Liu, Y.,
Zheng, Y.F.,
Video Object Segmentation and Tracking Using psi-Learning
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CirSysVideo(15), No. 7, July 2005, pp. 885-899.
IEEE DOI
0508
BibRef
Shen, C.F.[Chun-Feng],
Lin, X.Y.[Xue-Yin],
Shi, Y.C.[Yuan-Chun],
Moving object tracking under varying illumination conditions,
PRL(27), No. 14, 15 October 2006, pp. 1632-1643.
Elsevier DOI
0609
BibRef
Fusion of Texture Variation and On-Line Color Sampling for Moving
Object Detection Under Varying Chromatic Illumination,
ACCV06(I:90-99).
Springer DOI
0601
Tracking, Color model, Texture model, Illumination variation, Level set
BibRef
Vidal, R.[René],
Ma, Y.[Yi],
A Unified Algebraic Approach to 2-D and 3-D Motion Segmentation and
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JMIV(25), No. 3, October 2006, pp. 403-421.
Springer DOI
0611
BibRef
Earlier:
A Unified Algebraic Approach to 2-D and 3-D Motion Segmentation,
ECCV04(Vol I: 1-15).
Springer DOI
0405
Award, ECCV, HM.
BibRef
Rao, S.R.[Shankar R.],
Tron, R.[Roberto],
Vidal, R.[Rene],
Ma, Y.[Yi],
Motion Segmentation in the Presence of Outlying, Incomplete, or
Corrupted Trajectories,
PAMI(32), No. 10, October 2010, pp. 1832-1845.
IEEE DOI
1008
Code, Motion Segmentation.
BibRef
Earlier:
Motion segmentation via robust subspace separation in the presence of
outlying, incomplete, or corrupted trajectories,
CVPR08(1-8).
IEEE DOI
0806
Segmenting tracked trajectories, occlusions, deformations lead
to problems. Develop robust separation scheme to deal with these issues.
Related to lossy compression.
Code is available.
WWW Link.
BibRef
Vidal, R.[René],
Tron, R.[Roberto],
Hartley, R.I.[Richard I.],
Multiframe Motion Segmentation with Missing Data Using
PowerFactorization and GPCA,
IJCV(79), No. 1, August 2008, pp. xx-yy.
Springer DOI
0804
BibRef
Earlier: A1, A3, Only:
Motion segmentation with missing data using powerfactorization and GPCA,
CVPR04(II: 310-316).
IEEE DOI
0408
See also Generalized Principal Component Analysis (GPCA).
See also Minimum Effective Dimension for Mixtures of Subspaces: A Robust GPCA Algorithm and its Applications.
See also Three-View Multibody Structure from Motion.
BibRef
Vidal, R.,
Sastry, S.,
Optimal segmentation of dynamic scenes from two perspective views,
CVPR03(II: 281-286).
IEEE DOI
0307
BibRef
Earlier:
Segmentation of dynamic scenes from image intensities,
Motion02(44-49).
IEEE DOI
0303
BibRef
Xu, F.[Feng],
Lam, K.M.[Kin-Man],
Dai, Q.H.[Qiong-Hai],
Video-object segmentation and 3D-trajectory estimation for monocular
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IVC(29), No. 2-3, February 2011, pp. 190-205.
Elsevier DOI
1101
2D-to-3D video conversion, 3D trajectory estimation, Video-object segmentation
BibRef
Wang, Z.J.[Zhi-Jie],
Salah, M.B.[Mohamed Ben],
Zhang, H.[Hong],
Object joint detection and tracking using adaptive multiple motion
models,
VC(30), No. 2, February 2014, pp. 173-187.
WWW Link.
1402
BibRef
Earlier: A1, A3, Only:
Object Detection with Multiple Motion Models,
ACCV09(III: 183-192).
Springer DOI
0909
BibRef
Liwicki, S.[Stephan],
Zafeiriou, S.P.[Stefanos P.],
Pantic, M.[Maja],
Online Kernel Slow Feature Analysis for Temporal Video Segmentation
and Tracking,
IP(24), No. 10, October 2015, pp. 2955-2970.
IEEE DOI
1507
BibRef
Earlier:
Incremental Slow Feature Analysis with Indefinite Kernel for Online
Temporal Video Segmentation,
ACCV12(II:162-176).
Springer DOI
1304
Eigenvalues and eigenfunctions
See also Learning Slow Features for Behaviour Analysis.
See also Slow Feature Analysis for Human Action Recognition.
BibRef
Lai, Y.,
Yang, C.,
Video Object Retrieval by Trajectory and Appearance,
CirSysVideo(25), No. 6, June 2015, pp. 1026-1037.
IEEE DOI
1506
Adaptive optics
BibRef
Chen, L.,
Shen, J.,
Wang, W.,
Ni, B.,
Video Object Segmentation Via Dense Trajectories,
MultMed(17), No. 12, December 2015, pp. 2225-2234.
IEEE DOI
1512
Clustering algorithms
BibRef
Luo, Y.[Ye],
Yuan, J.S.[Jun-Song],
Lu, J.W.[Jian-Wei],
Finding spatio-temporal salient paths for video objects discovery,
JVCIR(38), No. 1, 2016, pp. 45-54.
Elsevier DOI
1605
Spatio-temporal path
BibRef
Mahmood, M.H.[Muhammad Habib],
Díez, Y.[Yago],
Salvi, J.[Joaquim],
Lladó, X.[Xavier],
A collection of challenging motion segmentation benchmark datasets,
PR(61), No. 1, 2017, pp. 1-14.
Elsevier DOI
1705
Dataset, Motion Segmentation. Motion segmentation
BibRef
Mahmood, M.H.[Muhammad Habib],
Zappella, L.[Luca],
Díez, Y.[Yago],
Salvi, J.[Joaquim],
Lladó, X.[Xavier],
A New Trajectory Based Motion Segmentation Benchmark Dataset (UdG-MS15),
IbPRIA15(463-470).
Springer DOI
1506
Dataset, Motion Segmentation.
BibRef
Wang, Y.,
Liu, Y.,
Blasch, E.,
Ling, H.,
Simultaneous Trajectory Association and Clustering for Motion
Segmentation,
SPLetters(25), No. 1, January 2018, pp. 145-149.
IEEE DOI
1801
image motion analysis, image segmentation, image sequences,
iterative methods, optimisation, pattern clustering, tensors,
tensor-based association
BibRef
Lu, Y.C.[Yu-Cheng],
Kim, D.W.[Dong-Wook],
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1912
Dual-side cameras, Foreground extraction,
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BibRef
Fan, Y.C.[Ying-Chun],
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Tang, Y.L.[Yu-Liang],
Zhi, T.[Tao],
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PRL(127), 2019, pp. 191-201.
Elsevier DOI
1911
SLAM, Dynamic objects, Trajectory, Navigate, Image fusion
BibRef
Masuda, M.,
Mochizuki, Y.,
Ishikawa, H.,
Unsupervised video object segmentation by supertrajectory labeling,
MVA17(448-451)
DOI Link
1708
Color, Labeling, Motion segmentation, Object segmentation,
Optimization, Robustness, Trajectory
BibRef
Zhai, M.Y.[Meng-Yao],
Chen, L.[Lei],
Li, J.L.[Jin-Ling],
Khodabandeh, M.[Mehran],
Mori, G.[Greg],
Object detection in surveillance video from dense trajectories,
MVA15(535-538)
IEEE DOI
1507
Bandwidth
BibRef
Shi, F.[Feng],
Zhou, Z.[Zhong],
Xiao, J.J.[Jiang-Jian],
Wu, W.[Wei],
Robust Trajectory Clustering for Motion Segmentation,
ICCV13(3088-3095)
IEEE DOI
1403
BibRef
Trichet, R.[Remi],
Nevatia, R.[Ramakant],
Burns, B.[Brian],
Video event classification with temporal partitioning,
AVSS15(1-6)
IEEE DOI
1511
Animals
BibRef
Trichet, R.[Remi],
Nevatia, R.[Ramakant],
Video segmentation and feature co-occurrences for activity
classification,
WACV14(385-392)
IEEE DOI
1406
BibRef
Earlier:
Video segmentation with spatio-temporal tubes,
AVSS13(330-335)
IEEE DOI
1311
Long-term temporal interactions among objects.
based on dense trajectory clustering.
Context.
BibRef
Narayan, S.[Sanath],
Ramakrishnan, K.R.,
Motion segmentation using curve fitting on Lagrangian particle
trajectories,
ICPR12(3692-3695).
WWW Link.
1302
BibRef
Zhang, G.[Geng],
Yuan, Z.[Zejian],
Chen, D.P.[Da-Peng],
Liu, Y.H.[Yue-Hu],
Zheng, N.N.[Nan-Ning],
Video object segmentation by clustering region trajectories,
ICPR12(2598-2601).
WWW Link.
1302
BibRef
Vishnyakov, B.V.,
Vizilter, Y.V.,
Knyaz, V.,
Spectrum-based Object Detection and Tracking Technique for Digital
Video Surveillance,
ISPRS12(XXXIX-B3:579-583).
DOI Link
1209
BibRef
Kim, C.,
Li, F.X.[Fu-Xin],
Ciptadi, A.,
Rehg, J.M.,
Multiple Hypothesis Tracking Revisited,
ICCV15(4696-4704)
IEEE DOI
1602
Computational modeling
BibRef
Li, F.X.[Fu-Xin],
Kim, T.Y.[Tae-Young],
Humayun, A.[Ahmad],
Tsai, D.[David],
Rehg, J.M.[James M.],
Video Segmentation by Tracking Many Figure-Ground Segments,
ICCV13(2192-2199)
IEEE DOI
1403
CPMC
BibRef
Beaugendre, A.[Axel],
Zhang, C.Y.[Chen-Yuan],
Xu, J.[Jiu],
Goto, S.[Satoshi],
Enhanced moving object detection using tracking system for video
surveillance purposes,
VCIP12(1-6).
IEEE DOI
1302
BibRef
Jain, R.,
Sankar, K.P.,
Jawahar, C.V.,
Interpolation Based Tracking for Fast Object Detection in Videos,
NCVPRIPG11(102-105).
IEEE DOI
1205
BibRef
Huang, C.C.[Ching-Chun],
Wang, S.J.[Sheng-Jyh],
A cascaded hierarchical framework for moving object detection and
tracking,
ICIP10(4629-4632).
IEEE DOI
1009
BibRef
Lu, W.C.[Wang-Chou],
Wang, Y.C.F.,
Chen, C.S.[Chu-Song],
Learning Dense Optical-Flow Trajectory Patterns for Video Object
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AVSS10(315-322).
IEEE DOI
1009
BibRef
Pinho da Silva, N.[Nuno],
Costeira, J.P.[Joaao Paulo],
The Normalized Subspace Inclusion:
Robust clustering of motion subspaces,
ICCV09(1444-1450).
IEEE DOI
0909
point trajectories for clustering into objects.
BibRef
Baugh, G.[Gary],
Kokaram, A.[Anil],
Semi-automatic motion based segmentation using long term motion
trajectories,
ICIP10(3009-3012).
IEEE DOI
1009
BibRef
Hernandez, J.[Josue],
Morita, H.[Hiroshi],
Nakano-Miytake, M.[Mariko],
Perez-Meana, H.M.[Hector M.],
Movement Detection and Tracking Using Video Frames,
CIARP09(1054-1061).
Springer DOI
0911
BibRef
Guler, S.[Sadiye],
Silverstein, J.A.[Jason A.],
Pushee, I.H.[Ian H.],
Stationary objects in multiple object tracking,
AVSBS07(248-253).
IEEE DOI
0709
BibRef
Huang, K.Q.[Kai-Qi],
Wang, L.S.[Liang-Sheng],
Tan, T.N.[Tie-Niu],
Detecting and Tracking Distant Objects at Night Based on Human Visual
System,
ACCV06(II:822-831).
Springer DOI
0601
Measure spatio-temporal contrast change
BibRef
Xu, M.[Min],
Niu, R.X.[Rui-Xin],
Varshney, P.K.,
Detection and tracking of moving objects in image sequences with
varying illumination,
ICIP04(IV: 2595-2598).
IEEE DOI
0505
BibRef
Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Real-Time Motion Segmentation, Hardware for Motion Detection .