18.3.4.7.3 Motion Sequence, Background Subtraction

Chapter Contents (Back)
Background. Subtraction. Background Subtraction. Motion, Detection. Generally, one main moving region or object. Changing background, moving camera: See also Dynamic Background Subtraction, Moving Camera Background Subtraction. Color versions: See also Motion Sequence, Color Models for Background Subtraction. See also Moving Object Extraction, Using Models or Analysis of Regions. See also Grouping, Figure-Ground, Background, Foreground.

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WWW Link. Background subtraction. BibRef 7912

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Astle, B.[Brian],
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Ivanov, Y.A.[Yuri A.], Bobick, A.F.[Aaron F.], Liu, J.[John],
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IJCV(37), No. 2, June 2000, pp. 199-207.
DOI Link 0008
BibRef
Earlier: VS98(Image Processing for Visual Surveillance). BibRef Vismod--437, 1997.
PS File. BibRef

Tzidon, D.[Dekel], Tzidon, A.[Aviv],
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Kondo, T.[Tetsujiro],
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Kondo, T.[Tetsujiro],
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Elgammal, A.M.[Ahmed M.], Duraiswami, R., Harwood, D.[David], Davis, L.S.[Larry S.],
Background and foreground modeling using nonparametric kernel density estimation for visual surveillance,
PIEEE(90), 2002, pp. 1151-1163.
IEEE DOI BibRef 0200
Earlier: A1, A3, A4, Only:
Non-Parametric Model for Background Subtraction,
ECCV00(II: 751-767).
Springer DOI BibRef Frame-Rate99().
HTML Version. BibRef

Haritaoglu, I., Harwood, D., Davis, L.S.,
A Fast Background Scene Modeling and Maintenance for Outdoor Surveillance,
ICPR00(Vol IV: 179-183).
IEEE DOI 0009
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Horprasert, T.[Thanarat], Harwood, D.[David], Davis, L.S.[Larry S.],
A Statistical Approach for Real-time Robust Background Subtraction and Shadow Detection,
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Kim, K.[Kyungnam], Chalidabhongse, T.H.[Thanarat H.], Harwood, D.[David], Davis, L.S.[Larry S.],
Real-time foreground-background segmentation using codebook model,
RealTimeImg(11), No. 3, June 2005, pp. 172-185.
Elsevier DOI 0508
BibRef
Earlier:
Background modeling and subtraction by codebook construction,
ICIP04(V: 3061-3064).
IEEE DOI 0505
BibRef

Kim, K.[Kyungnam], Harwood, D.[David], Davis, L.S.[Larry S.],
Background Updating for Visual Surveillance,
ISVC05(337-346).
Springer DOI 0512
BibRef

Wu, Q.Z.[Quen-Zong], Jeng, B.S.[Bor-Shenn],
Background subtraction based on logarithmic intensities,
PRL(23), No. 13, November 2002, pp. 1529-1536.
Elsevier DOI 0206
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Itokawa, O.[Osamu],
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And:
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US_Patent6,784,927, Aug 31, 2004
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Lee, D.S.[Dar-Shyang],
Effective Gaussian Mixture Learning for Video Background Subtraction,
PAMI(27), No. 5, May 2005, pp. 827-832.
IEEE Abstract. 0501
BibRef
Earlier:
Online Adaptive Gaussian Mixture Learning for Video Applications,
SMVP04(105-116).
Springer DOI 0505
Adapt the measure for each frame. BibRef

Cheung, S.C.S.[Sen-Ching S.], Kamath, C.[Chandrika],
Robust Background Subtraction with Foreground Validation for Urban Traffic Video,
JASP(2005), No. 14, 2005, pp. 2330-2340.
WWW Link. 0603
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Li, B.X.[Bao-Xin],
Image background replacement method,
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Schoepflin, T.[Todd], Haynor, D.R.[David R.], Sahr, J.D.[John D.], Kim, Y.M.[Yong-Min],
Video object tracking by estimating and subtracting background,
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Olson, T.J.[Thomas J.],
Automatic video monitoring system which selectively saves information,
US_Patent7,023,469, Apr 4, 2006
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Spagnolo, P., d'Orazio, T.[Tiziana], Leo, M.[Marco], Distante, A.,
Moving object segmentation by background subtraction and temporal analysis,
IVC(24), No. 5, 1 May 2006, pp. 411-423.
Elsevier DOI 0606
Moving object segmentation; Temporal analysis; Background updating BibRef

Zivkovic, Z.[Zoran], van der Heijden, F.[Ferdinand],
Efficient adaptive density estimation per image pixel for the task of background subtraction,
PRL(27), No. 7, May 2006, pp. 773-780.
Elsevier DOI 0604
On-line density estimation; Gaussian mixture model; Non-parametric density estimation BibRef

Zivkovic, Z.,
Layered image model using binary PCA transparency masks,
BMVC07(xx-yy).
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Cheng, J.[Jian], Yang, J.[Jie], Zhou, Y.[Yue], Cui, Y.Y.[Ying-Ying],
Flexible background mixture models for foreground segmentation,
IVC(24), No. 5, 1 May 2006, pp. 473-482.
Elsevier DOI Foreground segmentation; Background subtraction; Mixture models; EM algorithm; Maximum a posteriori (MAP) 0606
BibRef

Davis, J.W.[James W.], Sharma, V.[Vinay],
Background-Subtraction in Thermal Imagery Using Contour Saliency,
IJCV(71), No. 2, February 2007, pp. 161-181.
Springer DOI 0609
BibRef
Earlier:
Fusion-Based Background-Subtraction using Contour Saliency,
OTCBVS05(III: 11-11).
IEEE DOI 0507
BibRef
And: A2, A1:
Feature-level Fusion for Object Segmentation using Mutual Information,
OTCBVS06(139).
IEEE DOI 0609
BibRef

Davis, J.W.[James W.], Sharma, V.[Vinay],
Background-subtraction using contour-based fusion of thermal and visible imagery,
CVIU(106), No. 2-3, May-June 2007, pp. 162-182.
Elsevier DOI 0705
BibRef
Earlier:
Robust detection of people in thermal imagery,
ICPR04(IV: 713-716).
IEEE DOI 0409
BibRef
Earlier:
Robust Background-Subtraction for Person Detection in Thermal Imagery,
OTCBVS04(128).
WWW Link. 0502
Background-subtraction; Fusion; Thermal imagery; Infrared; FLIR; Contour Saliency Map; CSM; Video surveillance and monitoring; Person detection BibRef

Sharma, V.[Vinay], Davis, J.W.[James W.],
Integrating Appearance and Motion Cues for Simultaneous Detection and Segmentation of Pedestrians,
ICCV07(1-8).
IEEE DOI 0710
BibRef
And:
Simultaneous Detection and Segmentation of Pedestrians using Top-down and Bottom-up Processing,
VS07(1-8).
IEEE DOI 0706
BibRef
Earlier:
Extraction of Person Silhouettes from Surveillance Imagery using MRFs,
WACV07(33-33).
IEEE DOI 0702
BibRef

Wang, H.Z.[Han-Zi], Suter, D.[David],
A consensus-based method for tracking: Modelling background scenario and foreground appearance,
PR(40), No. 3, March 2007, pp. 1091-1105.
Elsevier DOI 0611
BibRef
Background Subtraction Based on a Robust Consensus Method,
ICPR06(I: 223-226).
IEEE DOI 0609
BibRef
Earlier:
A Novel Robust Statistical Method for Background Initialization and Visual Surveillance,
ACCV06(I:328-337).
Springer DOI 0601
BibRef
Earlier:
Background initialization with a new robust statistical approach,
PETS05(153-159).
IEEE DOI 0602
Background modelling; Background subtraction; Sample consensus; Visual tracking; Segmentation; Foreground appearance modelling; Occlusion BibRef

Schindler, K.[Konrad], Wang, H.Z.[Han-Zi],
Smooth Foreground-Background Segmentation for Video Processing,
ACCV06(II:581-590).
Springer DOI 0601
BibRef

Manzanera, A.[Antoine], Richefeu, J.C.[Julien C.],
A new motion detection algorithm based on Sigma-Delta background estimation,
PRL(28), No. 3, 1 February 2007, pp. 320-328.
Elsevier DOI 0701
Motion detection; Background estimation; Recursive filtering BibRef

Lacassagne, L., Manzanera, A., Dupret, A.,
Motion detection: Fast and robust algorithms for embedded systems,
ICIP09(3265-3268).
IEEE DOI 0911
BibRef

Manzanera, A.[Antoine],
Sigma-Delta Background Subtraction and the Zipf Law,
CIARP07(42-51).
Springer DOI 0711
BibRef

Oral, M.[Mustafa], Deniz, U.[Umut],
Centre of mass model: A novel approach to background modelling for segmentation of moving objects,
IVC(25), No. 8, 1 August 2007, pp. 1365-1376.
Elsevier DOI 0706
Centre of mass; Image modelling; Motion segmentation; Background subtracting BibRef

Chen, Y.T.[Yu-Ting], Chen, C.S.[Chu-Song], Huang, C.R.[Chun-Rong], Hung, Y.P.[Yi-Ping],
Efficient hierarchical method for background subtraction,
PR(40), No. 10, October 2007, pp. 2706-2715.
Elsevier DOI 0707
Hierarchical background modeling; Background subtraction; Contrast histogram; Non-stationary background; Object detection; Video surveillance BibRef

Tsai, Y.P.[Yu-Pao], Ko, C.H.[Cheng-Hung], Hung, Y.P.[Yi-Ping], Shih, Z.C.[Zen-Chung],
Background Removal of Multiview Images by Learning Shape Priors,
IP(16), No. 10, October 2007, pp. 2607-2616.
IEEE DOI 0711
BibRef
Earlier: A2, A1, A4, A3:
A New Image Segmentation Method for Removing Background of Object Movies by Learning Shape Priors,
ICPR06(I: 323-326).
IEEE DOI 0609
BibRef

Taycher, L.[Leonid], Fisher, III, J.W.[John W.], Darrell, T.J.[Trevor J.],
Combining Object and Feature Dynamics in Probabilistic Tracking,
CVIU(108), No. 3, December 2007, pp. 243-260.
Elsevier DOI 0711
BibRef
Earlier: CVPR05(II: 106-113).
IEEE DOI 0507
BibRef
And: CSAIL-2005-016, March 2005.
WWW Link. BibRef
And:
Incorporating Object Tracking Feedback into Background Maintenance Framework,
Motion05(II: 120-125).
IEEE DOI 0502
Probabilistic graphical models; Approximate models; Articulated body tracking; Background subtraction; Shape from motion BibRef

Liu, Y.Z.[Ya-Zhou], Yao, H.X.[Hong-Xun], Gao, W.[Wen], Chen, X.L.[Xi-Lin], Zhao, D.B.[De-Bin],
Nonparametric background generation,
JVCIR(18), No. 3, June 2007, pp. 253-263.
Elsevier DOI 0711
BibRef
Earlier: ICPR06(IV: 916-919).
IEEE DOI 0609
Background subtraction; Background generation; Mean shift; Effect components description; Most reliable background mode; Video surveillance BibRef

Maddalena, L.[Lucia], Petrosino, A.[Alfredo],
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications,
IP(17), No. 7, July 2008, pp. 1168-1177.
IEEE DOI 0806
BibRef
Earlier:
Moving Object Detection for Real-Time Applications,
CIAP07(542-547).
IEEE DOI 0709
BibRef
Earlier:
A Self-organizing Approach to Detection of Moving Patterns for Real-Time Applications,
BVAI07(181-190).
Springer DOI 0710
BibRef

Maddalena, L.[Lucia], Petrosino, A.[Alfredo],
The 3dSOBS+ algorithm for moving object detection,
CVIU(122), No. 1, 2014, pp. 65-73.
Elsevier DOI 1404
BibRef
Earlier:
The SOBS algorithm: What are the limits?,
CDW12(21-26).
IEEE DOI 1207
BibRef
And:
3D Neural Model-Based Stopped Object Detection,
CIAP09(585-593).
Springer DOI 0909
Self-Organizing Background Subtraction . Background subtraction. BibRef

Tsai, D.M., Lai, S.C.,
Independent Component Analysis-Based Background Subtraction for Indoor Surveillance,
IP(18), No. 1, January 2009, pp. 158-167.
IEEE DOI 0812
BibRef

Abbott, R.G.[Robert G.], Williams, L.R.[Lance R.],
Multiple target tracking with lazy background subtraction and connected components analysis,
MVA(20), No. 2, February 2009, pp. xx-yy.
Springer DOI 0902
BibRef

McHugh, J.M.[J. Mike], Konrad, J.[Janusz], Saligrama, V.[Venkatesh], Jodoin, P.M.[Pierre-Marc],
Foreground-Adaptive Background Subtraction,
SPLetters(16), No. 5, May 2009, pp. 390-393.
IEEE DOI 0903
BibRef

Wang, Y.[Yi], Luo, Z.M.[Zhi-Ming], Jodoin, P.M.[Pierre-Marc],
Interactive deep learning method for segmenting moving objects,
PRL(96), No. 1, 2017, pp. 66-75.
Elsevier DOI 1709
Motion, detection BibRef

McHugh, J.M.[J. Mike], Konrad, J.[Janusz], Saligrama, V.[Venkatesh], Jodoin, P.M.[Pierre-Marc], Castanon, D.[David],
Motion detection with false discovery rate control,
ICIP08(873-876).
IEEE DOI 0810
BibRef

Jodoin, P.M.[Pierre-Marc], Mignotte, M.[Max], Konrad, J.[Janusz],
Statistical Background Subtraction Using Spatial Cues,
CirSysVideo(17), No. 12, December 2007, pp. 1758-1763.
IEEE DOI 0712
BibRef
Earlier:
Background Subtraction Framework Based on Local Spatial Distributions,
ICIAR06(I: 370-380).
Springer DOI 0610
BibRef
And:
Light and Fast Statistical Motion Detection Method Based on Ergodic Model,
ICIP06(1053-1056).
IEEE DOI 0610
BibRef

Jodoin, P.M.[Pierre-Marc], Saligrama, V.[Venkatesh], Konrad, J.[Janusz],
Behavior Subtraction,
IP(21), No. 9, September 2012, pp. 4244-4255.
IEEE DOI 1208
BibRef
Earlier:
Implicit Active-Contouring with MRF,
ICIAR09(178-190).
Springer DOI 0907
BibRef
Earlier: A1, A3, A2:
Modeling background activity for behavior subtraction,
ICDSC08(1-10).
IEEE DOI 0809
BibRef

Jodoin, P.M.[Pierre-Marc], Konrad, J.[Janusz], Saligrama, V.[Venkatesh], Veilleux-Gaboury, V.[Vincent],
Motion detection with an unstable camera,
ICIP08(229-232).
IEEE DOI 0810
BibRef

Jodoin, P.M.[Pierre-Marc], Mignotte, M.[Max],
Motion Segmentation Using a K-Nearest-Neighbor-Based Fusion Procedure of Spatial and Temporal Label Cues,
ICIAR05(778-788).
Springer DOI 0509
BibRef
Earlier:
Unsupervised Motion Detection Using a Markovian Temporal Model with Global Spatial Constraints,
ICIP04(IV: 2591-2594).
IEEE DOI 0505
BibRef

Kaplan, L.M.[Lance M.], Nasrabadi, N.M.[Nasser M.],
Block Wiener-based image registration for moving target indication,
IVC(27), No. 6, 4 May 2009, pp. 694-703.
Elsevier DOI 0904
Image registration; Wiener filtering; Least mean-squared estimation; Moving target indication BibRef

Poppe, C.[Chris], de Bruyne, S.[Sarah], Paridaens, T.[Tom], Lambert, P.[Peter], van de Walle, R.[Rik],
Moving object detection in the H.264/AVC compressed domain for video surveillance applications,
JVCIR(20), No. 6, August 2009, pp. 428-437.
Elsevier DOI 0907
Moving object detection; Compressed domain analysis; Video surveillance; Object Segmentation; MPEG video; Block-based video coding; H. 264/AVC; Signal processing BibRef

Verstockt, S.[Steven], de Bruyne, S.[Sarah], Poppe, C.[Chris], Lambert, P.[Peter], van de Walle, R.[Rik],
Multi-view Object Localization in H.264/AVC Compressed Domain,
AVSBS09(370-374).
IEEE DOI 0909
BibRef

Poppe, C.[Chris], Martens, G.[Gaëtan], Lambert, P.[Peter], van de Walle, R.[Rik],
Improved Background Mixture Models for Video Surveillance Applications,
ACCV07(I: 251-260).
Springer DOI 0711
BibRef
And:
Mixture Models Based Background Subtraction for Video Surveillance Applications,
CAIP07(28-35).
Springer DOI 0708
BibRef

Zha, Y.F.[Yu-Fei], Bi, D.Y.[Du-Yan], Yang, Y.[Yuan],
Learning complex background by multi-scale discriminative model,
PRL(30), No. 11, 1 August 2009, pp. 1003-1014,.
Elsevier DOI 0909
Background subtraction; Multi-scale; Kernel density estimation; AdaBoost; Markov random field See also Graph-based transductive learning for robust visual tracking. BibRef

Elhabian, S., El-Sayed, K., Ahmed, S.,
Moving Object Detection in Spatial Domain using Background Removal Techniques: State-of-Art,
RPCS(1), No. 1, January 2008, pp. 32-54.
PDF File.
WWW Link. Survey, Background Subtraction. 1001
BibRef

Bouwmans, T., El Baf, F., Vachon, B.,
Background Modeling using Mixture of Gaussians for Foreground Detection: A Survey,
RPCS(1), No. 3, November 2008, pp. 219-237.
PDF File. Survey, Background Subtraction. 1001
BibRef
And:
Statistical Background Modeling for Foreground Detection: A Survey,
HPRCV09(IV: 181-199). Survey, Background Subtraction. 1001
BibRef

Bouwmans, T.[Thierry],
Subspace Learning for Background Modeling: A Survey,
RPCS(2), No. 3, November 2009, pp. 223-234.
WWW Link. 1001
Survey, Motion Detection. BibRef

Marghes, C.[Cristina], Bouwman, T.[Thierry],
Background Modeling via Incremental Maximum Margin Criterion,
Subspace10(394-403).
Springer DOI 1109
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Casares, M.[Mauricio], Velipasalar, S.[Senem], Pinto, A.[Alvaro],
Light-weight salient foreground detection for embedded smart cameras,
CVIU(114), No. 11, November 2010, pp. 1223-1237.
Elsevier DOI 1011
BibRef
Earlier: A1, A2, Only: ICDSC08(1-7).
IEEE DOI 0809
Foreground detection; Background subtraction; Salient motion; Light-weight; Memory; Embedded; Smart camera See also Adaptive Methodologies for Energy-Efficient Object Detection and Tracking With Battery-Powered Embedded Smart Cameras. BibRef

Casares, M.[Mauricio], Santinelli, P.[Paolo], Velipasalar, S.[Senem], Prati, A.[Andrea], Cucchiara, R.[Rita],
Energy-Efficient Foreground Object Detection on Embedded Smart Cameras by Hardware-Level Operations,
ECVW11(150-156).
IEEE DOI 1106
BibRef

Appiah, K.[Kofi], Hunter, A.[Andrew], Dickinson, P.[Patrick], Meng, H.Y.[Hong-Ying],
Accelerated hardware video object segmentation: From foreground detection to connected components labelling,
CVIU(114), No. 11, November 2010, pp. 1282-1291.
Elsevier DOI 1011
Background differencing; Image segmentation; Connected component labelling; Object extraction; FPGA BibRef

Fakhfakh, N., Khoudour, L., El-Koursi, E., Bruyelle, J.L., Dufaux, A., Jacot, J.,
3D Objects Localization Using Fuzzy Approach and Hierarchical Belief Propagation: Application at Level Crossings,
JIVP(2011), No. 2011, pp. xx-yy.
DOI Link 1101
BibRef
Earlier:
Background subtraction and 3D localization of moving and stationary obstacles at level crossings,
IPTA10(72-78).
IEEE DOI 1007
BibRef

Salmane, H.[Houssam], Ruichek, Y.[Yassine], Khoudour, L.[Louahdi],
Gaussian Propagation Model Based Dense Optical Flow for Objects Tracking,
ICIAR12(I: 234-244).
Springer DOI 1206
BibRef

Zhao, C.[Cong], Wang, X.G.[Xiao-Gang], Cham, W.K.[Wai-Kuen],
Background Subtraction via Robust Dictionary Learning,
JIVP(2011), No. 2011, pp. xx-yy.
DOI Link 1103
BibRef

Suhr, J.K., Jung, H.G., Li, G., Kim, J.,
Mixture of Gaussians-Based Background Subtraction for Bayer-Pattern Image Sequences,
CirSysVideo(21), No. 3, March 2011, pp. 365-370.
IEEE DOI 1104
BibRef

Barnich, O., van Droogenbroeck, M.,
ViBe: A Universal Background Subtraction Algorithm for Video Sequences,
IP(20), No. 6, June 2011, pp. 1709-1724.
IEEE DOI 1106
BibRef

Cheng, F.C., Huang, S.C., Ruan, S.J.,
Scene Analysis for Object Detection in Advanced Surveillance Systems Using Laplacian Distribution Model,
SMC-C(41), No. 5, September 2011, pp. 589-598.
IEEE DOI 1109
background subtraction approach to detect objects. BibRef

Xue, G.J.[Geng-Jian], Sun, J.[Jun], Song, L.[Li],
Background subtraction based on phase feature and distance transform,
PRL(33), No. 12, 1 September 2012, pp. 1601-1613.
Elsevier DOI 1208
BibRef
Earlier:
Background subtraction based on phase and distance transform under sudden illumination change,
ICIP10(3465-3468).
IEEE DOI 1009
Background subtraction; Phase feature; Phase based background model; Distance transform BibRef

Farcas, D.[Diana], Marghes, C.[Cristina], Bouwmans, T.[Thierry],
Background subtraction via incremental maximum margin criterion: a discriminative subspace approach,
MVA(23), No. 6, November 2012, pp. 1083-1101.
WWW Link. 1210
BibRef

Vosters, L.[Luc], Shan, C.[Caifeng], Gritti, T.[Tommaso],
Real-time robust background subtraction under rapidly changing illumination conditions,
IVC(30), No. 12, December 2012, pp. 1004-1015.
Elsevier DOI 1212
Background subtraction; EM; Eigenbackground; Illumination changes BibRef

Hati, K.K., Sa, P.K.[Pankaj Kumar], Majhi, B.[Banshidhar],
Intensity Range Based Background Subtraction for Effective Object Detection,
SPLetters(20), No. 8, 2013, pp. 759-762.
IEEE DOI 1307
object detection BibRef

Akula, A.[Aparna], Ghosh, R.[Ripul], Kumar, S.[Satish], Sardana, H.K.,
Moving target detection in thermal infrared imagery using spatiotemporal information,
JOSA-A(30), No. 8, August 2013, pp. 1492-1501.
WWW Link. 1309
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Huang, Z.K.[Zhen-Kun], Hu, R.M.[Rui-Min], Wang, Z.Y.[Zhong-Yuan],
Background Subtraction With Video Coding,
SPLetters(20), No. 11, 2013, pp. 1058-1061.
IEEE DOI 1310
Gaussian processes BibRef

Yoo, S.[Seungwoo], Kim, C.[Changick],
Background subtraction using hybrid feature coding in the bag-of-features framework,
PRL(34), No. 16, 2013, pp. 2086-2093.
Elsevier DOI 1310
Background subtraction BibRef

Guo, J.M.[Jing-Ming], Hsia, C.H.[Chih-Hsien], Liu, Y.F.[Yun-Fu], Shih, M.H.[Min-Hsiung], Chang, C.H.[Cheng-Hsin], Wu, J.Y.[Jing-Yu],
Fast Background Subtraction Based on a Multilayer Codebook Model for Moving Object Detection,
CirSysVideo(23), No. 10, 2013, pp. 1809-1821.
IEEE DOI 1311
feature extraction BibRef

Hsia, C.H.[Chih-Hsien], Wu, T.C.[Tsung-Cheng], Chiang, J.S.[Jen-Shiun],
A new method of moving object detection using adaptive filter,
RealTimeIP(13), No. 2, June 2017, pp. 311-325.
WWW Link. 1708
BibRef

Dey, B., Kundu, M.K.,
Robust Background Subtraction for Network Surveillance in H.264 Streaming Video,
CirSysVideo(23), No. 10, 2013, pp. 1695-1703.
IEEE DOI 1311
feature extraction BibRef

Dey, B., Kundu, M.K.,
Efficient Foreground Extraction From HEVC Compressed Video for Application to Real-Time Analysis of Surveillance 'Big' Data,
IP(24), No. 11, November 2015, pp. 3574-3585.
IEEE DOI 1509
Big Data BibRef

Tian, Y.H.[Yong-Hong], Wang, Y.W.[Yao-Wei], Hu, Z.P.[Zhi-Peng], Huang, T.J.[Tie-Jun],
Selective Eigenbackground for Background Modeling and Subtraction in Crowded Scenes,
CirSysVideo(23), No. 11, 2013, pp. 1849-1864.
IEEE DOI 1312
eigenvalues and eigenfunctions BibRef

Haines, T.S.F.[Tom S. F.], Xiang, T.[Tao],
Active Rare Class Discovery and Classification Using Dirichlet Processes,
IJCV(106), No. 3, February 2014, pp. 315-331.
WWW Link. 1402
BibRef
Earlier:
Background Subtraction with Dirichlet Processes,
ECCV12(IV: 99-113).
Springer DOI 1210
BibRef
Earlier:
Delta-Dual Hierarchical Dirichlet Processes: A pragmatic abnormal behaviour detector,
ICCV11(2198-2205).
IEEE DOI 1201
BibRef
And:
Active Learning using Dirichlet Processes for Rare Class Discovery and Classification,
BMVC11(xx-yy).
HTML Version. 1110
BibRef

Haines, T.S.F.[Tom S. F.], Xiang, T.[Tao],
Background Subtraction with Dirichlet Process Mixture Models,
PAMI(36), No. 4, April 2014, pp. 670-683.
IEEE DOI 1404
Bayes methods BibRef

Zhang, X.G.[Xian-Guo], Huang, T.J.[Tie-Jun], Tian, Y.H.[Yong-Hong], Gao, W.[Wen],
Background-Modeling-Based Adaptive Prediction for Surveillance Video Coding,
IP(23), No. 2, February 2014, pp. 769-784.
IEEE DOI 1402
data compression See also Optimizing the Hierarchical Prediction and Coding in HEVC for Surveillance and Conference Videos With Background Modeling. BibRef

Han, S.[Shumin], Zhang, X.G.[Xian-Guo], Tian, Y.H.[Yong-Hong], Huang, T.J.[Tie-Jun],
An Efficient Background Reconstruction Based Coding Method for Surveillance Videos Captured by Moving Camera,
AVSS12(160-165).
IEEE DOI 1211
BibRef

Wu, H.[Hefeng], Liu, N.[Ning], Luo, X.N.[Xiao-Nan], Su, J.W.[Jia-Wei], Chen, L.S.[Liang-Shi],
Real-time background subtraction-based video surveillance of people by integrating local texture patterns,
SIViP(8), No. 4, May 2014, pp. 665-676.
Springer DOI 1404
BibRef

Sobral, A.[Andrews], Vacavant, A.[Antoine],
A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos,
CVIU(122), No. 1, 2014, pp. 4-21.
Elsevier DOI 1404
Survey, Background Subtraction. BibRef

Ferone, A., Maddalena, L.,
Neural Background Subtraction for Pan-Tilt-Zoom Cameras,
SMCS(44), No. 5, May 2014, pp. 571-579.
IEEE DOI 1405
cameras BibRef

Lee, S.W.[Sang-Wook], Lee, C.H.[Chul-Hee],
Low-complexity background subtraction based on spatial similarity,
JIVP(2014), No. 1, 2014, pp. 30.
DOI Link 1407
BibRef

Dehghani, A.[Alireza], Sutherland, A.[Alistair], Srinivasa, G.[Gowri],
A Novel Interest-Point-Based Background Subtraction Algorithm,
ELCVIA(13), No. 1, 2014, pp.
WWW Link. 1407
BibRef

Seidel, F.[Florian], Hage, C.[Clemens], Kleinsteuber, M.[Martin],
pROST: a smoothed lp -norm robust online subspace tracking method for background subtraction in video,
MVA(25), No. 5, July 2014, pp. 1227-1240.
WWW Link. 1407
BibRef

Romanoni, A.[Andrea], Matteucci, M.[Matteo], Sorrenti, D.G.[Domenico G.],
Background subtraction by combining Temporal and Spatio-Temporal histograms in the presence of camera movement,
MVA(25), No. 6, 2014, pp. 1573-1584.
WWW Link. 1408
BibRef

Lim, T.[Taegyu], Han, B.H.[Bo-Hyung], Han, J.H.[Joon H.],
Modeling and segmentation of floating foreground and background in videos,
PR(45), No. 4, 2012, pp. 1696-1706.
Elsevier DOI 1410
Foreground/background segmentation BibRef

Kwak, S.[Suha], Lim, T.[Taegyu], Nam, W.H.[Woon-Hyun], Han, B.H.[Bo-Hyung], Han, J.H.[Joon Hee],
Generalized background subtraction based on hybrid inference by belief propagation and Bayesian filtering,
ICCV11(2174-2181).
IEEE DOI 1201
BibRef

Nam, W.H.[Woon-Hyun], Han, J.H.[Joon-Hee],
Motion-based background modeling for foreground segmentation,
VSSN06(35-44).
WWW Link. 0701
BibRef

Pei, Z.[Zhao], Zhang, Y.N.[Yan-Ning], Yang, T.[Tao], Zhang, X.W.[Xiu-Wei], Yang, Y.H.[Yee-Hong],
A novel multi-object detection method in complex scene using synthetic aperture imaging,
PR(45), No. 4, 2012, pp. 1637-1658.
Elsevier DOI 1410
Background subtraction See also Synthetic aperture photography using a moving camera-IMU system. BibRef

Azab, M.M.[Maha M.], Shedeed, H.A.[Howida A.], Hussein, A.S.[Ashraf S.],
New technique for online object tracking-by-detection in video,
IET-IPR(8), No. 12, 2014, pp. 794-803.
DOI Link 1412
BibRef
Earlier:
A new technique for background modeling and subtraction for motion detection in real-time videos,
ICIP10(3453-3456).
IEEE DOI 1009
BibRef

Wen, J.J.[Jia-Jun], Xu, Y.[Yong], Tang, J.H.[Jin-Hui], Zhan, Y.W.[Yin-Wei], Lai, Z.H.[Zhi-Hui], Guo, X.T.[Xiao-Tang],
Joint Video Frame Set Division and Low-Rank Decomposition for Background Subtraction,
CirSysVideo(24), No. 12, December 2014, pp. 2034-2048.
IEEE DOI 1412
image motion analysis BibRef

Cruz, A.C.[Albert C.], Bhanu, B.[Bir], Thakoor, N.S.[Ninad S.],
Background suppressing Gabor energy filtering,
PRL(52), No. 1, 2015, pp. 40-47.
Elsevier DOI 1412
Gabor filter BibRef

Seo, J.W.[Ja-Won], Kim, S.D.[Seong Dae],
Recursive On-Line 2D PCA and Its Application to Long-Term Background Subtraction,
MultMed(16), No. 8, December 2014, pp. 2333-2344.
IEEE DOI 1502
image recognition BibRef

Mitsugami, I.[Ikuhisa], Fukui, H.[Hiromasa], Minoh, M.[Michihiko],
Extraction of Potential Sunny Region for Background Subtraction under Sudden Illumination Changes,
IJCVSP(4), No. 1, 2014, pp. xx-yy.
WWW Link. 1502
BibRef

Rodriguez-Gomez, R.[Rafael], Fernandez-Sanchez, E.J.[Enrique J.], Diaz, J.[Javier], Ros, E.[Eduardo],
Codebook hardware implementation on FPGA for background subtraction,
RealTimeIP(10), No. 1, March 2015, pp. 43-57.
Springer DOI 1503
BibRef

Qin, L.[Lixia], Sheng, B.[Bin], Lin, W.Y.[Wei-Yao], Wu, W.[Wen], Shen, R.[Ruimin],
GPU-Accelerated Video Background Subtraction Using Gabor Detector,
JVCIR(32), No. 1, 2015, pp. 1-9.
Elsevier DOI 1511
Background subtraction BibRef

Szwoch, G.[Grzegorz], Ellwart, D.[Damian], Czyzewski, A.[Andrzej],
Parallel implementation of background subtraction algorithms for real-time video processing on a supercomputer platform,
RealTimeIP(11), No. 1, January 2016, pp. 111-125.
WWW Link. 1601
BibRef

Lee, S.H.[Se-Ho], Kang, J.W.[Je-Won], Kim, C.S.[Chang-Su],
Compressed domain video saliency detection using global and local spatiotemporal features,
JVCIR(35), No. 1, 2016, pp. 169-183.
Elsevier DOI 1602
Video saliency detection BibRef

Lee, S.H.[Se-Ho], Kim, J.H.[Jin-Hwan], Choi, K.P.[Kwang Pyo], Sim, J.Y.[Jae-Young], Kim, C.S.[Chang-Su],
Video saliency detection based on spatiotemporal feature learning,
ICIP14(1120-1124)
IEEE DOI 1502
Feature extraction BibRef

Lee, D.Y.[Dae-Youn], Ahn, J.K.[Jae-Kyun], Kim, C.S.[Chang-Su],
Fast background subtraction algorithm using two-level sampling and silhouette detection,
ICIP09(3177-3180).
IEEE DOI 0911
BibRef

Kim, W., Kim, Y.,
Background Subtraction Using Illumination-Invariant Structural Complexity,
SPLetters(23), No. 5, May 2016, pp. 634-638.
IEEE DOI 1604
Adaptation models BibRef

Kim, W.J.[Won-Jun],
Illumination-Invariant Face Representation via Normalized Structural Information,
IEICE(E99-D), No. 10, October 2016, pp. 2661-2663.
WWW Link. 1610
BibRef

Cao, W., Wang, Y., Sun, J., Meng, D., Yang, C., Cichocki, A., Xu, Z.,
Total Variation Regularized Tensor RPCA for Background Subtraction From Compressive Measurements,
IP(25), No. 9, September 2016, pp. 4075-4090.
IEEE DOI 1609
compressed sensing BibRef

Dadkhahi, H.[Hamid], Duarte, M.F.[Marco F.], Dadkhahi, H., Duarte, M.F.,
Masking Strategies for Image Manifolds,
IP(25), No. 9, September 2016, pp. 4314-4328.
IEEE DOI 1609
compressed sensing BibRef

Cevher, V.[Volkan], Sankaranarayanan, A.C.[Aswin C.], Duarte, M.F.[Marco F.], Reddy, D.[Dikpal], Baraniuk, R.G.[Richard G.], Chellappa, R.[Rama],
Compressive Sensing for Background Subtraction,
ECCV08(II: 155-168).
Springer DOI 0810
BibRef

Zhou, Z.W.[Zong-Wei], Jin, Z.[Zhong],
Two-dimension principal component analysis-based motion detection framework with subspace update of background,
IET-CV(10), No. 6, 2016, pp. 603-612.
DOI Link 1609
computational complexity BibRef

Gemignani, G.[Giorgio], Rozza, A.[Alessandro],
A Robust Approach for the Background Subtraction Based on Multi-Layered Self-Organizing Maps,
IP(25), No. 11, November 2016, pp. 5239-5251.
IEEE DOI 1610
BibRef
Earlier:
A novel background subtraction approach based on multi layered self-organizing maps,
ICIP15(462-466)
IEEE DOI 1512
Adaptation models. Background Subtraction; Self Organizing Maps BibRef

St-Charles, P.L.[Pierre-Luc], Bilodeau, G.A.[Guillaume-Alexandre], Bergevin, R.[Robert],
Universal Background Subtraction Using Word Consensus Models,
IP(25), No. 10, October 2016, pp. 4768-4781.
IEEE DOI 1610
BibRef
Earlier:
Flexible Background Subtraction with Self-Balanced Local Sensitivity,
CDW14(414-419)
IEEE DOI 1409
image segmentation. background subtraction See also SuBSENSE: A Universal Change Detection Method With Local Adaptive Sensitivity. BibRef

Sajid, H.[Hasan], Cheung, S.C.S.[Sen-Ching S.], Jacobs, N.[Nathan],
Appearance based background subtraction for PTZ cameras,
SP:IC(47), No. 1, 2016, pp. 417-425.
Elsevier DOI 1610
Background subtraction BibRef

Calvo-Gallego, E.[Elisa], Brox, P.[Piedad], Sánchez-Solano, S.[Santiago],
Low-cost dedicated hardware IP modules for background subtraction in embedded vision systems,
RealTimeIP(12), No. 4, December 2016, pp. 681-695.
Springer DOI 1612
BibRef

Dou, J.F.[Jian-Fang], Qin, Q.[Qin], Tu, Z.M.[Zi-Mei],
Background subtraction based on circulant matrix,
SIViP(11), No. 3, March 2017, pp. 407-414.
WWW Link. 1702
BibRef

Thien, H.T.[Huynh-The], Banos, O.[Oresti], Lee, S.Y.[Sung-Young], Kang, B.H.[Byeong Ho], Kim, E.S.[Eun-Soo], Thuong, L.T.[Le-Tien],
NIC: A Robust Background Extraction Algorithm for Foreground Detection in Dynamic Scenes,
CirSysVideo(27), No. 7, July 2017, pp. 1478-1490.
IEEE DOI 1707
Cameras, Estimation, Heuristic algorithms, Object detection, Real-time systems, Standards, Surveillance, Adaptive Otsu thresholding, background subtraction, foreground detection, neighbor-based, intensity, correction, (NIC) BibRef

Laugraud, B.[Benjamin], Piérard, S.[Sébastien], Van Droogenbroeck, M.[Marc],
LaBGen: A method based on motion detection for generating the background of a scene,
PRL(96), No. 1, 2017, pp. 12-21.
Elsevier DOI 1709
BibRef
Earlier:
LaBGen-P: A pixel-level stationary background generation method based on LaBGen,
ICPR16(107-113)
IEEE DOI 1705
BibRef
Earlier: A2, A3, Only:
A Perfect Estimation of a Background Image Does Not Lead to a Perfect Background Subtraction: Analysis of the Upper Bound on the Performance,
SBMI15(527-534).
Springer DOI 1511
Background, generation. Estimation, Gray-scale, Measurement, Motion detection, Motion segmentation, Video, sequences BibRef

Braham, M.[Marc], Van Droogenbroeck, M.[Marc],
Deep background subtraction with scene-specific convolutional neural networks,
WSSIP16(1-4)
IEEE DOI 1608
image sequences BibRef

Laugraud, B.[Benjamin], Piérard, S.[Sébastien], Braham, M.[Marc], Van Droogenbroeck, M.[Marc],
Simple Median-Based Method for Stationary Background Generation Using Background Subtraction Algorithms,
SBMI15(477-484).
Springer DOI 1511
BibRef

Braham, M.[Marc], Lejeune, A.[Antoine], Van Droogenbroeck, M.[Marc],
A physically motivated pixel-based model for background subtraction in 3D images,
IC3D14(1-8)
IEEE DOI 1503
Adaptation models BibRef

Ortego, D.[Diego], San Miguel, J.C.[Juan Carlos], Martinez, J.M.[José M.],
Stand-alone quality estimation of background subtraction algorithms,
CVIU(162), No. 1, 2017, pp. 87-102.
Elsevier DOI 1710
BibRef
Earlier: A2, A3, Only:
On the Evaluation of Background Subtraction Algorithms without Ground-Truth,
AVSS10(180-187).
IEEE DOI 1009
BibRef
Earlier: A1, A2, Only:
Stationary foreground detection for video-surveillance based on foreground and motion history images,
AVSS13(75-80)
IEEE DOI 1311
Stand-alone quality estimation. Accuracy; Delays; History; Lighting; Robustness; Standards; Surveillance BibRef

Bayona, Á.[Álvaro], San Miguel, J.C.[Juan Carlos], Martinez, J.M.[José M.],
Stationary foreground detection using background subtraction and temporal difference in video surveillance,
ICIP10(4657-4660).
IEEE DOI 1009
BibRef
Earlier:
Comparative Evaluation of Stationary Foreground Object Detection Algorithms Based on Background Subtraction Techniques,
AVSBS09(25-30).
IEEE DOI 0909
See also Rejection based multipath reconstruction for background estimation in video sequences with stationary objects. BibRef

Zhang, X., Zhu, C., Wu, H., Liu, Z., Xu, Y.,
An Imbalance Compensation Framework for Background Subtraction,
MultMed(19), No. 11, November 2017, pp. 2425-2438.
IEEE DOI 1710
Bayes methods, Cost function, Databases, Feature extraction, Training, Background subtraction, class imbalance, imbalance compensation, BibRef


Derrouz, H., Hassouny, A.E., Thami, R.O.H., Tairi, H.,
Hybrid method for background modeling and subtracting,
ISCV17(1-5)
IEEE DOI 1710
Adaptation models, Analytical models, Feature extraction, Lighting, Proposals, Training, W4 algorithm, XCS-LBP algorithm, background modeling, background subtraction, extracting moving objects, video, surveillance BibRef

Scherzinger, A.[Aaron], Klemm, S.[Sören], Berh, D.[Dimitri], Jiang, X.Y.[Xiao-Yi],
CNN-Based Background Subtraction for Long-Term In-Vial FIM Imaging,
CAIP17(I: 359-371).
Springer DOI 1708
BibRef

García, R.O.[Reinier Oves], Valentin, L.[Luis], Risquet, C.P.[Carlos Pérez], Sucar, L.E.[L. Enrique],
A Pathline-Based Background Subtraction Algorithm,
MCPR17(179-188).
Springer DOI 1706
BibRef

Guo, L., Xu, D., Qiang, Z.,
Background Subtraction Using Local SVD Binary Pattern,
Robust16(1159-1167)
IEEE DOI 1612
BibRef

Dokic, K., Idlbek, R., Marinac, A.,
New Approach of Visual Activity Measuring with Background Subtraction Algorithms,
CGiV16(216-220)
IEEE DOI 1608
computer vision BibRef

Javed, S., Oh, S.H., Sobral, A., Bouwmans, T., Jung, S.K.,
Background Subtraction via Superpixel-Based Online Matrix Decomposition with Structured Foreground Constraints,
RSL-CV15(930-938)
IEEE DOI 1602
Computational modeling BibRef

Sobral, A., Javed, S., Jung, S.K., Bouwmans, T.[Thierry], Zahzah, E.H.[El-Hadi],
Online Stochastic Tensor Decomposition for Background Subtraction in Multispectral Video Sequences,
RSL-CV15(946-953)
IEEE DOI 1602
Matrix decomposition BibRef

Javed, S., Jung, S.K.[Soon Ki], Mahmood, A., Bouwmans, T.,
Motion-Aware Graph Regularized RPCA for background modeling of complex scenes,
ICPR16(120-125)
IEEE DOI 1705
BibRef
Earlier: A1, A4, A2, Only:
Depth extended online RPCA with spatiotemporal constraints for robust background subtraction,
FCV15(1-6)
IEEE DOI 1506
Computational modeling, Dynamics, Manifolds, Matrix decomposition, Optical imaging, Sparse matrices, Video sequences. feature extraction BibRef

Makantasis, K.[Konstantinos], Doulamis, A.[Anastasios], Loupos, K.[Konstantinos],
Variational Inference for Background Subtraction in Infrared Imagery,
ISVC15(I: 693-705).
Springer DOI 1601
BibRef

Chen, Y.Y.[Ying-Ying], Wang, J.Q.[Jin-Qiao], Li, J.Q.[Jian-Qiang], Lu, H.Q.[Han-Qing],
Multiple features based shared models for background subtraction,
ICIP15(3946-3950)
IEEE DOI 1512
Background modeling; shared model BibRef

Ebadi, S.E.[Salehe Erfanian], Ones, V.G.[Valia Guerra], Izquierdo, E.[Ebroul],
Efficient background subtraction with low-rank and sparse matrix decomposition,
ICIP15(4863-4867)
IEEE DOI 1512
Background subtraction BibRef

Siva, P.[Parthipan], Shafiee, M.J.[Mohammad Javad], Li, F.[Francis], Wong, A.[Alexander],
PIRM: Fast background subtraction under sudden, local illumination changes via probabilistic illumination range modelling,
ICIP15(789-792)
IEEE DOI 1512
background subtraction BibRef

Tzanidou, G.[Giounona], Climent-Perez, P.[Pau], Hummel, G.[Georg], Schmitt, M.[Marc], Stutz, P.[Peter], Monekosso, D.N.[Dorothy N.], Remagnino, P.[Paolo],
Telemetry assisted frame registration and background subtraction in low-altitude UAV videos,
AVSS15(1-6)
IEEE DOI 1511
Accuracy BibRef

Xin, B.[Bo], Tian, Y.[Yuan], Wang, Y.[Yizhou], Gao, W.[Wen],
Background Subtraction via generalized fused lasso foreground modeling,
CVPR15(4676-4684)
IEEE DOI 1510
BibRef

Nguyen, T.M.[Thanh Minh], Wu, Q.M.J.[Q.M. Jonathan], Mukherjee, D.[Dibyendu],
An Online Adaptive Fuzzy Clustering and Its Application for Background Suppression,
CVS15(179-187).
Springer DOI 1507
BibRef
And:
An Online Unsupervised Feature Selection and its Application for Background Suppression,
CRV15(161-168)
IEEE DOI 1507
Adaptation models See also Robust Student's-t Mixture Model With Spatial Constraints and Its Application in Medical Image Segmentation. BibRef

Shahbaz, A., Hariyono, J., Jo, K.H.[Kang-Hyun],
Evaluation of background subtraction algorithms for video surveillance,
FCV15(1-4)
IEEE DOI 1506
Gaussian processes BibRef

Chen, Y.F.[Yu-Feng], Zhao, K.[Kun], Wu, W.Z.[Wen-Zhe], Liu, S.[Shikai],
Background Subtraction: Model-Sharing Strategy Based on Temporal Variation Analysis,
VSegCV14(333-343).
Springer DOI 1504
BibRef

Hamid, R.[Raffay], Sarma, A.D.[Atish Das], DeCoste, D.[Dennis], Sundaresan, N.[Neel],
Fast Approximate Matching of Videos from Hand-Held Cameras for Robust Background Subtraction,
WACV15(294-301)
IEEE DOI 1503
Accuracy BibRef

Mozdren, K., Sojka, E., Fusek, R., Surkala, M.,
Layered RC circuit with dynamic resistances for background subtraction,
IPTA14(1-6)
IEEE DOI 1503
RC circuits BibRef

Ahn, J.H.[Jong-Hoon],
Fast Adaptive Robust Subspace Tracking for Online Background Subtraction,
ICPR14(2555-2559)
IEEE DOI 1412
Cameras BibRef

Setitra, I.[Insaf], Larabi, S.[Slimane],
Background Subtraction Algorithms with Post-processing: A Review,
ICPR14(2436-2441)
IEEE DOI 1412
Survey, Background Subtraction. Computational modeling BibRef

Sobral, A.[Andrews], Baker, C.G.[Christopher G.], Bouwmans, T.[Thierry], Zahzah, E.H.[El-Hadi],
Incremental and Multi-feature Tensor Subspace Learning Applied for Background Modeling and Subtraction,
ICIAR14(I: 94-103).
Springer DOI 1410
BibRef

Wang, B.[Bin], Dudek, P.[Piotr],
A Fast Self-Tuning Background Subtraction Algorithm,
CDW14(401-404)
IEEE DOI 1409
BibRef

Chen, M.L.[Ming-Liang], Yang, Q.X.[Qing-Xiong], Li, Q.[Qing], Wang, G.[Gang], Yang, M.H.[Ming-Hsuan],
Spatiotemporal Background Subtraction Using Minimum Spanning Tree and Optical Flow,
ECCV14(VII: 521-534).
Springer DOI 1408
BibRef

Lim, J.W.[Jong-Woo], Han, B.H.[Bo-Hyung],
Generalized Background Subtraction Using Superpixels with Label Integrated Motion Estimation,
ECCV14(V: 173-187).
Springer DOI 1408
BibRef

Vishal, K., Bhargava, N., Chaudhuri, S., Seetharaman, G.,
Fast compensation of illumination changes for background subtraction,
AIPR13(1-7)
IEEE DOI 1408
Gaussian processes BibRef

Kim, I.[Intaek], Awan, T.W.[Tayyab Wahab], Soh, Y.S.[Young-Sung],
Background subtraction-based multiple object tracking using particle filter,
WSSIP14(71-74) 1406
Business BibRef

Diaz, R.[Raul], Hallman, S.[Sam], Fowlkes, C.C.[Charless C.],
Detecting Dynamic Objects with Multi-view Background Subtraction,
ICCV13(273-280)
IEEE DOI 1403
BibRef

Rodríguez, A.S.[Alejandro Sánchez], Castolo, J.C.G.[Juan Carlos González], Suárez, Ó.D.[Óscar Déniz],
TimeViewer, a Tool for Visualizing the Problems of the Background Subtraction,
PSIVT13(372-384).
Springer DOI 1402
BibRef

Jiang, Z.Q.[Zheng-Qiang], Huynh, D.Q.[Du Q.], Moran, W.[William], Challa, S.[Subhash],
Combining background subtraction and temporal persistency in pedestrian detection from static videos,
ICIP13(4141-4145)
IEEE DOI 1402
Pedestrian detection BibRef

Kumar, A.[Avinash], Hart, J.M.[John M.], Ahuja, N.[Narendra],
Motion-based background subtraction and panoramic mosaicing for freight train analysis,
ICIP13(4564-4568)
IEEE DOI 1402
background removal BibRef

Wang, B.[Bin], Dudek, P.[Piotr],
AMBER: Adapting multi-resolution background extractor,
ICIP13(3417-3421)
IEEE DOI 1402
background subtraction; motion detection; surveillance; video analytics BibRef

Zhang, X.[Xiang], Cheng, J.[Jian], Liu, Z.[Zhi], Yang, J.[Jie],
Cost-sensitive background subtraction,
ICIP13(3336-3339)
IEEE DOI 1402
background subtraction BibRef

Poleg, Y.[Yair], Arora, C.[Chetan], Peleg, S.[Shmuel],
Head Motion Signatures from Egocentric Videos,
ACCV14(III: 315-329).
Springer DOI 1504
BibRef
Earlier:
Temporal Segmentation of Egocentric Videos,
CVPR14(2537-2544)
IEEE DOI 1409
BibRef

Hoshen, Y.[Yedid], Arora, C.[Chetan], Poleg, Y.[Yair], Peleg, S.[Shmuel],
Efficient representation of distributions for background subtraction,
AVSS13(276-281)
IEEE DOI 1311
Arrays BibRef

Mozdren, K.[Karel], Sojka, E.[Eduard], Fusek, R.[Radovan], Šurkala, M.[Milan],
Layered RC Circuit Model for Background Subtraction,
ISVC13(II:199-209).
Springer DOI 1311
BibRef

Liu, X.C.[Xiao-Chun], Zhong, T.[Tao], Fu, D.[Dan],
Robust compositional method for background subtraction,
ICARCV12(1419-1424).
IEEE DOI 1304
BibRef

Wang, J.Q.[Jun-Qiu], Yagi, Y.S.[Yasu-Shi],
Efficient Background Subtraction under Abrupt Illumination Variations,
ACCV12(I:675-688).
Springer DOI 1304
BibRef

Noh, S.[Seung_Jong], Jeon, M.[Moongu],
A New Framework for Background Subtraction Using Multiple Cues,
ACCV12(III:493-506).
Springer DOI 1304
BibRef

Ma, F.[Fan], Sang, N.[Nong],
Background Subtraction Based on Multi-channel SILTP,
CVLBP12(I:73-84).
Springer DOI 1304
BibRef

Zhao, F.[Fei], Zhang, Z.Y.[Zhi-Yong], Lu, H.Z.[Huan-Zhang],
Dim point target detection based on novel complex background suppression,
CVRS12(45-51).
IEEE DOI 1302
BibRef

Cui, X.[Xinyi], Huang, J.Z.[Jun-Zhou], Zhang, S.T.[Shao-Ting], Metaxas, D.N.[Dimitris N.],
Background Subtraction Using Low Rank and Group Sparsity Constraints,
ECCV12(I: 612-625).
Springer DOI 1210
See also Automatic Image Annotation and Retrieval Using Group Sparsity. BibRef

Chang, H.J.[Hyung Jin], Jeong, H.[Hawook], Choi, J.Y.[Jin Young],
Active attentional sampling for speed-up of background subtraction,
CVPR12(2088-2095).
IEEE DOI 1208
BibRef

Van Droogenbroeck, M., Paquot, O.,
Background subtraction: Experiments and improvements for ViBe,
CDW12(32-37).
IEEE DOI 1207
BibRef

Sekkati, H.[Hicham], Laganière, R.[Robert], Mitiche, A.[Amar], Youmaran, R.[Richard],
Robust Background Subtraction Using Geodesic Active Contours in ICA Subspace for Video Surveillance Applications,
CRV12(190-197).
IEEE DOI 1207
BibRef

Paruchuri, J.K.[Jithendra K.], Sathiyamoorthy, E.P.[Edwin P.], Cheung, S.C.S.[Sen-Ching S.], Chen, C.H.[Chung-Hao],
Spatially adaptive illumination modeling for background subtraction,
VS11(1745-1752).
IEEE DOI 1201
BibRef

Zaharescu, A.[Andrei], Jamieson, M.[Michael],
Multi-scale multi-feature codebook-based background subtraction,
VS11(1753-1760).
IEEE DOI 1201
BibRef

Hu, Z.P.[Zhi-Peng], Wang, Y.[Yaowei], Tian, Y.H.[Yong-Hong], Huang, T.J.[Tie-Jun],
Selective eigenbackgrounds method for background subtraction in crowed scenes,
ICIP11(3277-3280).
IEEE DOI 1201
BibRef

Xing, J.L.[Jun-Liang], Liu, L.W.[Li-Wei], Ai, H.Z.[Hai-Zhou],
Background subtraction through multiple life span modeling,
ICIP11(2953-2956).
IEEE DOI 1201
BibRef

Bhutta, A.A.[Adeel A.], Junejo, I.N.[Imran N.], Foroosh, H.[Hassan],
Selective subtraction when the scene cannot be learned,
ICIP11(3273-3276).
IEEE DOI 1201
BibRef

Nguyen, T.T.[Thuy Tuong], Jeon, J.W.[Jae Wook],
Real-Time Background Compensation for PTZ Cameras Using GPU Accelerated and Range-Limited Genetic Algorithm Search,
PSIVT11(I: 85-96).
Springer DOI 1111
BibRef

Wang, H., Miller, P.,
Regularized online Mixture of Gaussians for background subtraction,
AVSBS11(249-254).
IEEE DOI 1111
BibRef

Gorur, P., Amrutur, B.,
Speeded up Gaussian Mixture Model algorithm for background subtraction,
AVSBS11(386-391).
IEEE DOI 1111
BibRef

Song, T.[Taeyup], Han, D.K., Ko, H.S.[Han-Seok],
Robust background subtraction using data fusion for real elevator scene,
AVSBS11(392-397).
IEEE DOI 1111
BibRef

Pollok, T., Monari, E.,
A visual SLAM-based approach for calibration of distributed camera networks,
AVSS16(429-437)
IEEE DOI 1611
Cameras BibRef

Monari, E., Pollok, T.,
A real-time image-to-panorama registration approach for background subtraction using pan-tilt-cameras,
AVSBS11(237-242).
IEEE DOI 1111
BibRef

Lanza, A.[Alessandro], Salti, S., di Stefano, L.[Luigi],
Background subtraction by non-parametric probabilistic clustering,
AVSBS11(243-248).
IEEE DOI 1111
BibRef

Lanza, A.[Alessandro], Tombari, F.[Federico], di Stefano, L.[Luigi],
Second-Order Polynomial Models for Background Subtraction,
VS10(1-11).
Springer DOI 1109
BibRef
And:
Robust and efficient background subtraction by quadratic polynomial fitting,
ICIP10(1537-1540).
IEEE DOI 1009
BibRef
And:
Accurate and Efficient Background Subtraction by Monotonic Second-Degree Polynomial Fitting,
AVSS10(376-383).
IEEE DOI 1009
BibRef

Dauphin, G.[Gabriel], Khanfir, S.[Sami],
Background suppression with low-resolution camera in the context of medication intake monitoring,
EUVIP11(128-133).
IEEE DOI 1110
BibRef

Varadarajan, S., Karam, L.J., Florencio, D.A.F.,
Background subtraction using spatio-temporal continuities,
EUVIP10(144-148).
IEEE DOI 1110
BibRef

Liu, L.[Leyuan], Sang, N.[Nong],
Metrics for Objective Evaluation of Background Subtraction Algorithms,
ICIG11(562-565).
IEEE DOI 1109
BibRef

Brutzer, S.[Sebastian], Hoferlin, B.[Benjamin], Heidemann, G.[Gunther],
Evaluation of background subtraction techniques for video surveillance,
CVPR11(1937-1944).
IEEE DOI 1106
BibRef

Lee, Y.C.[Yong-Cheol], Jung, J.Y.[Ji-Young], Kweon, I.S.[In-So],
Hierarchical on-line boosting based background subtraction,
FCV11(1-5).
IEEE DOI 1102
BibRef

Wang, L.F.[Ling-Feng], Wu, H.Y.[Huai-Yu], Pan, C.H.[Chun-Hong],
Adaptive e-LBP for Background Subtraction,
ACCV10(III: 560-571).
Springer DOI 1011
BibRef

Fabian, T.[Tomas],
Mixture of Gaussians Exploiting Histograms of Oriented Gradients for Background Subtraction,
ISVC10(II: 716-725).
Springer DOI 1011
BibRef

Movshovitz-Attias, Y.[Yair], Peleg, S.[Shmuel],
Bacteria-Filters: Persistent particle filters for background subtraction,
ICIP10(677-680).
IEEE DOI 1009
BibRef

Vosters, L.P.J., Shan, C.F.[Cai-Feng], Gritti, T.,
Background Subtraction under Sudden Illumination Changes,
AVSS10(384-391).
IEEE DOI 1009
BibRef

Langmann, B.[Benjamin], Ghobadi, S.E.[Seyed E.], Hartmann, K.[Klaus], Loffeld, O.[Otmar],
Multi-modal background subtraction using Gaussian Mixture Models,
PCVIA10(A:61).
PDF File. 1009
BibRef

Dickinson, P.[Patrick], Hunter, A.[Andrew], Appiah, K.[Kofi],
Segmenting Video Foreground Using a Multi-Class MRF,
ICPR10(1848-1851).
IEEE DOI 1008
BibRef

Wolf, C.[Christian], Jolion, J.M.[Jean-Michel],
Integrating a Discrete Motion Model into GMM Based Background Subtraction,
ICPR10(9-12).
IEEE DOI 1008
BibRef

Guo, J.M.[Jing-Ming], Hsu, C.S.[Chih-Sheng],
Cascaded Background Subtraction Using Block-Based and Pixel-Based Codebooks,
ICPR10(1373-1376).
IEEE DOI 1008
BibRef

Guillot, C.[Constant], Taron, M.[Maxime], Sayd, P.[Patrick], Pham, Q.C.[Quoc Cuong], Tilmant, C.[Christophe], Lavest, J.M.[Jean-Marc],
Background Subtraction for PTZ Cameras Performing a Guard Tour and Application to Cameras with Very Low Frame Rate,
VS10(33-42).
Springer DOI 1109
BibRef
And:
Background subtraction adapted to PTZ cameras by keypoint density estimation,
BMVC10(xx-yy).
HTML Version. 1009
BibRef

Gabard, C.[Christophe], Lucat, L.[Laurent], Achard, C.[Catherine], Guillot, C., Sayd, P.[Patrick],
Error Decreasing of Background Subtraction Process by Modeling the Foreground,
VS10(123-132).
Springer DOI 1109
BibRef

Fard, H.O.[Hamidreza Odabai], Chaouch, M.[Mohamed], Pham, Q.C.[Quoc-Cuong], Vacavant, A.[Antoine], Chateau, T.[Thierry],
Joint hierarchical learning for efficient multi-class object detection,
WACV14(261-268)
IEEE DOI 1406
Detectors; classifying a dominating background label. BibRef

Vacavant, A.[Antoine], Chateau, T.[Thierry], Wilhelm, A.[Alexis], Lequièvre, L.[Laurent],
A Benchmark Dataset for Outdoor Foreground/Background Extraction,
BMC12(I:291-300).
Springer DOI 1304
Dataset, Foreground Extraction. Surveillance applications. BibRef

Dhome, Y.[Yoann], Tronson, N.[Nicolas], Vacavant, A.[Antoine], Chateau, T.[Thierry], Gabard, C.[Christophe], Goyat, Y.[Yann], Gruyer, D.[Dominique],
A benchmark for Background Subtraction Algorithms in monocular vision: A comparative study,
IPTA10(66-71).
IEEE DOI 1007
BibRef

Liao, S.C.[Sheng-Cai], Zhao, G.Y.[Guo-Ying], Kellokumpu, V.[Vili], Pietikainen, M.[Matti], Li, S.Z.[Stan Z.],
Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes,
CVPR10(1301-1306).
IEEE DOI 1006
BibRef

Ko, T.[Teresa], Soatto, S.[Stefano], Estrin, D.[Deborah],
Warping background subtraction,
CVPR10(1331-1338).
IEEE DOI 1006
BibRef

Zhong, B.N.[Bi-Neng], Liu, S.H.[Shao-Hui], Yao, H.X.[Hong-Xun],
Local Spatial Co-occurrence for Background Subtraction via Adaptive Binned Kernel Estimation,
ACCV09(III: 152-161).
Springer DOI 0909
BibRef

Rahtu, E.[Esa], Heikkila, J.[Janne],
A Simple and efficient saliency detector for background subtraction,
VS09(1137-1144).
IEEE DOI 0910
BibRef

Tombari, F.[Federico], di Stefano, L.[Luigi], Lanza, A.[Alessandro], Mattoccia, S.[Stefano],
Non-linear parametric Bayesian regression for robust background subtraction,
WMVC09(1-7).
IEEE DOI 0912
See also Bayesian Loop for Synergistic Change Detection and Tracking. BibRef

Varcheie, P.D.Z.[Parisa Darvish Zadeh], Sills-Lavoie, M.[Michael], Bilodeau, G.A.[Guillaume-Alexandre],
An Efficient Region-Based Background Subtraction Technique,
CRV08(71-78).
IEEE DOI 0805
BibRef

Ko, T.[Teresa], Soatto, S.[Stefano], Estrin, D.[Deborah],
Background Subtraction on Distributions,
ECCV08(III: 276-289).
Springer DOI 0810
BibRef

Panahi, S., Sheikhi, S., Hadadan, S., Gheissari, N.,
Evaluation of Background Subtraction Methods,
DICTA08(357-364).
IEEE DOI 0812
BibRef

Min, S.[Seungki], Kim, J.W.[Jung-Whan], Park, A.[Anjin], Hong, G.J.[Gwang-Jin], Jung, K.C.[Kee-Chul],
Graph-Cut Based Background Subtraction Using Visual Hull in Multiveiw Images,
DICTA08(372-377).
IEEE DOI 0812
BibRef

Izquierdo-Guerra, W.[Walter], García-Reyes, E.[Edel],
Background Division, A Suitable Technique for Moving Object Detection,
CIARP10(121-127).
Springer DOI 1011
BibRef
Earlier:
A Novel Approach to Robust Background Subtraction,
CIARP09(69-76).
Springer DOI 0911
BibRef

Zhang, S.P.[Suo-Ping], Qi, Z.H.[Zhan-Hui], Zhang, D.L.[Dong-Liang],
Ship Tracking Using Background Subtraction and Inter-Frame Correlation,
CISP09(1-4).
IEEE DOI 0910
BibRef

Tian, G.D.[Guo-Dong], Men, A.D.[Ai-Dong],
An Improved Texture-Based Method for Background Subtraction Using Local Binary Patterns,
CISP09(1-4).
IEEE DOI 0910
BibRef

Herrero, S.[Sonsoles], Bescós, J.[Jesús],
Background Subtraction Techniques: Systematic Evaluation and Comparative Analysis,
ACIVS09(33-42).
Springer DOI 0909
BibRef

Vijverberg, J.A.[Julien A.], Loomans, M.J.H.[Marijn J.H.], Koeleman, C.J.[Cornelis J.], de With, P.H.N.[Peter H.N.],
Global Illumination Compensation for Background Subtraction Using Gaussian-Based Background Difference Modeling,
AVSBS09(448-453).
IEEE DOI 0909
BibRef

Klare, B.[Brendan], Sarkar, S.[Sudeep],
Background subtraction in varying illuminations using an ensemble based on an enlarged feature set,
OTCBVS09(66-73).
IEEE DOI 0906
BibRef

Singh, A.[Abhishek], Jaikumar, P.[Padmini], Mitra, S.K.[Suman K.],
A Sampling-Resampling Based Bayesian Learning Approach for Object Tracking,
ICCVGIP08(442-449).
IEEE DOI 0812
BibRef

Amato, A.[Ariel], Mozerov, M.[Mikhail], Huerta, I.[Ivan], Gonzalez, J.[Jordi], Villanueva, J.J.[Juan J.],
Background subtraction technique based on chromaticity and intensity patterns,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Reddy, D.[Dikpal], Sankaranarayanan, A.C.[Aswin C.], Cevher, V.[Volkan], Chellappa, R.[Rama],
Compressed sensing for multi-view tracking and 3-D voxel reconstruction,
ICIP08(221-224).
IEEE DOI 0810
BibRef

Pilet, J.[Julien], Strecha, C.[Christoph], Fua, P.[Pascal],
Making Background Subtraction Robust to Sudden Illumination Changes,
ECCV08(IV: 567-580).
Springer DOI 0810
BibRef

Jaikumar, P., Singh, A., Mitra, S.K.,
Background Subtraction in Videos using Bayesian Learning with Motion Information,
BMVC08(xx-yy).
PDF File. 0809
BibRef

Shafait, F.[Faisal], van Beusekom, J.[Joost], Keysers, D.[Daniel], Breuel, T.M.[Thomas M.],
Background variability modeling for statistical layout analysis,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Ulges, A.[Adrian], Breuel, T.M.[Thomas M.],
Can Motion Segmentation Improve Patch-Based Object Recognition?,
ICPR10(3041-3044).
IEEE DOI 1008
BibRef

Ulges, A.[Adrian], Breuel, T.M.[Thomas M.],
A Local Discriminative Model for Background Subtraction,
DAGM08(xx-yy).
Springer DOI 0806
BibRef

Jia, L.[Lihao], Liu, Y.K.[Yi-Kun],
A Novel Thresholding Approach to Background Subtraction,
WACV08(1-6).
IEEE DOI 0801
BibRef

Rodhetbhai, W.[Wasara], Lewis, P.H.[Paul H.],
Salient Region Filtering for Background Subtraction,
Visual07(126-135).
Springer DOI 0706
BibRef

Wang, L.[Lu], Wang, L.[Lei], Wen, M.[Ming], Zhuo, Q.[Qing], Wang, W.Y.[Wen-Yuan],
Background Subtraction using Incremental Subspace Learning,
ICIP07(V: 45-48).
IEEE DOI 0709
BibRef

Fukui, S.[Shinji], Iwahori, Y.[Yuji], Itoh, H.[Hidenori], Kawanaka, H.[Haruki], Woodham, R.J.[Robert J.],
Robust Background Subtraction for Quick Illumination Changes,
PSIVT06(1244-1253).
Springer DOI 0612
BibRef

Park, J., Tabb, A., Kak, A.C.,
Hierarchical Data Structure for Real-Time Background Subtraction,
ICIP06(1849-1852).
IEEE DOI 0610
BibRef

Cezar Silveira Jacques, J., Rosito Jung, C., Musse, S.R.,
A Background Subtraction Model Adapted to Illumination Changes,
ICIP06(1817-1820).
IEEE DOI 0610
BibRef

Noriega, P., Bernier, O.,
Real Time Illumination Invariant Background Subtraction Using Local Kernel Histograms,
BMVC06(III:979).
PDF File. 0609
BibRef

Cheng, L.[Li], Gong, M., Schuurmans, D., Caelli, T.M.,
Real-Time Discriminative Background Subtraction,
IP(20), No. 5, May 2011, pp. 1401-1414.
IEEE DOI 1104
BibRef

Cheng, L.[Li], Wang, S.J.[Shao-Jun], Schuurmans, D., Caelli, T.M., Vishwanathan, S.V.N.,
An Online Discriminative Approach to Background Subtraction,
AVSBS06(2-2).
IEEE DOI 0611
BibRef

Parag, T.[Toufiq], Elgammal, A.M.[Ahmed M.], Mittal, A.[Anurag],
A Framework for Feature Selection for Background Subtraction,
CVPR06(II: 1916-1923).
IEEE DOI 0606
BibRef

Amnuaykanchanasin, P., Thongkamwitoon, T., Srisawaiwilai, N., Aramvith, S., Chalidabhongse, T.H.,
Adaptive parametric statistical background subtraction for video segmentation,
VSSN05(63-66).
WWW Link. 0511
BibRef

Aggarwal, A.[Ashwani], Biswas, S.[Susmit], Singh, S.[Sandeep], Sural, S.[Shamik], Majumdar, A.K.,
Object Tracking Using Background Subtraction and Motion Estimation in MPEG Videos,
ACCV06(II:121-130).
Springer DOI 0601
BibRef

Krüger, V.[Volker], Anderson, J.[Jakob], Prehn, T.[Thomas],
Probabilistic Model-Based Background Subtraction,
SCIA05(567-576).
Springer DOI 0506
BibRef
And: CIAP05(180-187).
Springer DOI 0509
BibRef

Fiala, M.[Mark], Shu, C.[Chang],
Background Subtraction using Self-Identifying Patterns,
CRV05(558-565).
IEEE DOI 0505
See also Automatic Projector Calibration Using Self-Identifying Patterns. BibRef

Grossmann, E., Kale, A., Jaynes, C.,
Towards Interactive Generation of 'Ground-truth' in Background Subtraction from Partially Labeled Examples,
PETS05(325-332).
IEEE DOI 0602
BibRef

Grossmann, E., Kale, A., Jaynes, C., Cheung, S.C.S.,
Offline Generation of High Quality Background Subtraction Data,
BMVC05(xx-yy).
HTML Version. 0509
BibRef

Lim, S.N.[Ser-Nam], Mittal, A.[Anurag], Davis, L.S.[Larry S.], Paragios, N.[Nikos],
Fast Illumination-Invariant Background Subtraction Using Two Views: Error Analysis, Sensor Placement and Applications,
CVPR05(I: 1071-1078).
IEEE DOI 0507
BibRef

Migdal, J.[Joshua], Grimson, W.E.L.[W. Eric L.],
Background Subtraction Using Markov Thresholds,
Motion05(II: 58-65).
IEEE DOI 0502
BibRef

Yamazawa, K.[Kazumasa], Yokoya, N.[Naokazu],
Detecting Moving Objects with an Omnidirectional Camera Based on Adaptive Background Subtraction,
SCIA03(969-974).
Springer DOI 0310
BibRef
And:
Detecting moving objects from omnidirectional dynamic images based on adaptive background subtraction,
ICIP03(III: 953-956).
IEEE DOI 0312
BibRef

Hayman, E., Eklundh, J.O.,
Statistical background subtraction for a mobile observer,
ICCV03(67-74).
IEEE DOI 0311
BibRef

Seki, M., Wada, T., Fujiwara, H., Sumi, K.,
Background subtraction based on cooccurrence of image variations,
CVPR03(II: 65-72).
IEEE DOI 0307
BibRef

Seki, M.[Makito], Fujiwara, H.[Hideto], Sumi, K.[Kazuhiko],
A Robust Background Subtraction Method for Changing Background,
WACV00(207-213).
WWW Link. 0010
BibRef

Wang, D.S.[Dong-Sheng], Feng, T.[Tao], Shum, H.Y.[Heung-Yeung], Ma, S.D.[Song-De],
A Novel Probability Model for Background Maintenance and Subtraction,
VI02(109).
PDF File. 0208
BibRef

Ohta, N.[Naoya],
A Statistical Approach to Background Subtraction for Surveillance Systems,
ICCV01(II: 481-486).
IEEE DOI 0106
BibRef

Stauffer, C.[Chris], Grimson, W.E.L.,
Adaptive Background Mixture Models for Real-time Tracking,
CVPR99(II: 246-252).
IEEE DOI Award, Longuet-Higgins. (Awarded after 10 years) Compute the background through major changes in the scene (lighting, etc.) for subtraction for motion detection. BibRef 9900

Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Dynamic Background Subtraction, Moving Camera Background Subtraction .


Last update:Nov 11, 2017 at 13:31:57