19.3.4.12.3 Motion Sequence, Background Subtraction

Moore, J.K.[J. Kenneth], Kaiser, A.[Arthur], Mahler, H.W.[Henry W.],
Television system for displaying and recording paths of motion,
US_Patent4,179,704, Dec 18, 1979
WWW Link. Background subtraction. BibRef 7912

Woolfson, M.G.[Martin G.], Romanski, J.G.[John G.],
Apparatus and method for preprocessing video frame signals,
US_Patent4,405,940, Sep 20, 1983
WWW Link. target from background BibRef 8309

Morton, R.R.A.[Roger R. A.], Redden, J.E.[John E.],
Apparatus and methods for locating edges and document boundaries in video scan lines,
US_Patent4,833,722, May 23, 1989
WWW Link. Given background BibRef 8905

Morton, R.R.A.[Roger R. A.], Redden, J.E.[John E.], Lewis, S.[Scott],
Apparatus for enhancing and thresholding scanned microfilm images and methods for use therein,
US_Patent4,982,294, Jan 1, 1991
WWW Link. BibRef 9101

Toyama, M.[Masakazu], Hamba, N.[Nobuhiro],
Apparatus for measuring the dynamic state of traffic,
US_Patent5,301,239, 02/03/1992.
HTML Version. Compare to reference image. BibRef 9202

Otsuki, A.[Akira],
Image processing device and method for sensing moving objects and rangefinder employing the same,
US_Patent5,212,547, 05/18/1993.
HTML Version. Compute background based on average of sequence, detect object from difference. BibRef 9305

Brady, M.J.[Mark J.], Cerny, D.G.[Darin G.],
Method and apparatus for background determination and subtraction for a monocular vision system,
US_Patent5,684,898, Nov 4, 1997
WWW Link. BibRef 9711

Astle, B.[Brian],
Video coding scheme with foreground/background separation,
US_Patent5,812,787, Sep 22, 1998
WWW Link. BibRef 9809

Smoot, L.S.[Lanny Starkes],
Background extraction in a video picture,
US_Patent5,940,139, Aug 17, 1999
WWW Link. BibRef 9908

Ivanov, Y.A.[Yuri A.], Bobick, A.F.[Aaron F.], Liu, J.[John],
Fast Lighting Independent Background Subtraction,
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],
Method of providing background patterns for camera tracking,
US_Patent6,191,812, Feb 20, 2001
WWW Link. Chroma key background BibRef 0102

Kondo, T.[Tetsujiro],
Method and apparatus for separating/generating background and motion object planes,
US_Patent6,275,617, Aug 14, 2001
WWW Link. BibRef 0108
And: US_Patent6,335,988, Jan 1, 2002
WWW Link. BibRef

Kondo, T.[Tetsujiro],
Image signal processing apparatus and recording/reproducing apparatus,
US_Patent5,835,138, Nov 10, 1998
WWW Link. BibRef 9811
And: US_Patent5,926,212, Jul 20, 1999
WWW Link. For stabilization BibRef

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
BibRef

Horprasert, T.[Thanarat], Harwood, D.[David], Davis, L.S.[Larry S.],
A Statistical Approach for Real-time Robust Background Subtraction and Shadow Detection,
Frame-Rate99().
HTML Version. BibRef 9900

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
BibRef

Itokawa, O.[Osamu],
Image information processing apparatus and its method,
US_Patent6,404,901, Jun 11, 2002
WWW Link. BibRef 0206
And:
Image processing apparatus and image processing method, and storage medium,
US_Patent6,784,927, Aug 31, 2004
WWW Link. 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
BibRef

Li, B.X.[Bao-Xin],
Image background replacement method,
US_Patent6,909,806, Jun 21, 2005
WWW Link. BibRef 0506

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,
US_Patent6,870,945, Mar 22, 2005
WWW Link. BibRef 0503

Olson, T.J.[Thomas J.],
Automatic video monitoring system which selectively saves information,
US_Patent7,023,469, Apr 4, 2006
WWW Link. BibRef 0604

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).
PDF File. 0709
BibRef

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

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

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

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

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

Marghes, C.[Cristina], Bouwman, T.[Thierry],
Background Modeling via Incremental Maximum Margin Criterion,
Subspace10(394-403).
Springer DOI 1109
BibRef

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

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.F.[Cai-Feng], 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.[Harish K.],
Moving target detection in thermal infrared imagery using spatiotemporal information,
JOSA-A(30), No. 8, August 2013, pp. 1492-1501.
WWW Link. 1309
BibRef

Vasamsetti, S.[Srikanth], Setia, S.[Supriya], Mittal, N.[Neerja], Sardana, H.K.[Harish K.], Babbar, G.[Geetanjali],
Automatic underwater moving object detection using multi-feature integration framework in complex backgrounds,
IET-CV(12), No. 6, September 2018, pp. 770-778.
DOI Link 1808
BibRef

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

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

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.
DOI 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.X.[Li-Xia], Sheng, B.[Bin], Lin, W.Y.[Wei-Yao], Wu, W.[Wen], Shen, R.M.[Rui-Min],
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

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

St-Charles, P.L.[Pierre-Luc], Bilodeau, G.A.[Guillaume-Alexandre], Bergevin, R.[Robert],
Online Mutual Foreground Segmentation for Multispectral Stereo Videos,
IJCV(127), No. 8, August 2019, pp. 1044-1062.
Springer DOI 1907
BibRef
Earlier: A3, A1, A2:
Mutual Foreground Segmentation with Multispectral Stereo Pairs,
MSF17(375-384)
IEEE DOI 1802
Image segmentation, Imaging, Labeling, Motion segmentation, Object segmentation, Shape 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

Sajid, H.[Hasan], Cheung, S.C.S.[Sen-Ching S.], Jacobs, N.[Nathan],
Motion and appearance based background subtraction for freely moving cameras,
SP:IC(75), 2019, pp. 11-21.
Elsevier DOI 1906
Background subtraction, Foreground segmentation, Moving camera, Image segmentation 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], van Droogenbroeck, M.[Marc],
Is a Memoryless Motion Detection Truly Relevant for Background Generation with LaBGen?,
ACIVS17(443-454).
Springer DOI 1712
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

Silva, C.[Caroline], Bouwmans, T.[Thierry], Frélicot, C.[Carl],
Superpixel-based online wagging one-class ensemble for feature selection in foreground/background separation,
PRL(100), No. 1, 2017, pp. 144-151.
Elsevier DOI 1712
BibRef
Earlier:
Online Weighted One-Class Ensemble for feature selection in background/foreground separation,
ICPR16(2216-2221)
IEEE DOI 1705
Background subtraction. Adaptation models, Boosting, Computational modeling, Feature extraction, Image color analysis, Support vector machines, Training BibRef

Xia, S.L.[Sen-Lin], Sun, H.J.[Huai-Jiang], Chen, B.J.[Bei-Jia],
A regularized tensor decomposition method with adaptive rank adjustment for Compressed-Sensed-Domain background subtraction,
SP:IC(62), 2018, pp. 149-163.
Elsevier DOI 1802
Background subtraction, Compressive sensing, Adaptive rank adjustment, Non-convex surrogate, Alternative direction multiplier method BibRef

Halperin, T., Poleg, Y.[Yair], Arora, C.[Chetan], Peleg, S.[Shmuel],
EgoSampling: Wide View Hyperlapse From Egocentric Videos,
CirSysVideo(28), No. 5, May 2018, pp. 1248-1259.
IEEE DOI 1805
BibRef
Earlier: A2, A3, A4, Only:
Head Motion Signatures from Egocentric Videos,
ACCV14(III: 315-329).
Springer DOI 1504
BibRef
Earlier: A2, A3, A4, Only:
Temporal Segmentation of Egocentric Videos,
CVPR14(2537-2544)
IEEE DOI 1409
Cameras, Head, Legged locomotion, Optical imaging, Videos, Egocentric video, fast-forward, video stabilization 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

Shen, W.J.[Wen-Jie], Lin, Y.[Yun], Yu, L.[Lingjuan], Xue, F.T.[Fei-Teng], Hong, W.[Wen],
Single Channel Circular SAR Moving Target Detection Based on Logarithm Background Subtraction Algorithm,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Chan, K.L.,
Saliency detection in video sequences using perceivable change encoded local pattern,
SIViP(12), No. 5, July 2018, pp. 975-982.
Springer DOI 1806
BibRef

Roy, S.M., Ghosh, A.,
Real-Time Adaptive Histogram Min-Max Bucket (HMMB) Model for Background Subtraction,
CirSysVideo(28), No. 7, July 2018, pp. 1513-1525.
IEEE DOI 1807
Adaptation models, Computational modeling, Hidden Markov models, Histograms, Lighting, Real-time systems, sliding window BibRef

Roy, S.M., Ghosh, A.,
Foreground Segmentation Using Adaptive 3 Phase Background Model,
ITS(21), No. 6, June 2020, pp. 2287-2296.
IEEE DOI 2006
Adaptation models, Cameras, Atmospheric modeling, Computational modeling, History, Heuristic algorithms, Dynamics, real-time analysis BibRef

Zeng, D.D.[Dong-Dong], Zhu, M.[Ming], Xu, F.[Fang], Zhou, T.X.[Tong-Xue],
Extended scale invariant local binary pattern for background subtraction,
IET-IPR(12), No. 8, August 2018, pp. 1292-1302.
DOI Link 1808
BibRef

Liu, X., Yao, J., Hong, X., Huang, X., Zhou, Z., Qi, C., Zhao, G.,
Background Subtraction Using Spatio-Temporal Group Sparsity Recovery,
CirSysVideo(28), No. 8, August 2018, pp. 1737-1751.
IEEE DOI 1808
Matrix decomposition, Sparse matrices, Dictionaries, Compressed sensing, Robustness, Training, spatio-temporal BibRef

Makantasis, K., Nikitakis, A., Doulamis, A.D., Doulamis, N.D., Papaefstathiou, I.,
Data-Driven Background Subtraction Algorithm for In-Camera Acceleration in Thermal Imagery,
CirSysVideo(28), No. 9, September 2018, pp. 2090-2104.
IEEE DOI 1809
Hardware, Thermal sensors, Computational modeling, Estimation, Videos, Real-time systems, Thermal imaging, variational inference, foreground estimation
See also Tensor-Based Classification Models for Hyperspectral Data Analysis. BibRef

Jiang, S., Lu, X.,
WeSamBE: A Weight-Sample-Based Method for Background Subtraction,
CirSysVideo(28), No. 9, September 2018, pp. 2105-2115.
IEEE DOI 1809
Computational modeling, Adaptation models, Classification algorithms, Algorithm design and analysis, background subtraction BibRef

Zhang, G.[Guian], Yuan, Z.Y.[Zhi-Yong], Tong, Q.Q.[Qian-Qian], Zheng, M.L.[Mian-Lun], Zhao, J.H.[Jian-Hui],
A novel framework for background subtraction and foreground detection,
PR(84), 2018, pp. 28-38.
Elsevier DOI 1809
Background modeling, Mino vector, Dynamic nature, KDE, Denoising, Tetris update scheme BibRef

Sheri, A.M.[Ahmad Muqeem], Rafique, M.A.[Muhammad Aasim], Jeon, M.[Moongu], Pedrycz, W.[Witold],
Background subtraction using Gaussian-Bernoulli restricted Boltzmann machine,
IET-IPR(12), No. 9, September 2018, pp. 1646-1654.
DOI Link 1809
BibRef

Panda, D.K.[Deepak Kumar], Meher, S.[Sukadev],
Adaptive spatio-temporal background subtraction using improved Wronskian change detection scheme in Gaussian mixture model framework,
IET-IPR(12), No. 10, October 2018, pp. 1832-1843.
DOI Link 1809
BibRef

Panda, D.K.[Deepak Kumar], Meher, S.[Sukadev],
A new Wronskian change detection model based codebook background subtraction for visual surveillance applications,
JVCIR(56), 2018, pp. 52-72.
Elsevier DOI 1811
Visual surveillance, Moving object detection, Background subtraction, Dynamic backgrounds, Codebook model BibRef

Ha, S.V.U.[Synh Viet-Uyen], Tran, D.N.N.[Duong Nguyen-Ngoc], Nguyen, T.P.[Tien Phuoc], Dao, S.V.T.[Son Vu-Truong],
High variation removal for background subtraction in traffic surveillance systems,
IET-CV(12), No. 8, December 2018, pp. 1163-1170.
DOI Link 1812
BibRef

Ortego, D., Sanmiguel, J.C., Martínez, J.M.,
Hierarchical Improvement of Foreground Segmentation Masks in Background Subtraction,
CirSysVideo(29), No. 6, June 2019, pp. 1645-1658.
IEEE DOI 1906
Image segmentation, Motion segmentation, Merging, Adaptation models, Lighting, Optical imaging, Videos, post-processing BibRef

Shen, W.J.[Wen-Jie], Hong, W.[Wen], Han, B.[Bing], Wang, Y.P.[Yan-Ping], Lin, Y.[Yun],
Moving Target Detection with Modified Logarithm Background Subtraction and Its Application to the GF-3 Spotlight Mode,
RS(11), No. 10, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Srivastava, G.[Gargi], Srivastava, R.[Rajeev],
Salient object detection using background subtraction, Gabor filters, objectness and minimum directional backgroundness,
JVCIR(62), 2019, pp. 330-339.
Elsevier DOI 1908
Background subtraction, Gabor filters, Minimum directional backgroundness BibRef

Cocorullo, G.[Giuseppe], Corsonello, P.[Pasquale], Frustaci, F.[Fabio], Guachi-Guachi, L.D.L.A.[Lorena-De-Los-Angeles], Perri, S.[Stefania],
Multimodal background subtraction for high-performance embedded systems,
RealTimeIP(16), No. 5, October 2019, pp. 1407-1423.
Springer DOI 1911
BibRef

Djerida, A.[Achraf], Zhao, Z.H.[Zhong-Hua], Zhao, J.K.[Jian-Kang],
Background subtraction in dynamic scenes using the dynamic principal component analysis,
IET-IPR(14), No. 2, February 2020, pp. 245-255.
DOI Link 2001
BibRef

Rasoulidanesh, M.S.[Maryam S.], Payandeh, S.[Shahram],
A novel change-detection scheduler for a network of depth sensors,
JVCIR(66), 2020, pp. 102733.
Elsevier DOI 2003
Change detection, Depth sensor, Network sensor, Sensor network scheduler, Background subtraction, RGBD tracking BibRef

Lin, C.Y.[Chih-Yang], Muchtar, K.[Kahlil], Lin, W.Y.[Wei-Yang], Jian, Z.Y.[Zhi-Yao],
Moving Object Detection Through Image Bit-Planes Representation Without Thresholding,
ITS(21), No. 4, April 2020, pp. 1404-1414.
IEEE DOI 2004
Computational modeling, Object detection, Image color analysis, Lighting, Adaptation models, Intelligent transportation systems, temporal improvement BibRef

Trigano, T., Bechor, Y.,
Fast background removal of JPEG images based on HSV polygonal cuts for a foot scanner device,
RealTimeIP(17), No. 4, August 2020, pp. 981-992.
WWW Link. 2007
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Reitberger, G.[Günther], Sauer, T.[Tomas],
Background Subtraction using Adaptive Singular Value Decomposition,
JMIV(62), No. 8, October 2020, pp. xx-yy.
WWW Link. 2009
BibRef

Chan, Y.T.[Yi-Tung],
Comprehensive comparative evaluation of background subtraction algorithms in open sea environments,
CVIU(202), 2021, pp. 103101.
Elsevier DOI 2012
Maritime security surveillance, Background subtraction, Maritime visual system, Maritime video processing, Maritime intelligent transportation BibRef

Kushwaha, A.[Arati], Khare, A.[Ashish], Prakash, O.[Om], Khare, M.[Manish],
Dense optical flow based background subtraction technique for object segmentation in moving camera environment,
IET-IPR(14), No. 14, December 2020, pp. 3393-3404.
DOI Link 2012
BibRef

Hossain, M.A.[Md Alamgir], Nguyen, V.D.[Van-Dung], Huh, E.N.[Eui-Nam],
The trade-off between accuracy and the complexity of real-time background subtraction,
IET-IPR(15), No. 2, 2021, pp. 350-368.
DOI Link 2106
BibRef

Nawaz, M.[Mehmood], Yan, H.[Hong],
Saliency Detection Using Deep Features and Affinity-Based Robust Background Subtraction,
MultMed(23), 2021, pp. 2902-2916.
IEEE DOI 2109
Feature extraction, Saliency detection, Object detection, Image reconstruction, Image segmentation, Image color analysis, convolution neural network BibRef

Liu, R.R.[Rong-Rong], Ruichek, Y.[Yassine], El Bagdouri, M.[Mohammed],
Multispectral background subtraction with deep learning,
JVCIR(80), 2021, pp. 103267.
Elsevier DOI 2110
Background subtraction, Multispectral images, Deep learning, Convolutional neural networks BibRef

He, W.[Wei], Li, W.[Wujing], Zhang, G.Y.[Guo-Yun], Tu, B.[Bing], Kwan Kim, Y.[Yong], Wu, J.H.[Jian-Hui], Qi, Q.[Qi],
Detection of moving objects using adaptive multi-feature histograms,
JVCIR(80), 2021, pp. 103278.
Elsevier DOI 2110
Moving object detection, Background subtraction, Local compact binary descriptor, Multi-feature histogram BibRef

Zhang, J.[Jin], Zhang, X.[Xi], Zhang, Y.Y.[Yan-Yan], Duan, Y.X.[Ye-Xin], Li, Y.[Yang], Pan, Z.S.[Zhi-Song],
Meta-Knowledge Learning and Domain Adaptation for Unseen Background Subtraction,
IP(30), 2021, pp. 9058-9068.
IEEE DOI 2112
Semantics, Heuristic algorithms, Inference algorithms, Training, Task analysis, Adaptation models, Image color analysis, frame differencing algorithm BibRef

Zeng, Z.[Zhi], Wang, T.[Ting], Ma, F.[Fulei], Zhang, L.[Liang], Shen, P.[Peiyi], Shah, S.A.A.[Syed Afaq Ali], Bennamoun, M.[Mohammed],
Probability-Based Framework to Fuse Temporal Consistency and Semantic Information for Background Segmentation,
MultMed(24), 2022, pp. 740-754.
IEEE DOI 2202
Semantics, Image segmentation, Deep learning, Training, Visualization, Recurrent neural networks, Merging, the law of total probability BibRef

Sahoo, S.[Subhaluxmi], Nanda, P.K.[Pradipta Kumar],
Adaptive Feature Fusion and Spatio-Temporal Background Modeling in KDE Framework for Object Detection and Shadow Removal,
CirSysVideo(32), No. 3, March 2022, pp. 1103-1118.
IEEE DOI 2203
Adaptation models, Feature extraction, Color, Image color analysis, Object detection, Entropy, Computational modeling, Shadow, entropy map BibRef

Yang, Y.Z.[Yi-Zhong], Ruan, J.H.[Jia-Hao], Zhang, Y.Q.[Yong-Qiang], Cheng, X.[Xin], Zhang, Z.[Zhang], Xie, G.J.[Guang-Jun],
STPNet: A Spatial-Temporal Propagation Network for Background Subtraction,
CirSysVideo(32), No. 4, April 2022, pp. 2145-2157.
IEEE DOI 2204
Videos, Feature extraction, Training, Task analysis, Correlation, Adaptation models, Background subtraction, affinity matrices BibRef

Zhao, C.Q.[Chen-Qiu], Hu, K.K.[Kang-Kang], Basu, A.[Anup],
Universal Background Subtraction Based on Arithmetic Distribution Neural Network,
IP(31), No. 2022, pp. 2934-2949.
IEEE DOI 2204
Arithmetic, Histograms, Training, Deep learning, Neural networks, Streaming media, Kernel, Background subtraction, deep learning, arithmetic distribution operations BibRef

Sultana, M.[Maryam], Mahmood, A.[Arif], Jung, S.K.[Soon Ki],
Unsupervised moving object segmentation using background subtraction and optimal adversarial noise sample search,
PR(129), 2022, pp. 108719.
Elsevier DOI 2206
Moving objects segmentation, Generative adversarial network, Background subtraction BibRef

Liu, Q.[Qi], Li, X.P.[Xiao-Peng],
Efficient Low-Rank Matrix Factorization Based on l1,e-Norm for Online Background Subtraction,
CirSysVideo(32), No. 7, July 2022, pp. 4900-4904.
IEEE DOI 2207
Sparse matrices, Minimization, Tuning, Streaming media, Principal component analysis, Computational modeling, Trajectory, low-rank matrix factorization BibRef

Panda, M.K.[Manoj Kumar], Sharma, A.[Akhilesh], Bajpai, V.[Vatsalya], Subudhi, B.N.[Badri Narayan], Thangaraj, V.[Veerakumar], Jakhetiya, V.[Vinit],
Encoder and decoder network with ResNet-50 and global average feature pooling for local change detection,
CVIU(222), 2022, pp. 103501.
Elsevier DOI 2209
Background subtraction, Convolutional neural networks, Feature Pooling Module (FPM) BibRef

Bou, X.[Xavier], Ehret, T.[Thibaud], Facciolo, G.[Gabriele], Morel, J.M.[Jean-Michel], Grompone von Gioi, R.[Rafael],
Reviewing ViBe, a Popular Background Subtraction Algorithm for Real-Time Applications,
IPOL(12), 2022, pp. 527-549.
DOI Link 2212
Code is available for research purposes only:
WWW Link. BibRef

Chan, Y.T.[Yi-Tung],
Ensemble learning-based method for maritime background subtraction in open sea environments,
CVIU(238), 2024, pp. 103859.
Elsevier DOI 2312
Maritime autonomous surface ships, Background subtraction, Change detection, Ensemble learning, Open sea BibRef

An, Y.Q.[Yong-Qi], Zhao, X.[Xu], Yu, T.[Tao], Gu, H.Y.[Hai-Yun], Zhao, C.Y.[Chao-Yang], Tang, M.[Ming], Wang, J.Q.[Jin-Qiao],
ZBS: Zero-Shot Background Subtraction via Instance-Level Background Modeling and Foreground Selection,
CVPR23(6355-6364)
IEEE DOI 2309
BibRef

Zhen, Y.[Yang], Guo, Y.F.[Yuan-Fang], Wei, J.J.[Jin-Jie], Bao, X.[Xiuguo], Huang, D.[Di],
Multi-Scale Background Suppression Anomaly Detection In Surveillance Videos,
ICIP21(1114-1118)
IEEE DOI 2201
Correlation, Convolution, Surveillance, Image processing, Supervised learning, Interference, Fasteners, weakly supervised learning BibRef

Diamantas, S.[Sotirios], Alexis, K.[Kostas],
Optical Flow Based Background Subtraction with a Moving Camera: Application to Autonomous Driving,
ISVC20(II:398-409).
Springer DOI 2103
BibRef

Shah, M., Cave, V., dos Reis, M.,
Automatically localising ROIs in hyperspectral images using background subtraction techniques,
IVCNZ20(1-6)
IEEE DOI 2012
Subtraction techniques, Cameras, Data models, Sensors, Reliability, Hyperspectral imaging, Testing, hyperspectral images, agriculture BibRef

Cioppa, A., Droogenbroeck, M.V., Braham, M.,
Real-Time Semantic Background Subtraction,
ICIP20(3214-3218)
IEEE DOI 2011
Semantics, Real-time systems, Change detection algorithms, Heuristic algorithms, Image segmentation, Prediction algorithms, real-time processing BibRef

Piérard, S., Droogenbroeck, M.V.,
Summarizing The Performances Of A Background Subtraction Algorithm Measured On Several Videos,
ICIP20(3234-3238)
IEEE DOI 2011
Videos, Probabilistic logic, Prediction algorithms, Extraterrestrial measurements, Error analysis, Harmonic analysis, classification performance BibRef

Arefin, M.R.[Md Rifat], Makhmudkhujaev, F.[Farkhod], Chae, O.[Oksam], Kim, J.[Jaemyun],
Background Subtraction Based on Fusion of Color and Local Patterns,
ACCV18(VI:214-230).
Springer DOI 1906
BibRef

O'Gorman, L.,
Temporal Filter Parameters for Motion Pattern Maps,
ICPR18(2612-2617)
IEEE DOI 1812
Dynamics, Filtering, Heating systems, Density functional theory, Hidden Markov models, Autoregressive processes, background subtraction BibRef

He, W., Kim, Y., Wu, J., Zhang, G., Qi, Q., Guo, L., Tu, B., Huang, F.,
Local Compact Binary Patterns for Background Subtraction in Complex Scenes,
ICPR18(1518-1523)
IEEE DOI 1812
Image color analysis, Binary codes, Colored noise, Spatiotemporal phenomena, Feature extraction, multiple color spaces BibRef

Porr, W.[William], Easton, J.[James], Tavakkoli, A.[Alireza], Loffredo, D.[Donald], Simmons, S.[Sean],
GPU Accelerated Non-Parametric Background Subtraction,
ISVC18(629-639).
Springer DOI 1811
BibRef

Huynh-The, T., Lee, S., Hua, C.H.,
ADM-HIPaR: An efficient background subtraction approach,
AVSS17(1-6)
IEEE DOI 1806
image segmentation, object detection, ADM-HIPaR, adaptive background, automated-directional masking algorithm, Video sequences BibRef

Zang, X., Li, G., Yang, J., Wang, W.,
Adaptive difference modelling for background subtraction,
VCIP17(1-4)
IEEE DOI 1804
Gaussian processes, image sequences, mixture models, object detection, object tracking, video signal processing, surveillance video BibRef

Rezaei, B., Ostadabbas, S.S.,
Background Subtraction via Fast Robust Matrix Completion,
RSL-CV17(1871-1879)
IEEE DOI 1802
Algorithm design and analysis, Computational modeling, Matrix decomposition, Optimization, Robustness, Sparse matrices BibRef

Liu, R.R.[Rong-Rong], Ruichek, Y.[Yassine], El Bagdouri, M.[Mohammed],
Multispectral Dynamic Codebook and Fusion Strategy for Moving Objects Detection,
ICISP20(35-43).
Springer DOI 2009
BibRef
Earlier:
Enhanced Codebook Model and Fusion for Object Detection with Multispectral Images,
ACIVS18(225-232).
Springer DOI 1810
BibRef
Earlier:
Background Subtraction with Multispectral Images Using Codebook Algorithm,
ACIVS17(581-590).
Springer DOI 1712
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

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

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

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

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.Y.[Xin-Yi], 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

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.Y.[Le-Yuan], 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

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

Amato, A.[Ariel], Mozerov, M.G.[Mikhail G.], 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

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

Fukui, S.[Shinji], Iwahori, Y.J.[Yu-Ji], 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).
IEEE DOI 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
Neural Network Guided Background Subtraction, Learning Methods .