learning Multi-domain Convolutional Neural Networks for Visual
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CVPR16(4293-4302)
IEEE DOI
1612
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Han, B.H.[Bo-Hyung],
Online Graph-Based Tracking,
ECCV14(V: 112-126).
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1408
BibRef
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
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Kwak, S.[Suha],
Nam, W.H.[Woon-Hyun],
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ICCV11(1551-1558).
IEEE DOI
1201
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Robust real-time object tracking under background clutter,
ICIIP11(1-6).
IEEE DOI
1112
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ICARCV10(1051-1056).
IEEE DOI
1109
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MLVMA11(1-8).
IEEE DOI
1106
BibRef
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ICPR10(2077-2080).
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1008
To deal with occlusions.
BibRef
Grabner, H.[Helmut],
Matas, J.G.[Jiri G.],
Van Gool, L.J.[Luc J.],
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IEEE DOI
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Self Occlusions and Graph Based Edge Measurement Schemes for Visual
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DICTA09(416-423).
IEEE DOI
0912
BibRef
Earlier:
Measurement Function Design for Visual Tracking Applications,
ICPR06(I: 789-792).
IEEE DOI
0609
BibRef
Meng, G.[Gang],
Jiang, Z.G.[Zhi-Guo],
Zhao, D.[Danpei],
Ye, K.[Keren],
Real-Time Illumination Robust Maneuvering Target Tracking Based on
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CISP09(1-5).
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0910
BibRef
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0909
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Bianchi, L.[Luca],
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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.
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Commentary Paper 1 on 'A Probabilistic Bayesian Framework for
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AVSBS08(116-116).
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0809
See also Probabilistic Bayesian Framework for Model-Based Object Tracking Using Undecimated Wavelet Packet Descriptors, A.
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0809
See also Probabilistic Bayesian Framework for Model-Based Object Tracking Using Undecimated Wavelet Packet Descriptors, A.
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Indirect Tracking to Reduce Occlusion Problems,
ISVC08(II: 224-235).
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0812
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Miller, P.,
Where is It? Object Reacquisition in Surveillance Video,
IMVIP08(182-187).
IEEE DOI
0809
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Joshi, N.[Neel],
Avidan, S.[Shai],
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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
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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
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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).
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0509
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Guha, P.[Prithwijit],
Mukerjee, A.[Amitabha],
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Formulation, detection and application of occlusion states (Oc-7) in
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AVSBS11(191-196).
IEEE DOI
1111
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Guha, P.[Prithwijit],
Mukerjee, A.[Amitabha],
Venkatesh, K.S.,
Efficient occlusion handling for multiple agent tracking by reasoning
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PETS05(49-56).
IEEE DOI
0602
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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
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Motion05(II: 90-95).
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0502
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Apostoloff, N.[Nicholas],
Fitzgibbon, A.W.[Andrew W.],
Learning Spatiotemporal T-Junctions for Occlusion Detection,
CVPR05(II: 553-559).
IEEE DOI
0507
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Huang, Y.[Yan],
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Tracking Multiple Objects through Occlusions,
CVPR05(II: 1051-1058).
IEEE DOI
WWW Link.
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0507
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And:
CVPR05(II: 1182).
IEEE DOI
0507
See also Georgia Tech.
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Tsukamoto, Y.[Yoshihiko],
Matsumoto, Y.[Yusuke],
Wada, T.[Toshikazu],
Tracking a firefly: A stable likelihood estimation for variable
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ICPR08(1-4).
IEEE DOI
0812
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Sakagaito, J.Y.[Jun-Ya],
Wada, T.[Toshikazu],
Nearest First Traversing Graph for Simultaneous Object Tracking and
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CVPR07(1-7).
IEEE DOI
0706
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Wada, T.[Toshikazu],
Sakagaito, J.Y.[Jun-Ya],
Kato, T.[Takekazu],
Simultaneous Object Tracking and Recognition by Nearest Neighbor
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CREST05(154-161).
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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
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3DPVT04(147-154).
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0412
Occlusions for gait.
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Kang, J.M.[Jin-Man],
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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
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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
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ICPR02(II: 977-980).
IEEE DOI
0211
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Gao, J.,
Kak, A.,
Multi-frame based motion estimation for semantic object tracking in the
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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
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ICIP02(III: 341-344).
IEEE DOI
0210
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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
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Brill, F.Z.,
Martin, W.N.,
Olson, T.J.,
Markers Elucidated and Applied in Local 3-Space,
SCV95(49-54).
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Oberti, F.[Franco],
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Adaptive Tracking of Multiple Non Rigid Objects in Cluttered Scenes,
ICPR00(Vol III: 1096-1099).
IEEE DOI
0009
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Resolving Visual Uncertainty and Occlusion through Probabilistic
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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.
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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 .