19.3.4.12.1 Foreground Object Extraction, Object Models

Neri, A., Colonnese, S., Russo, G., Talone, P.,
Automatic Moving Object and Background Separation,
SP(66), No. 2, April 1998, pp. 219-232. 9807
BibRef

Sullivan, J., Blake, A., Isard, M., MacCormick, J.P.,
Bayesian Object Localisation in Images,
IJCV(44), No. 2, September 2001, pp. 111-135.
DOI Link 0110
BibRef
Earlier:
Object Localization by Bayesian Correlation,
ICCV99(1068-1075).
IEEE DOI Model both foreground (the object) and the background. Apply a bank of (approximately) independent filters, learn background/foreground distributions. BibRef

Sullivan, J., Blake, A., Rittscher, J.,
Statistical Foreground Modelling for Object Localisation,
ECCV00(II: 307-323).
Springer DOI 0003
BibRef

Cho, J.H.[Ju-Hyun], Kim, S.D.[Seong-Dae],
Object detection using multi-resolution mosaic in image sequences,
SP:IC(20), No. 3, March 2005, pp. 233-253.
Elsevier DOI 0501
BibRef

Chang, G.H.[Gyu-Hwan], Jung, H.M.[Hae-Mook], Kim, S.D.[Seong-Dae], Choi, J.G.[Jae-Gark], Lee, S.W.[Si-Woong], Cho, S.J.[Soon-Jae],
Method for segmenting and estimating a moving object motion using a hierarchy of motion models,
US_Patent5,734,737, Mar 31, 1998
WWW Link. Frame difference, region segmentation. BibRef 9803

Cooper, F.J.[Frederick J.], Skarbo, R.A.[Rune A.],
Method of presence detection using video input,
US_Patent5,892,856, Apr 6, 1999
WWW Link. BibRef 9904

Hillman, P., Hannah, J., Renshaw, D.,
Semi-automatic foreground/background segmentation of motion picture images and image sequences,
VISP(152), No. 4, August 2005, pp. 387-397.
DOI Link 0512
BibRef
Earlier:
Alpha Channel Estimation in High Resolution Images and Image Sequences,
CVPR01(I:1063-1068).
IEEE DOI 0110
Segment actor from background. BibRef

Butler, D.E.[Darren E.], Bove, Jr., V.M.[V. Michael], Sridharan, S.[Sridha],
Real-Time Adaptive Foreground/Background Segmentation,
JASP(2005), No. 14, 2005, pp. 2292-2304.
WWW Link. 0603
BibRef

Kolmogorov, V.[Vladimir], Criminisi, A.[Antonio], Blake, A.[Andrew], Cross, G.[Geoffrey], Rother, C.[Carsten],
Probabilistic Fusion of Stereo with Color and Contrast for Bi-Layer Segmentation,
IJCV(76), No. 2, February 2008, pp. 107.
Springer DOI 0801
Part of the CVPR 2005 Award Issue. BibRef
And: PAMI(28), No. 9, September 2006, pp. 1480-1492.
IEEE DOI 0608
BibRef
Earlier: CVPR05(II: 407-414).
IEEE DOI 0507
Award, CVPR. BibRef
And:
Bi-Layer Segmentation of Binocular Stereo Video,
CVPR05(II: 1186).
IEEE DOI 0507
Award, CVPR, HM. Real-time segmentation of foreground/background in stereo video. Two different sets, fuse stereo match with color to get layers, fuse color with stereo hypotheses. BibRef

Wang, Y.[Yi],
Implementation of Bilayer Segmentation of Live Video,
OnlineCaltech 2006.
WWW Link. Code, Motion Segmentation.
See also Probabilistic Fusion of Stereo with Color and Contrast for Bi-Layer Segmentation. BibRef 0600

Grundmann, M.[Matthias], Kwatra, V.[Vivek], Han, M.[Mei], Essa, I.A.[Irfan A.],
Efficient hierarchical graph-based video segmentation,
CVPR10(2141-2148).
IEEE DOI 1006
BibRef

Yin, P.[Pei], Criminisi, A.[Antonio], Winn, J.[John], Essa, I.A.[Irfan A.],
Bilayer Segmentation of Webcam Videos Using Tree-Based Classifiers,
PAMI(33), No. 1, January 2011, pp. 30-42.
IEEE DOI 1011
BibRef
Earlier:
Tree-based Classifiers for Bilayer Video Segmentation,
CVPR07(1-8).
IEEE DOI 0706
Automatic segmentation for video, monocular camera, approximate depth segmentation form stereo. Frames into foreground/background layers. BibRef

Criminisi, A., Cross, G., Blake, A., Kolmogorov, V.[Vladimir],
Bilayer Segmentation of Live Video,
CVPR06(I: 53-60).
IEEE DOI 0606
BibRef

Nanda, P.K.[Pradipta Kumar], Ghosh, A.[Asish], Kanungo, P.[Priyadarshi],
Classification of Objects and Background Using Parallel Genetic Algorithm Based Clustering,
ELCVIA(6), No. 3, 2007, pp. 42-53.
DOI Link 0803
BibRef

Zhang, X.[Xiang], Yang, J.[Jie],
Moving object detection based on shape prediction,
JOSA-A(26), No. 2, February 2009, pp. 342-349.
WWW Link. 0902
BibRef

Jiang, H.T.[Hong-Tu], Owall, V., Ardo, H.[Hakan],
A Hardware Architecture for Real-Time Video Segmentation Utilizing Memory Reduction Techniques,
CirSysVideo(19), No. 2, February 2009, pp. 226-236.
IEEE DOI 0902
BibRef
Earlier: A1, A3, A2:
Real-Time Video Segmentation with VGA Resolution and Memory Bandwidth Reduction,
AVSBS06(104-104).
IEEE DOI 0611
BibRef

Kristensen, F.[Fredrik], Nilsson, P.[Peter], Öwall, V.[Viktor],
Background Segmentation Beyond RGB,
ACCV06(II:602-612).
Springer DOI 0601
BibRef

El Baf, F.[Fida], and Bouwmans, T.[Thierry],
Robust fuzzy statistical modeling of dynamic backgrounds in IR videos,
SPIE(Newsroom), November 13, 2009.
DOI Link 0911
Taking into account uncertainties in classification results in an improved alternative to conventional means of detecting moving objects. BibRef

Bouwmans, T.[Thierry], Zahzah, E.H.[El Hadi],
Robust PCA via Principal Component Pursuit: A review for a comparative evaluation in video surveillance,
CVIU(122), No. 1, 2014, pp. 22-34.
Elsevier DOI 1404
Foreground detection BibRef

Guyon, C.[Charles], Bouwmans, T.[Thierry], Zahzah, E.H.[El-Hadi],
Foreground Detection via Robust Low Rank Matrix Decomposition Including Spatio-Temporal Constraint,
BMC12(I:315-320).
Springer DOI 1304
BibRef
And:
Foreground detection based on low-rank and block-sparse matrix decomposition,
ICIP12(1225-1228).
IEEE DOI 1302
BibRef
And:
Foreground detection via robust low rank matrix factorization including spatial constraint with Iterative reweighted regression,
ICPR12(2805-2808).
WWW Link. 1302
BibRef
And:
Moving Object Detection via Robust Low Rank Matrix Decomposition with Irls Scheme,
ISVC12(I: 665-674).
Springer DOI 1209
BibRef
And:
Moving Object Detection by Robust PCA Solved via a Linearized Symmetric Alternating Direction Method,
ISVC12(I: 427-436).
Springer DOI 1209
BibRef
And:
Foreground Detection by Robust PCA Solved via a Linearized Alternating Direction Method,
ICIAR12(I: 115-122).
Springer DOI 1206
Robust PCA to avoid blending object to background BibRef

El Baf, F.[Fida], Bouwmans, T.[Thierry], Vachon, B.[Bertrand],
Fuzzy statistical modeling of dynamic backgrounds for moving object detection in infrared videos,
OTCBVS09(60-65).
IEEE DOI 0906
BibRef
Earlier:
Type-2 Fuzzy Mixture of Gaussians Model: Application to Background Modeling,
ISVC08(I: 772-781).
Springer DOI 0812
BibRef
And:
Fuzzy foreground detection for infrared videos,
OTCBVS08(1-6).
IEEE DOI 0806
BibRef
And:
A fuzzy approach for background subtraction,
ICIP08(2648-2651).
IEEE DOI 0810
BibRef

Wang, L.[Lu], Yung, N.H.C.[Nelson H.C.],
Extraction of Moving Objects From Their Background Based on Multiple Adaptive Thresholds and Boundary Evaluation,
ITS(11), No. 1, March 2010, pp. 40-51.
IEEE DOI 1003
BibRef

Bhaskar, H., Mihaylova, L., Achim, A.,
Video Foreground Detection Based on Symmetric Alpha-Stable Mixture Models,
CirSysVideo(20), No. 8, August 2010, pp. 1133-1138.
IEEE DOI 1008
BibRef

Kwak, S.[Sooyeong], Bae, G.[Guntae], Byun, H.R.[Hye-Ran],
Moving-object segmentation using a foreground history map,
JOSA-A(27), No. 2, February 2010, pp. 180-187.
WWW Link. 1002
BibRef

Hyun, M.H.[Myung-Han], Kim, S.Y.[Sung-Yeol], Kang, Y.S.[Yun-Suk], Ho, Y.S.[Yo-Sung],
Multiview foreground extraction and composition to multiview background using trimap sharing for natural 3D scene generation,
IJIST(20), No. 3, September 2010, pp. 285-293.
DOI Link 1008
BibRef

Lee, W.W.[Won-Woo], Woo, W.T.[Woon-Tack], Boyer, E.[Edmond],
Silhouette Segmentation in Multiple Views,
PAMI(33), No. 7, July 2011, pp. 1429-1441.
IEEE DOI 1106
BibRef
Earlier:
Identifying Foreground from Multiple Images,
ACCV07(II: 580-589).
Springer DOI 0711
Consistent foreground regions. Exploit spatial consistency of several projections. BibRef

Caseiro, R.[Rui], Martins, P.[Pedro], Henriques, J.F.[João F.], Batista, J.P.[Jorge P.],
A nonparametric Riemannian framework on tensor field with application to foreground segmentation,
PR(45), No. 11, November 2012, pp. 3997-4017.
Elsevier DOI 1206
BibRef
Earlier: A1, A3, A2, A4: ICCV11(1-8).
IEEE DOI 1201
Nonparametric density estimation, Kernel density estimation, Riemannian geometry, Tensor manifold, Riemannian metrics, Foreground segmentation on tensor field BibRef

Caseiro, R.[Rui], Martins, P.[Pedro], Henriques, J.F.[Joao F.], Leite, F.S.[Fatima Silva], Batista, J.P.[Jorge P.],
Rolling Riemannian Manifolds to Solve the Multi-class Classification Problem,
CVPR13(41-48)
IEEE DOI 1309
Object Classification, Riemannian Manifolds, Rolling motions BibRef

Caseiro, R.[Rui], Henriques, J.F.[João F.], Martins, P.[Pedro], Batista, J.P.[Jorge P.],
Semi-intrinsic Mean Shift on Riemannian Manifolds,
ECCV12(I: 342-355).
Springer DOI 1210
BibRef

Caseiro, R.[Rui], Henriques, J.F.[Joao F.], Batista, J.P.[Jorge P.],
Foreground Segmentation via Background Modeling on Riemannian Manifolds,
ICPR10(3570-3574).
IEEE DOI 1008

See also Globally optimal solution to multi-object tracking with merged measurements. BibRef

Caseiro, R.[Rui], Batista, J.P.[Jorge P.], Martins, P.[Pedro],
Background Modelling on Tensor Field for Foreground Segmentation,
BMVC10(xx-yy).
HTML Version. 1009
BibRef

Gallego, J.[Jaime], Pardàs, M.[Montse], Haro, G.[Gloria],
Enhanced foreground segmentation and tracking combining Bayesian background, shadow and foreground modeling,
PRL(33), No. 12, 1 September 2012, pp. 1558-1568.
Elsevier DOI 1208
BibRef
Earlier:
Bayesian foreground segmentation and tracking using pixel-wise background model and region based foreground model,
ICIP09(3205-3208).
IEEE DOI 0911
Foreground segmentation, Space-color models, Shadow model, Objects tracking, GMM BibRef

Gallego, J.[Jaime], Pardàs, M.[Montse],
Region based foreground segmentation combining color and depth sensors via logarithmic opinion pool decision,
JVCIR(25), No. 1, 2014, pp. 184-194.
Elsevier DOI 1502
BibRef
And:
Multiview foreground segmentation using 3D probabilistic model,
ICIP14(3317-3321)
IEEE DOI 1502
BibRef
Earlier:
Enhanced Bayesian foreground segmentation using Brightness and Color Distortion region-based model for shadow removal,
ICIP10(3449-3452).
IEEE DOI 1009
Foreground segmentation. Cameras BibRef

Gallego, J.[Jaime], Salvador, J.[Jordi], Casas, J.R.[Josep R.], Pardas, M.[Montse],
Joint multi-view foreground segmentation and 3D reconstruction with tolerance loop,
ICIP11(997-1000).
IEEE DOI 1201
BibRef

Tian, Y.L.[Ying Li], Senior, A.[Andrew], Lu, M.[Max],
Robust and efficient foreground analysis in complex surveillance videos,
MVA(23), No. 5, September 2012, pp. 967-983.
WWW Link. 1208
BibRef

Tian, Y.L.[Ying-Li], Lu, M.[Max], Hampapur, A.[Arun],
Robust and Efficient Foreground Analysis for Real-Time Video Surveillance,
CVPR05(I: 1182-1187).
IEEE DOI 0507
BibRef

Sun, S.W.[Shih-Wei], Wang, Y.C.A.F.[Yu-Chi-Ang Frank], Huang, F.[Fay], Liao, H.Y.M.[Hong-Yuan Mark],
Moving foreground object detection via robust SIFT trajectories,
JVCIR(24), No. 3, April 2013, pp. 232-243.
Elsevier DOI 1303
Template matching, Object tracking, Video object segmentation; Foreground segmentation, Background subtraction BibRef

Zhou, X.W.[Xiao-Wei], Yang, C.[Can], Yu, W.C.[Wei-Chuan],
Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation,
PAMI(35), No. 3, March 2013, pp. 597-610.
IEEE DOI 1303
integrates object detection and background learning BibRef

Wan, Y.L.[Yan-Li], Miao, Z.J.[Zhen-Jiang], Zhang, X.P.[Xiao-Ping], Tang, Z.[Zhen], Wang, Z.F.[Zhi-Fei],
Illumination Robust Video Foreground Prediction Based on Color Recovering,
MultMed(16), No. 3, April 2014, pp. 637-652.
IEEE DOI 1405
image colour analysis BibRef

Ji, Z.J.[Zhang-Jian], Wang, W.Q.[Wei-Qiang],
Detect foreground objects via adaptive fusing model in a hybrid feature space,
PR(47), No. 9, 2014, pp. 2952-2961.
Elsevier DOI 1406
Lighting Foreground detection BibRef

Ji, Z.J.[Zhang-Jian], Wang, W.Q.[Wei-Qiang],
Object tracking based on local dynamic sparse model,
JVCIR(28), No. 1, 2015, pp. 44-52.
Elsevier DOI 1503
BibRef
Earlier:
Robust object tracking via multi-task dynamic sparse model,
ICIP14(393-397)
IEEE DOI 1502
Object tracking BibRef

Ji, Z.J.[Zhang-Jian], Wang, W.Q.[Wei-Qiang],
Correlation filter tracker based on sparse regularization,
JVCIR(55), 2018, pp. 354-362.
Elsevier DOI 1809
Object tracking, Correlation filter, Adaptive regularization, Occlusion handling BibRef

Ji, Z.J.[Zhang-Jian], Wang, W.Q.[Wei-Qiang], Lu, K.[Ke],
Robustly tracking objects via multi-task kernel dynamic sparse model,
ICIP15(266-270)
IEEE DOI 1512
BibRef
Earlier:
Extract foreground objects based on sparse model of spatiotemporal spectrum,
ICIP13(3441-3445)
IEEE DOI 1402
Object tracking. Sparse model, Spatiotemporal spectrum, foreground object detection BibRef

He, J.[Jun], Zhang, D.[Dejiao], Balzano, L.[Laura], Tao, T.[Tao],
Iterative Grassmannian optimization for robust image alignment,
IVC(32), No. 10, 2014, pp. 800-813.
Elsevier DOI 1410
BibRef
Earlier:
Iterative online subspace learning for robust image alignment,
FG13(1-8)
IEEE DOI 1309
Robust subspace learning. computer vision. Grassmannian Robust Adaptive Subspace Tracking Algorithm. BibRef

He, J.[Jun], Balzano, L.[Laura], Szlam, A.[Arthur],
Incremental gradient on the Grassmannian for online foreground and background separation in subsampled video,
CVPR12(1568-1575).
IEEE DOI 1208
BibRef

Baktashmotlagh, M.[Mahsa], Harandi, M.T.[Mehrtash T.], Lovell, B.C.[Brian C.], Salzmann, M.[Mathieu],
Discriminative Non-Linear Stationary Subspace Analysis for Video Classification,
PAMI(36), No. 12, December 2014, pp. 2353-2366.
IEEE DOI 1411
BibRef
Earlier:
Domain Adaptation on the Statistical Manifold,
CVPR14(2481-2488)
IEEE DOI 1409
BibRef
Earlier:
Unsupervised Domain Adaptation by Domain Invariant Projection,
ICCV13(769-776)
IEEE DOI 1403
Separate stationary parts of video from what is moving. Algorithm design and analysis. Domain Adaptation, Object Recognition, Statistical Manifold. Training and test with different distributions. BibRef

Pereira, A.[Alex], Saotome, O.[Osamu], Sampaio, D.[Daniel],
Patch-based local histograms and contour estimation for static foreground classification,
JIVP(2015), No. 1, 2015, pp. 6.
DOI Link 1503
BibRef

Erichson, N.B.[N. Benjamin], Donovan, C.[Carl],
Randomized low-rank Dynamic Mode Decomposition for motion detection,
CVIU(146), No. 1, 2016, pp. 40-50.
Elsevier DOI 1604
Dynamic Mode Decomposition BibRef

Kutz, J.N., Fu, X., Brunton, S.L., Erichson, N.B.,
Multi-resolution Dynamic Mode Decomposition for Foreground/Background Separation and Object Tracking,
RSL-CV15(921-929)
IEEE DOI 1602
Eigenvalues and eigenfunctions BibRef

Dong, P.[Pei], Wang, S.S.[Shan-Shan], Xia, Y.[Yong], Liang, D.[Dong], Feng, D.D.[David Dagan],
Foreground Detection With Simultaneous Dictionary Learning and Historical Pixel Maintenance,
IP(25), No. 11, November 2016, pp. 5035-5049.
IEEE DOI 1610
image representation BibRef

Cuevas, C.[Carlos], Martínez, R.[Raquel], García, N.[Narciso],
Detection of stationary foreground objects: A survey,
CVIU(152), No. 1, 2016, pp. 41-57.
Elsevier DOI 1609
Survey, Foreground Objects. Stationary foreground BibRef

Cuevas, C.[Carlos], Yáñez, E.M.[Eva María], García, N.[Narciso],
Labeled dataset for integral evaluation of moving object detection algorithms: LASIESTA,
CVIU(152), No. 1, 2016, pp. 103-117.
Elsevier DOI 1609
Dataset, Foreground Detection. Database BibRef

Cuevas, C.[Carlos], Martínez, R.[Raquel], Berjón, D., García, N.[Narciso],
Detection of Stationary Foreground Objects Using Multiple Nonparametric Background-Foreground Models on a Finite State Machine,
IP(26), No. 3, March 2017, pp. 1127-1142.
IEEE DOI 1703
finite state machines BibRef

Yang, L.[Lei], Pong, T.K.[Ting Kei], Chen, X.J.[Xiao-Jun],
Alternating Direction Method of Multipliers for a Class of Nonconvex and Nonsmooth Problems with Applications to Background/Foreground Extraction,
SIIMS(10), No. 1, 2017, pp. 74-110.
DOI Link 1704
BibRef

Kim, H.[Hyuncheol], Jeon, S.[Semi], Lee, S.K.[Sang-Keun], Paik, J.[Joonki],
Robust Visual Tracking Using Structure-Preserving Sparse Learning,
SPLetters(24), No. 5, May 2017, pp. 707-711.
IEEE DOI 1704
Linear programming BibRef

Xi, T., Zhao, W., Wang, H., Lin, W.,
Salient Object Detection With Spatiotemporal Background Priors for Video,
IP(26), No. 7, July 2017, pp. 3425-3436.
IEEE DOI 1706
Computational modeling, Feature extraction, Image color analysis, Image segmentation, Object detection, Spatiotemporal phenomena, Visualization, Salient object detection, background priors, geodesic distance, video saliency. BibRef

Domadiya, P.[Prashant], Shah, P.[Pratik], Mitra, S.K.[Suman K.],
Shadow-Free, Expeditious and Precise, Moving Object Separation from Video,
IJIG(18), No. 01, 2018, pp. 1850005.
DOI Link 1801
BibRef
Earlier:
Fast and Accurate Foreground Background Separation for Video Surveillance,
PReMI15(95-104).
Springer DOI 1511
BibRef

Aytekin, Ç., Possegger, H., Mauthner, T., Kiranyaz, S., Bischof, H., Gabbouj, M.,
Spatiotemporal Saliency Estimation by Spectral Foreground Detection,
MultMed(20), No. 1, January 2018, pp. 82-95.
IEEE DOI 1801
feature extraction, graph theory, image colour analysis, image motion analysis, image segmentation, object detection, spectral graph theory BibRef

Guo, J., Cheong, L.F., Tan, R.,
Video Foreground Cosegmentation Based on Common Fate,
CirSysVideo(28), No. 3, March 2018, pp. 586-600.
IEEE DOI 1804
image motion analysis, image segmentation, iterative methods, video signal processing, articulated motion, video segmentation BibRef

Yang, S.C.[Sheng-Chih], Lin, G.C.[Geng-Cheng], Wang, C.M.[Chuin-Mu],
Foreground detection using texture-based codebook method for monitoring systems,
SIViP(12), No. 4, May 2018, pp. 693-701.
WWW Link. 1805
BibRef

Li, X., Liu, K., Dong, Y.,
Superpixel-Based Foreground Extraction With Fast Adaptive Trimaps,
Cyber(48), No. 9, September 2018, pp. 2609-2619.
IEEE DOI 1809
feature extraction, graph theory, image resolution, image segmentation, superpixel-based foreground extraction, superpixel BibRef

Kajo, I., Kamel, N., Ruichek, Y.[Yassine],
Incremental Tensor-Based Completion Method for Detection of Stationary Foreground Objects,
CirSysVideo(29), No. 5, May 2019, pp. 1325-1338.
IEEE DOI 1905
Tensile stress, Feature extraction, Spatiotemporal phenomena, Matrix decomposition, Object recognition, Real-time systems, incremental singular value decomposition BibRef

Sultana, M.[Maryam], Mahmood, A.[Arif], Javed, S.[Sajid], Jung, S.K.[Soon Ki],
Unsupervised deep context prediction for background estimation and foreground segmentation,
MVA(30), No. 3, April 2019, pp. 375-395.
WWW Link. 1906
BibRef

Yao, Y.Y.[Yi-Yang], Liu, P.Z.[Pei-Zhen], Sun, X.W.[Xiao-Wei], Zhang, L.M.[Lu-Ming],
RETRACTED: Moving object surveillance using object proposals and background prior prediction,
JVCIR(69), 2020, pp. 102838.
Elsevier DOI 2006
BibRef
And: Original: JVCIR(61), 2019, pp. 85-92. 1906
Video surveillance, Object, Moving target detection, Model learning BibRef

Ng, H.F.[Hui Fuang], Chin, C.Y.[Chee Yang],
Effective scene change detection in complex environments,
IJCVR(9), No. 3, 2019, pp. 310-328.
DOI Link 1906
Segment moving foreground objects from static background. BibRef

Panjappagounder-Rajamanickam, K.[Karthikeyan], Periyasamy, S.[Sakthivel],
Entropy Based Illumination-Invariant Foreground Detection,
IEICE(E102-D), No. 7, July 2019, pp. 1434-1437.
WWW Link. 1907
BibRef

Croitoru, I.[Ioana], Bogolin, S.V.[Simion-Vlad], Leordeanu, M.[Marius],
Unsupervised Learning of Foreground Object Segmentation,
IJCV(127), No. 9, September 2019, pp. 1279-1302.
Springer DOI 1908
BibRef
Earlier:
Unsupervised Learning from Video to Detect Foreground Objects in Single Images,
ICCV17(4345-4353)
IEEE DOI 1802
image segmentation, neural nets, object detection, unsupervised learning, video signal processing, Visualization BibRef

Leordeanu, M.[Marius], Collins, R.[Robert],
Unsupervised Learning of Object Features from Video Sequences,
CVPR05(I: 1142-1149).
IEEE DOI 0507
Single or multiple objects, change in pose, low res video. Related features tend to move the same. BibRef

García-González, J.[Jorge], Ortiz-de-Lazcano-Lobato, J.M.[Juan M.], Luque-Baena, R.M.[Rafael M.], Molina-Cabello, M.A.[Miguel A.], López-Rubio, E.[Ezequiel],
Foreground detection by probabilistic modeling of the features discovered by stacked denoising autoencoders in noisy video sequences,
PRL(125), 2019, pp. 481-487.
Elsevier DOI 1909
BibRef

Fu, Z.H.[Zhi-Hang], Chen, Y.W.[Yao-Wu], Yong, H.W.[Hong-Wei], Jiang, R.X.[Rong-Xin], Zhang, L.[Lei], Hua, X.S.[Xian-Sheng],
Foreground Gating and Background Refining Network for Surveillance Object Detection,
IP(28), No. 12, December 2019, pp. 6077-6090.
IEEE DOI 1909
Surveillance, Videos, Object detection, Feature extraction, Proposals, Cameras, Task analysis, Object detection, surveillance video BibRef

Shinde, T.S.[Tushar Shankar], Tiwari, A.K.[Anil Kumar], Lin, W.Y.[Wei-Yao], Shen, L.Q.[Li-Quan],
Background foreground boundary aware efficient motion search for surveillance videos,
SP:IC(82), 2020, pp. 115775.
Elsevier DOI 2001
Block matching, Surveillance video, Block classification, Search complexity, Directional motion BibRef

Akilan, T., Wu, Q.J., Safaei, A., Huo, J., Yang, Y.,
A 3D CNN-LSTM-Based Image-to-Image Foreground Segmentation,
ITS(21), No. 3, March 2020, pp. 959-971.
IEEE DOI 2003
Solid modeling, Image segmentation, Visualization, Decoding, Encoding, Computational modeling, spatiotemporal cues BibRef

Akilan, T.[Thangarajah], Wu, Q.M.J.[Q. M. Jonathan],
sEnDec: An Improved Image to Image CNN for Foreground Localization,
ITS(21), No. 10, October 2020, pp. 4435-4443.
IEEE DOI 2010
Computational modeling, Encoding, Image segmentation, Image color analysis, Decoding, Visualization, encoder-decoder network BibRef

Singha, A.[Anu], Bhowmik, M.K.[Mrinal Kanti],
Salient Features for Moving Object Detection in Adverse Weather Conditions During Night Time,
CirSysVideo(30), No. 10, October 2020, pp. 3317-3331.
IEEE DOI 2010
Atmospheric modeling, Cameras, Atmospheric waves, Aerosols, Visualization, Object detection, Rain, Atmosphere, aerosols, Infrared, background model BibRef

Wang, X.R.[Xi-Ran], Juang, J.[Jason], Chan, S.H.[Stanley H.],
Automatic foreground extraction from imperfect backgrounds using multi-agent consensus equilibrium,
JVCIR(72), 2020, pp. 102907.
Elsevier DOI 2010
Foreground extraction1, Alpha matting, Background subtraction, Video segmentation, Saliency detection, Plug-and-play ADMM, Consensus equilibrium BibRef

Yang, Z.X.[Zong-Xin], Wei, Y.C.[Yun-Chao], Yang, Y.[Yi],
Collaborative Video Object Segmentation by Multi-Scale Foreground-Background Integration,
PAMI(44), No. 9, September 2022, pp. 4701-4712.
IEEE DOI 2208
BibRef
Earlier:
Collaborative Video Object Segmentation by Foreground-background Integration,
ECCV20(V:332-348).
Springer DOI 2011
Object segmentation, Collaboration, Training, Feature extraction, Video sequences, Task analysis, Semantics, metric learning BibRef

Lutz, S.[Sebastian], Smolic, A.[Aljosa],
Foreground color prediction through inverse compositing,
WACV21(1609-1618)
IEEE DOI 2106
Deep learning, Recurrent neural networks, Image color analysis, Estimation, Prediction methods BibRef

Ge, Y.X.[Yong-Xin], Zhang, J.[Junyin], Ren, X.Y.[Xin-Yu], Zhao, C.Q.[Chen-Qiu], Yang, J.[Juan], Basu, A.[Anup],
Deep Variation Transformation Network for Foreground Detection,
CirSysVideo(31), No. 9, September 2021, pp. 3544-3558.
IEEE DOI 2109
Deep learning, Transforms, Image color analysis, Training, Streaming media, Lighting, Germanium, Foreground detection, deep learning BibRef

Kajo, I.[Ibrahim], Kamel, N.[Nidal], Ruichek, Y.[Yassine],
Tensor-Based Approach for Background-Foreground Separation in Maritime Sequences,
ITS(22), No. 11, November 2021, pp. 7115-7128.
IEEE DOI 2112
Matrix decomposition, Cameras, Sea surface, Computational modeling, Minimization, Dynamics, Real-time systems, Maritime environment, forgetting mechanism BibRef

Osman, I.[Islam], Abdelpakey, M.[Mohamed], Shehata, M.S.[Mohamed S.],
TransBlast: Self-Supervised Learning Using Augmented Subspace with Transformer for Background/Foreground Separation,
RSLCV21(215-224)
IEEE DOI 2112
Training, Computational modeling, Benchmark testing, Predictive models, Transformers, Data models BibRef

Farhand, S.[Sepehr], Tsechpenakis, G.[Gavriil],
Foreground discovery in streaming videos with dynamic construction of content graphs,
CVIU(227), 2023, pp. 103620.
Elsevier DOI 2301
Foreground discovery, Unsupervised detection, Video stream, Bottom-up, Content graph BibRef

Rao, A.[Anyi], Xu, L.N.[Lin-Ning], Li, Z.Z.[Zhi-Zhong], Huang, Q.Q.[Qing-Qiu], Kuang, Z.H.[Zhang-Hui], Zhang, W.[Wayne], Lin, D.[Dahua],
A Coarse-to-Fine Framework for Automatic Video Unscreen,
MultMed(25), 2023, pp. 2723-2733.
IEEE DOI 2307
Extract foreground from given videos. Image segmentation, Pipelines, Image color analysis, Green products, Estimation, Semantics, Production, background estimation BibRef

Yang, D.W.[Da-Wei], He, J.F.[Jian-Feng], Ma, Y.C.[Yin-Chao], Yu, Q.J.[Qian-Jin], Zhang, T.Z.[Tian-Zhu],
Foreground-Background Distribution Modeling Transformer for Visual Object Tracking,
ICCV23(10083-10093)
IEEE DOI 2401
BibRef

Sauvalle, B.[Bruno], de la Fortelle, A.[Arnaud],
Autoencoder-based background reconstruction and foreground segmentation with background noise estimation,
WACV23(3243-3254)
IEEE DOI 2302
Image segmentation, Adaptation models, Computational modeling, Motion segmentation, Predictive models, Cameras, Real-time systems BibRef

Benavides-Arce, A.A.[Anthony A.], Flores-Benites, V.[Victor], Mora-Colque, R.[Rensso],
Foreground Detection Using an Attention Module and a Video Encoding,
CIAP22(III:195-205).
Springer DOI 2205
BibRef

Habibian, A.[Amirhossein], Abati, D.[Davide], Cohen, T.S.[Taco S.], Bejnordi, B.E.[Babak Ehteshami],
Skip-Convolutions for Efficient Video Processing,
CVPR21(2694-2703)
IEEE DOI 2111
Motion detection (frame-to-frame changes) to skip background, process foreground. Convolution, Computational modeling, Redundancy, Logic gates, Streaming media, Predictive models, Cameras BibRef

Wu, Z.Z.[Zong-Ze], Lischinski, D.[Dani], Shechtman, E.[Eli],
Fine-grained Foreground Retrieval via Teacher-Student Learning,
WACV21(3645-3653)
IEEE DOI 2106
Training, Adaptation models, Image segmentation, Semantics, Image retrieval, Object detection BibRef

Germer, T.[Thomas], Uelwer, T.[Tobias], Conrad, S.[Stefan], Harmeling, S.[Stefan],
Fast Multi-Level Foreground Estimation,
ICPR21(1104-1111)
IEEE DOI 2105
Runtime, Image color analysis, Memory management, Measurement uncertainty, Estimation, Graphics processing units, Size measurement BibRef

Liang, D.[Dong], Liu, X.Y.[Xin-Yu],
Coarse-to-fine Foreground Segmentation based on Co-occurrence Pixel-Block and Spatio-Temporal Attention Model,
ICPR21(3807-3813)
IEEE DOI 2105
Training, Deep learning, Training data, Video surveillance, Task analysis BibRef

Yang, Y.C.[Yu-Chen], Ray, N.[Nilanjan],
Foreground-focused domain adaption for object detection,
ICPR21(6941-6948)
IEEE DOI 2105
Training, Backpropagation, Adaptation models, Pipelines, Object detection, Detectors, Predictive models BibRef

García-Gonzàlez, J., Molina-Cabello, M.A., Luque-Baena, R.M., Ortiz-de-Lazcano-Lobato, J.M., López-Rubio, E.,
Deep Autoencoder Architectures For Foreground Object Detection In Video Sequences Based On Probabilistic Mixture Models,
ICIP20(3199-3203)
IEEE DOI 2011
Probabilistic logic, Feature extraction, Training, Mixture models, Object detection, Video sequences, video surveillance BibRef

Chen, J.W.[Jia-Wei], Leou, J.J.[Jin-Jang],
Video Object Segmentation Using Convex Optimization of Foreground and Background Distributions,
ICIAR20(I:209-219).
Springer DOI 2007
BibRef

Xie, C.[Christopher], Xiang, Y.[Yu], Harchaoui, Z.[Zaid], Fox, D.[Dieter],
Object Discovery in Videos as Foreground Motion Clustering,
CVPR19(9986-9995).
IEEE DOI 2002
BibRef

Chen, Y.C.[Yun-Chun], Huang, P.H.[Po-Hsiang], Yu, L.Y.[Li-Yu], Huang, J.B.[Jia-Bin], Yang, M.H.[Ming-Hsuan], Lin, Y.Y.[Yen-Yu],
Deep Semantic Matching with Foreground Detection and Cycle-Consistency,
ACCV18(III:347-362).
Springer DOI 1906
BibRef

Lin, C., Yan, B., Tan, W.,
Foreground Detection in Surveillance Video with Fully Convolutional Semantic Network,
ICIP18(4118-4122)
IEEE DOI 1809
Semantics, Training, Surveillance, Training data, Image segmentation, Kernel, Robustness, Foreground detection, surveillance BibRef

Fouzia, S.[Syeda], Bell, M.[Mark], Klette, R.[Reinhard],
Deep Learning-Based Improved Object Recognition in Warehouses,
PSIVT17(350-365).
Springer DOI 1802
BibRef

Zheng, T., Xie, C., Zhou, W., Li, H.,
Compressive tracking with adaptive color feature selection and foreground modeling,
VCIP16(1-4)
IEEE DOI 1701
Adaptation models BibRef

Haller, E., Leordeanu, M.[Marius],
Unsupervised Object Segmentation in Video by Efficient Selection of Highly Probable Positive Features,
ICCV17(5095-5103)
IEEE DOI 1802
image segmentation, image sequences, learning (artificial intelligence), motion estimation, Video sequences BibRef

Stretcu, O.[Otilia], Leordeanu, M.[Marius],
Multiple Frames Matching for Object Discovery in Video,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Kao, J.Y., Tian, D.[Dong], Mansour, H.[Hassan], Vetro, A.[Anthony], Ortega, A.[Antonio],
Moving object segmentation using depth and optical flow in car driving sequences,
ICIP16(11-15)
IEEE DOI 1610
Cameras BibRef

Tian, D.[Dong], Mansour, H.[Hassan], Vetro, A.[Anthony],
Depth-weighted group-wise principal component analysis for video foreground/background separation,
ICIP15(3230-3234)
IEEE DOI 1512
Foreground /Background separation BibRef

Newson, A.[Alasdair], Tepper, M.[Mariano], Sapiro, G.[Guillermo],
Low-Rank Spatio-Temporal Video Segmentation,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Tepper, M.[Mariano], Newson, A.[Alasdair], Sprechmann, P.[Pablo], Sapiro, G.[Guillermo],
Multi-temporal foreground detection in videos,
ICIP15(4599-4603)
IEEE DOI 1512
Video, foreground detection, robust PCA BibRef

Perez-Rua, J.M.[Juan-Manuel], Crivelli, T.[Tomas], Perez, P.[Patrick],
Background-foreground tracking for video object segmentation,
ICIP15(1613-1617)
IEEE DOI 1512
Markov random field, Object tracking, Optical flow BibRef

Gao, W.[Wei], Kwong, S.[Sam], Zhou, Y.[Yu], Wang, X.[Xu],
Phase congruency analysis of down-sampled and blurring images for foreground extraction,
ICWAPR15(152-157)
IEEE DOI 1511
Gaussian processes BibRef

Sobral, A., Bouwmans, T., Zahzah, E.H.[El-Hadi],
Double-constrained RPCA based on saliency maps for foreground detection in automated maritime surveillance,
AVSS15(1-6)
IEEE DOI 1511
image enhancement BibRef

Ramesh, L.[Lekshmi], Shah, P.[Pratik],
R-SpaRCS: An algorithm for foreground-background separation of compressively-sensed surveillance videos,
AVSS15(1-6)
IEEE DOI 1511
Approximation algorithms BibRef

Martinez, D.[Duber], Saggese, A.[Alessia], Vento, M.[Mario], Loaiza, H.[Humberto], Caicedo, E.[Eduardo],
Locally Adapted Gain Control for Reliable Foreground Detection,
CAIP15(I:812-823).
Springer DOI 1511
BibRef

Sato, A.[Akari], Toda, M.[Masato], Tsukada, M.[Masato],
Foreground Detection Robust Against Cast Shadows in Outdoor Daytime Environment,
CIAP15(II:653-664).
Springer DOI 1511
BibRef

Kraft, E.[Erwin], Brox, T.[Thomas],
Motion Based Foreground Detection and Poselet Motion Features for Action Recognition,
ACCV14(V: 350-365).
Springer DOI 1504
BibRef

Javed, S.[Sajid], Bouwmans, T.[Thierry], Jung, S.K.[Soon Ki],
Combining ARF and OR-PCA for Robust Background Subtraction of Noisy Videos,
CIAP15(II:340-351).
Springer DOI 1511
BibRef

Javed, S.[Sajid], Oh, S.H.[Seon Ho], Sobral, A.[Andrews], Bouwmans, T.[Thierry], Jung, S.K.[Soon Ki],
OR-PCA with MRF for Robust Foreground Detection in Highly Dynamic Backgrounds,
ACCV14(III: 284-299).
Springer DOI 1504
BibRef

Kuo, P.C.[Ping-Cheng], Chen, C.A.[Chao-An], Chang, H.C.[Hsing-Chun], Su, T.F.[Te-Feng], Lai, S.H.[Shang-Hong],
3D Reconstruction with Automatic Foreground Segmentation from Multi-view Images Acquired from a Mobile Device,
IMEV14(352-365).
Springer DOI 1504
BibRef

Fu, Z.H.[Zhi-Hui], Xiong, H.K.[Hong-Kai],
Figure/ground video segmentation using greedy transductive cosegmentation,
ICIP14(3287-3291)
IEEE DOI 1502
Coherence BibRef

Lin, K.H.[Kai-Hsiang], Khorrami, P.[Pooya], Wang, J.P.[Jiang-Ping], Hasegawa-Johnson, M.[Mark], Huang, T.S.[Thomas S.],
Foreground object detection in highly dynamic scenes using saliency,
ICIP14(1125-1129)
IEEE DOI 1502
Algorithm design and analysis BibRef

Qin, H.W.[Hong-Wei], Peng, Y.G.[Yi-Gang], Li, X.[Xiu],
Foreground Extraction of Underwater Videos via Sparse and Low-Rank Matrix Decomposition,
CVAUI14(65-72)
IEEE DOI 1402
BibRef

Wu, X.M.[Xiao-Meng], Kashino, K.[Kunio],
Tri-Map Self-Validation Based on Least Gibbs Energy for Foreground Segmentation,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Wu, J.J.[Jia-Jun], Zhu, J.[Junyan], Tu, Z.W.[Zhuo-Wen],
Reverse Image Segmentation: A High-Level Solution to a Low-Level Task,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Guo, X.J.[Xiao-Jie], Wang, X.G.[Xing-Gang], Yang, L.[Liang], Cao, X.C.[Xiao-Chun], Ma, Y.[Yi],
Robust Foreground Detection Using Smoothness and Arbitrariness Constraints,
ECCV14(VII: 535-550).
Springer DOI 1408
BibRef

Ashwini, M.J., Babu, R.V.[R.Venkatesh], Ramakrishnan, K.R.,
Context-aware real-time tracking in sparse representation framework,
ICIP13(2450-2454)
IEEE DOI 1402
Foreground/Background classification BibRef

Wang, W.H.[Wei-Hong], Wang, Y.[Yang], Chen, F.[Fang], Sowmya, A.,
A weakly supervised approach for object detection based on Soft-Label Boosting,
WACV13(331-338).
IEEE DOI 1303
Combine background subtraction and learning-based approaches to get best of both. BibRef

Liu, H.[Hunaxi], Yan, J.C.[Jun-Chi], Zhu, J.[Jun], Lv, X.W.[Xiao-Wei], Li, X.[Xiong], Zhu, T.H.[Tian-Hong], Liu, Y.C.[Yun-Cai],
A Double-Layer Model for Foreground Detection from Video Sequence,
ICIG11(517-520).
IEEE DOI 1109
BibRef

Zhang, Z.H.[Zhao-Hui], Chen, R.Q.[Rui-Qing], Lu, H.Q.[Han-Qing], Yan, Y.K.[Yu-Kun], Cui, H.Q.[Hui-Qing],
Moving Foreground Detection Based on Modified Codebook,
CISP09(1-5).
IEEE DOI 0910
BibRef

Ilyas, A.[Atif], Scuturici, M.[Mihaela], Miguet, S.[Serge],
Real Time Foreground-Background Segmentation Using a Modified Codebook Model,
AVSBS09(454-459).
IEEE DOI 0909
BibRef

Paris, S.[Sylvain],
Edge-Preserving Smoothing and Mean-Shift Segmentation of Video Streams,
ECCV08(II: 460-473).
Springer DOI 0810
BibRef

Wu, X.Y.[Xiao-Yu], Wang, Y.S.[Yang-Sheng], Zheng, X.L.[Xiao-Long],
Monocular video foreground segmentation system,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Dikmen, M.[Mert], Tsai, S.F.[Shen-Fu], Huang, T.S.[Thomas S.],
Base selection in estimating sparse foreground in video,
ICIP09(3217-3220).
IEEE DOI 0911
BibRef

Dikmen, M.[Mert], Huang, T.S.[Thomas S.],
Robust estimation of foreground in surveillance videos by sparse error estimation,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Tezuka, H.[Hiroaki], Nishitani, T.[Takao],
A precise and stable foreground segmentation using fine-to-coarse approach in transform domain,
ICIP08(2732-2735).
IEEE DOI 0810
BibRef

Wu, Q.[Qiong], Boulanger, P.[Pierre], Bischof, W.F.[Walter F.],
Automatic bi-layer video segmentation based on sensor fusion,
ICPR08(1-4).
IEEE DOI 0812
BibRef
And:
Robust Real-Time Bi-Layer Video Segmentation Using Infrared Video,
CRV08(87-94).
IEEE DOI 0805
BibRef

Crabb, R.[Ryan], Tracey, C.[Colin], Puranik, A.[Akshaya], Davis, J.[James],
Real-time foreground segmentation via range and color imaging,
TOF-CV08(1-5).
IEEE DOI 0806
BibRef

Guerzhoy, M.[Michael], Zhou, H.[Hui],
Segmentation of Rectangular Objects Lying on an Unknown Background in a Small Preview Scan Image,
CRV08(369-375).
IEEE DOI 0805
BibRef

Kim, H.S.[Han-Sung], Sakamoto, R.[Ryuuki], Kitahara, I.[Itaru], Toriyama, T.[Tomoji], Kogure, K.[Kiyoshi],
Robust Foreground Extraction Technique Using Gaussian Family Model and Multiple Thresholds,
ACCV07(I: 758-768).
Springer DOI 0711
BibRef

Fan, J.L.[Jia-Lue], Yang, M.[Ming], Wu, Y.[Ying],
A bi-subspace model for robust visual tracking,
ICIP08(2660-2663).
IEEE DOI 0810
BibRef

Yang, M.[Ming], Wu, Y.[Ying],
Granularity and elasticity adaptation in visual tracking,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Yang, M.[Ming], Yuan, J.S.[Jun-Song], Wu, Y.[Ying],
Spatial selection for attentional visual tracking,
CVPR07(1-8).
IEEE DOI 0706
BibRef

Sun, J.[Jian], Kang, S.B.[Sing Bing], Xu, Z.B.[Zong-Ben], Tang, X.O.[Xiao-Ou], Shum, H.Y.[Heung-Yeung],
Flash Cut: Foreground Extraction with Flash and No-flash Image Pairs,
CVPR07(1-8).
IEEE DOI 0706
BibRef

Teixeira, L.F.[Luis F.], Corte-Real, L.[Luis],
Cascaded change detection for foreground segmentation,
Motion07(6-6).
IEEE DOI 0702
BibRef

Han, M.[Mei], Xu, W.[Wei], Gong, Y.H.[Yi-Hong],
Video Foreground Segmentation Based on Sequential Feature Clustering,
ICPR06(I: 492-496).
IEEE DOI 0609
BibRef

Berrabah, S.A.[Sid Ahmed], de Cubber, G.[Geert], Enescu, V., Sahli, H.[Hichem],
MRF-Based Foreground Detection in Image Sequences from a Moving Camera,
ICIP06(1125-1128).
IEEE DOI 0610
BibRef

Doshi, A., Trivedi, M.M.,
'Hybrid Cone-Cylinder' Codebook Model for Foreground Detection with Shadow and Highlight Suppression,
AVSBS06(19-19).
IEEE DOI 0611
BibRef

Liang, Y.Q.[Yi-Qing], Crnic, L.[Linda], Kobla, V.[Vikrant], Wolf, W.[Wayne],
System and method for object identification and behavior characterization using video analysis,
US_Patent6,678,413, Jan 13, 2004
WWW Link. BibRef 0401

Lluís, J.[Jordi], Miralles, X.[Xavier], Bastidas, O.[Oscar],
Reliable real-time foreground detection for video surveillance applications,
VSSN05(59-62).
WWW Link. 0511
BibRef

Kamkar-Parsi, A.H.[A. Homayoun], Laganière, R.[Robert], Bouchard, M.[Martin],
A multi-criteria model for robust foreground extraction,
VSSN05(67-70).
WWW Link. 0511
BibRef

Sormann, M.[Mario], Zach, C.[Christopher], Bauer, J.[Joachim], Karner, K.[Konrad], Bischof, H.[Horst],
Automatic Foreground Propagation in Image Sequences for 3D Reconstruction,
DAGM05(93).
Springer DOI 0509
BibRef

Mansouri, A.R., Mitiche, A., Dolla, F.,
Motion-based figure-ground segmentation by maximum motion separation,
ICIP03(III: 925-928).
IEEE DOI 0312
BibRef

Nordlund, P.[Peter], Eklundh, J.O.[Jan-Olof],
Real-Time Maintenance of Figure-Ground Segmentation,
CVS99(115 ff.).
Springer DOI 0209
BibRef

Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Background Models, Textured Surfaces, Regions .