Ren, Y.[Ying],
Chua, C.S.[Chin-Seng],
Ho, Y.K.[Yeong-Khing],
Statistical background modeling for non-stationary camera,
PRL(24), No. 1-3, January 2003, pp. 183-196.
Elsevier DOI
0211
BibRef
Pless, R.[Robert],
Spatio-temporal Background Models for Outdoor Surveillance,
JASP(2005), No. 14, 2005, pp. 2281-2291.
WWW Link.
PDF File.
0603
BibRef
Pless, R.,
Larson, J.,
Siebers, S.,
Westover, B.,
Evaluation of local models of dynamic backgrounds,
CVPR03(II: 73-78).
IEEE DOI
0307
BibRef
Mittal, A.[Anurag],
Monnet, A.[Antoine],
Paragios, N.[Nikos],
Scene modeling and change detection in dynamic scenes:
A subspace approach,
CVIU(113), No. 1, January 2009, pp. 63-79.
Elsevier DOI
0812
BibRef
Earlier: A1, A3, Only:
Motion-based background subtraction using adaptive kernel density
estimation,
CVPR04(II: 302-309).
IEEE DOI
0408
Scene analysis, Background subtraction, Time series, Principal
component analysis
BibRef
Monnet, A.,
Mittal, A.,
Paragios, N.,
Ramesh, V.,
Background modeling and subtraction of dynamic scenes,
ICCV03(1305-1312).
IEEE DOI
0311
BibRef
Mahadevan, V.[Vijay],
Vasconcelos, N.M.[Nuno M.],
Spatiotemporal Saliency in Dynamic Scenes,
PAMI(32), No. 1, January 2010, pp. 171-177.
IEEE DOI
0912
BibRef
Earlier:
Saliency-based discriminant tracking,
CVPR09(1007-1013).
IEEE DOI
0906
BibRef
Earlier:
Background subtraction in highly dynamic scenes,
CVPR08(1-6).
IEEE DOI
0806
Based on center-surround, inspired by biological systems.
Patches modeled as dynamic textures.
BibRef
Chan, A.B.[Antoni B.],
Mahadevan, V.[Vijay],
Vasconcelos, N.M.[Nuno M.],
Generalized Stauffer-Grimson background subtraction for dynamic scenes,
MVA(22), No. 5, September 2011, pp. 751-766.
WWW Link.
1108
See also Adaptive Background Mixture Models for Real-time Tracking.
BibRef
Chiranjeevi, P.,
Sengupta, S.,
Spatially correlated background subtraction, based on adaptive
background maintenance,
JVCIR(23), No. 6, August 2012, pp. 948-957.
Elsevier DOI
1208
Moving object detection, Background subtraction, Dynamic backgrounds;
Hu moment, Improved Hu moment, Covariance matrix, Spatial correlation;
Adaptive model updating rates
BibRef
Chiranjeevi, P.,
Sengupta, S.,
New Fuzzy Texture Features for Robust Detection of Moving Objects,
SPLetters(19), No. 10, October 2012, pp. 603-606.
IEEE DOI
1209
BibRef
Chiranjeevi, P.,
Sengupta, S.,
Robust detection of moving objects in video sequences through rough set
theory framework,
IVC(30), No. 11, November 2012, pp. 829-842.
Elsevier DOI
1211
Background subtraction, Rough set, 3D histon, 3D fuzzy histon, 3D HRI;
3D FHRI
BibRef
Chiranjeevi, P.,
Sengupta, S.,
Neighborhood Supported Model Level Fuzzy Aggregation for Moving
Object Segmentation,
IP(23), No. 2, February 2014, pp. 645-657.
IEEE DOI
1402
feature extraction
BibRef
Chiranjeevi, P.,
Sengupta, S.,
Detection of Moving Objects Using Multi-channel Kernel Fuzzy
Correlogram Based Background Subtraction,
Cyber(44), No. 6, June 2014, pp. 870-881.
IEEE DOI
1406
Clustering algorithms
BibRef
Haque, M.[Mahfuzul],
Murshed, M.[Manzur],
Perception-Inspired Background Subtraction,
CirSysVideo(23), No. 12, 2013, pp. 2127-2140.
IEEE DOI
1312
BibRef
Earlier:
Background Subtraction for Real-Time Video Analytics Based on
Multi-hypothesis Mixture-of-Gaussians,
AVSS12(166-171).
IEEE DOI
1211
Adaptation models
BibRef
Haque, M.[Mahfuzul],
Murshed, M.[Manzur],
Paul, M.[Manoranjan],
On Stable Dynamic Background Generation Technique Using Gaussian
Mixture Models for Robust Object Detection,
AVSBS08(41-48).
IEEE DOI
0809
See also Commentary Paper 2 on On Stable Dynamic Background Generation Technique Using Gaussian Mixture Models for Robust Object Detection.
See also Commentary Paper 1 on On Stable Dynamic Background Generation Technique Using Gaussian Mixture Models for Robust Object Detection.
BibRef
Narayana, M.[Manjunath],
Hanson, A.R.[Allen R.],
Learned-Miller, E.G.[Erik G.],
Background subtraction: separating the modeling and the inference,
MVA(25), No. 5, July 2014, pp. 1163-1174.
WWW Link.
1407
BibRef
Earlier:
Coherent Motion Segmentation in Moving Camera Videos Using Optical
Flow Orientations,
ICCV13(1577-1584)
IEEE DOI
1403
BibRef
Earlier:
Improvements in Joint Domain-Range Modeling for Background Subtraction,
BMVC12(115).
DOI Link
1301
BibRef
Earlier:
Background modeling using adaptive pixelwise kernel variances in a
hybrid feature space,
CVPR12(2104-2111).
IEEE DOI
1208
Motion segmentation, object detection, optical flow, tracking
BibRef
Zamalieva, D.[Daniya],
Yilmaz, A.[Alper],
Background subtraction for the moving camera: A geometric approach,
CVIU(127), No. 1, 2014, pp. 73-85.
Elsevier DOI
1408
Background subtraction
BibRef
Zamalieva, D.[Daniya],
Yilmaz, A.[Alper],
Davis, J.W.[James W.],
Exploiting Temporal Geometry for Moving Camera Background Subtraction,
ICPR14(1200-1205)
IEEE DOI
1412
BibRef
And:
A Multi-transformational Model for Background Subtraction with Moving
Cameras,
ECCV14(I: 803-817).
Springer DOI
1408
Cameras
BibRef
Lin, L.,
Xu, Y.,
Liang, X.,
Lai, J.,
Complex Background Subtraction by Pursuing Dynamic Spatio-Temporal
Models,
IP(23), No. 7, July 2014, pp. 3191-3202.
IEEE DOI
1407
Adaptation models
BibRef
Yoshinaga, S.[Satoshi],
Shimada, A.[Atsushi],
Nagahara, H.[Hajime],
Taniguchi, R.I.[Rin-Ichiro],
Object detection based on spatiotemporal background models,
CVIU(122), No. 1, 2014, pp. 84-91.
Elsevier DOI
1404
Background model
BibRef
Shimada, A.[Atsushi],
Nonaka, Y.[Yosuke],
Nagahara, H.[Hajime],
Taniguchi, R.I.[Rin-Ichiro],
Case-based background modeling: associative background database towards
low-cost and high-performance change detection,
MVA(25), No. 5, July 2014, pp. 1121-1131.
Springer DOI
1407
BibRef
Earlier: A2, A1, A3, A4:
Evaluation report of integrated background modeling based on
spatio-temporal features,
CDW12(9-14).
IEEE DOI
1207
See also Background light ray modeling for change detection.
BibRef
Abdelwahab, M.A.[Mohamed A.],
Abdelwahab, M.M.[Moataz M.],
Uchiyama, H.[Hideaki],
Shimada, A.[Atsushi],
Taniguchi, R.I.[Rin-Ichiro],
Video Object Segmentation Based on Superpixel Trajectories,
ICIAR16(191-197).
Springer DOI
1608
BibRef
Tanaka, T.[Tatsuya],
Shimada, A.[Atsushi],
Taniguchi, R.I.[Rin-Ichiro],
Yamashita, T.[Takayoshi],
Arita, D.[Daisaku],
Towards Robust Object Detection:
Integrated Background Modeling Based on Spatio-temporal Features,
ACCV09(I: 201-212).
Springer DOI
0909
BibRef
Yoshinaga, S.[Satoshi],
Shimada, A.[Atsushi],
Nagahara, H.[Hajime],
Taniguchi, R.I.[Rin-Ichiro],
Background model based on intensity change similarity among pixels,
FCV13(276-280).
IEEE DOI
1304
BibRef
Minematsu, T.[Tsubasa],
Uchiyama, H.[Hideaki],
Shimada, A.[Atsushi],
Nagahara, H.[Hajime],
Taniguchi, R.I.[Rin-Ichiro],
Adaptive background model registration for moving cameras,
PRL(96), No. 1, 2017, pp. 86-95.
Elsevier DOI
1709
BibRef
Earlier: A1, A2, A3, A4, A5:
Evaluation of foreground detection methodology for a moving camera,
FCV15(1-4)
IEEE DOI
1506
BibRef
Earlier: A1, A3, A5, Only:
Background initialization based on bidirectional analysis and
consensus voting,
ICPR16(126-131)
IEEE DOI
1705
BibRef
Earlier: A1, A2, A3, A4, A5:
Adaptive search of background models for object detection in images
taken by moving cameras,
ICIP15(2626-2630)
IEEE DOI
1512
Analytical models, Cameras, Image sequences,
Information science, Lighting.
Moving object detection. Background subtraction
BibRef
Pham, D.S.[Duc-Son],
Arandjelovic, O.,
Venkatesh, S.,
Detection of Dynamic Background Due to Swaying Movements From Motion
Features,
IP(24), No. 1, January 2015, pp. 332-344.
IEEE DOI
1502
computer vision
BibRef
Arandjelovic, O.,
Pham, D.S.,
Venkatesh, S.,
CCTV Scene Perspective Distortion Estimation From Low-Level Motion
Features,
CirSysVideo(26), No. 5, May 2016, pp. 939-949.
IEEE DOI
1605
Cameras
BibRef
López-Rubio, F.J.[Francisco Javier],
López-Rubio, E.[Ezequiel],
Foreground detection for moving cameras with stochastic approximation,
PRL(68, Part 1), No. 1, 2015, pp. 161-168.
Elsevier DOI
1512
Background modeling
BibRef
Chan, K.L.,
Detection of foreground in dynamic scene via two-step background
subtraction,
MVA(26), No. 6, August 2015, pp. 723-740.
WWW Link.
1508
BibRef
Seo, J.W.[Ja-Won],
Kim, S.D.[Seong Dae],
Dynamic background subtraction via sparse representation of dynamic
textures in a low-dimensional subspace,
SIViP(10), No. 1, January 2016, pp. 29-36.
WWW Link.
1601
BibRef
Ge, W.F.[Wei-Feng],
Guo, Z.H.[Zhen-Hua],
Dong, Y.H.[Yu-Han],
Chen, Y.B.[You-Bin],
Dynamic background estimation and complementary learning for
pixel-wise foreground/background segmentation,
PR(59), No. 1, 2016, pp. 112-125.
Elsevier DOI
1609
Background subtraction (BS)
BibRef
Zeng, Z.[Zhi],
Jia, J.Y.[Jian-Yuan],
Zhu, Z.F.[Zhao-Fei],
Yu, D.[Dalin],
Adaptive maintenance scheme for codebook-based dynamic background
subtraction,
CVIU(152), No. 1, 2016, pp. 58-66.
Elsevier DOI
1609
Background subtraction
BibRef
Wu, Y.,
He, X.,
Nguyen, T.Q.,
Moving Object Detection With a Freely Moving Camera via Background
Motion Subtraction,
CirSysVideo(27), No. 2, February 2017, pp. 236-248.
IEEE DOI
1702
Cameras
BibRef
Yun, K.[Kimin],
Lim, J.[Jongin],
Choi, J.Y.[Jin Young],
Scene conditional background update for moving object detection in a
moving camera,
PRL(88), No. 1, 2017, pp. 57-63.
Elsevier DOI
1703
Background subtraction in a moving camera
BibRef
Sajid, H.[Hasan],
Cheung, S.C.S.[Sen-Ching Samson],
Universal Multimode Background Subtraction,
IP(26), No. 7, July 2017, pp. 3249-3260.
IEEE DOI
1706
BibRef
Earlier:
Background subtraction for static and moving camera,
ICIP15(4530-4534)
IEEE DOI
1512
Cameras, Colored noise, Computational modeling,
Image color analysis, Lighting, Robustness,
Technological innovation, background model bank,
background subtraction, binary classifiers, change detection,
color spaces, foreground segmentation, pixel classification.
BibRef
Zhu, L.,
Hao, Y.,
Song, Y.,
L_1/2 Norm and Spatial Continuity Regularized Low-Rank Approximation
for Moving Object Detection in Dynamic Background,
SPLetters(25), No. 1, January 2018, pp. 15-19.
IEEE DOI
1801
approximation theory, image motion analysis, minimisation,
object detection, statistical analysis, L1/2 norm,
total variation (TV)
BibRef
Li, X.Y.[Xi-Ying],
Li, G.M.[Guo-Ming],
Jiang, Q.Y.[Qian-Yin],
Dynamic background subtraction method based on spatio-temporal
classification,
IET-CV(12), No. 4, June 2018, pp. 492-501.
DOI Link
1805
BibRef
Hou, J.Y.[Jing-Yi],
Wu, X.X.[Xin-Xiao],
Sun, Y.C.[Yu-Chao],
Jia, Y.D.[Yun-De],
Content-Attention Representation by Factorized Action-Scene Network
for Action Recognition,
MultMed(20), No. 6, June 2018, pp. 1537-1547.
IEEE DOI
1805
Deal with irrelevant motion in the background.
Encoding, Event detection, Feature extraction,
Training, Videos, Deep neural network,
multi-label action recognition
BibRef
Yong, H.W.[Hong-Wei],
Meng, D.Y.[De-Yu],
Zuo, W.M.[Wang-Meng],
Zhang, L.[Lei],
Robust Online Matrix Factorization for Dynamic Background Subtraction,
PAMI(40), No. 7, July 2018, pp. 1726-1740.
IEEE DOI
1806
Adaptation models, Cameras, Laplace equations, Mathematical model,
Real-time systems, Robustness, Videos, Backgroun0d subtraction,
subspace learning
BibRef
Sugimura, D.[Daisuke],
Teshima, F.[Fumihiro],
Hamamoto, T.[Takayuki],
Online background subtraction with freely moving cameras using
different motion boundaries,
IVC(76), 2018, pp. 76-92.
Elsevier DOI
1808
Online background subtraction, Freely moving camera,
Interactive image segmentation, Seeds estimation, Motion boundary
BibRef
Dey, B.,
Kundu, M.K.,
Enhanced Macroblock Features for Dynamic Background Modeling in
H.264/AVC Video Encoded at Low Bitrate,
CirSysVideo(28), No. 3, March 2018, pp. 616-625.
IEEE DOI
1804
image enhancement, image motion analysis, image segmentation,
image sequences, object detection, video coding,
video surveillance
BibRef
Wang, G.,
Li, B.,
Zhang, Y.,
Yang, J.,
Background Modeling and Referencing for Moving Cameras-Captured
Surveillance Video Coding in HEVC,
MultMed(20), No. 11, November 2018, pp. 2921-2934.
IEEE DOI
1810
Cameras, Surveillance, Encoding, Redundancy,
High efficiency video coding, Video compression,
background reference
BibRef
Zhao, C.,
Sain, A.,
Qu, Y.,
Ge, Y.,
Hu, H.,
Background Subtraction Based on Integration of Alternative Cues in
Freely Moving Camera,
CirSysVideo(29), No. 7, July 2019, pp. 1933-1945.
IEEE DOI
1907
Cameras, Adaptive optics, Optical sensors, Motion segmentation,
Trajectory, Gaussian mixture model, Background subtraction,
super-pixels
BibRef
Lam, B.S.Y.[Benson S.Y.],
Chu, A.M.Y.[Amanda M.Y.],
Yan, H.,
Statistical bootstrap-based principal mode component analysis for
dynamic background subtraction,
PR(100), 2020, pp. 107153.
Elsevier DOI
2005
Background modeling, Video surveillance,
Principal Component analysis, Statistical mode
BibRef
Zhang, Z.J.[Zhi-Jun],
Chang, Y.[Yi],
Zhong, S.[Sheng],
Yan, L.X.[Lu-Xin],
Zou, X.[Xu],
Learning dynamic background for weakly supervised moving object
detection,
IVC(121), 2022, pp. 104425.
Elsevier DOI
2205
Moving object detection, Dynamic background,
Data-driven discriminative prior, Low-rank framework
BibRef
Kim, W.J.[Woo Jin],
Hwang, S.[Sangwon],
Lee, J.[Junhyeop],
Woo, S.[Sungmin],
Lee, S.Y.[Sang-Youn],
AIBM: Accurate and Instant Background Modeling for Moving Object
Detection,
ITS(23), No. 7, July 2022, pp. 9021-9036.
IEEE DOI
2207
Computational modeling, Object detection, Adaptation models,
Cameras, Computational complexity, Probability density function,
spatio-temporal information
BibRef
Varga, L.A.[Leon Amadeus],
Zell, A.[Andreas],
Tackling the Background Bias in Sparse Object Detection via Cropped
Windows,
VisDrone21(2768-2777)
IEEE DOI
2112
Training, Image resolution, Pipelines, Object detection, Detectors
BibRef
Sultana, M.,
Mahmood, A.,
Bouwmans, T.,
Jung, S.K.,
Dynamic Background Subtraction Using Least Square Adversarial
Learning,
ICIP20(3204-3208)
IEEE DOI
2011
Generators, Training, Video sequences, Cameras, Meteorology, Jitter,
Testing, Dynamic background subtraction,
Change detection
BibRef
Chelly, I.,
Winter, V.,
Litvak, D.,
Rosen, D.,
Freifeld, O.,
JA-POLS: A Moving-Camera Background Model via Joint Alignment and
Partially-Overlapping Local Subspaces,
CVPR20(12582-12591)
IEEE DOI
2008
Cameras, Computational modeling,
Robustness, Training, Principal component analysis
BibRef
Tocker, Y.,
Hagege, R.R.,
Francos, J.M.,
Dynamic Spatial Predicted Background for Video Surveillance,
ICIP19(4005-4009)
IEEE DOI
1910
Background Modeling, Motion Detection, Video Analysis
BibRef
Loveday, M.,
Breckon, T.P.,
On the Impact of Parallax Free Colour and Infrared Image
Co-Registration to Fused Illumination Invariant Adaptive Background
Modelling,
PBVS18(1267-126709)
IEEE DOI
1812
Image color analysis, Adaptation models, Cameras, Lighting,
Synchronization, Visualization, Task analysis
BibRef
Mandal, M.,
Saxena, P.,
Vipparthi, S.K.,
Murala, S.,
CANDID: Robust Change Dynamics and Deterministic Update Policy for
Dynamic Background Subtraction,
ICPR18(2468-2473)
IEEE DOI
1812
Computational modeling, Adaptation models, History, Dynamics,
Subtraction techniques, Task analysis, Streaming media,
background modelling
BibRef
Allebosch, G.[Gianni],
Slembrouck, M.[Maarten],
Roegiers, S.[Sanne],
Luong, H.Q.[Hiêp Quang],
Veelaert, P.[Peter],
Philips, W.[Wilfried],
Foreground Background Segmentation in Front of Changing Footage on a
Video Screen,
ACIVS18(175-187).
Springer DOI
1810
BibRef
Braham, M.,
Piérard, S.,
van Droogenbroeck, M.,
Semantic background subtraction,
ICIP17(4552-4556)
IEEE DOI
1803
Automobiles, Heuristic algorithms, Motion detection,
Motion segmentation, Optimized production technology, Semantics,
semantic segmentation
BibRef
Huang, Z.,
Hu, R.,
Thierry, B.,
Chen, S.,
Multi-feature fusion based background subtraction for video sequences
with strong background changes,
ICIP17(3370-3374)
IEEE DOI
1803
Computational modeling, Feature extraction, Fuses,
Image reconstruction, Kernel, Lighting, Video sequences,
Feature Fusion
BibRef
Galoogahi, H.K.,
Fagg, A.,
Lucey, S.,
Learning Background-Aware Correlation Filters for Visual Tracking,
ICCV17(1144-1152)
IEEE DOI
1802
correlation methods, feature extraction, image filtering,
learning (artificial intelligence), object tracking, photometry,
Visualization
BibRef
Zhu, Y.,
Elgammal, A.M.[Ahmed M.],
A Multilayer-Based Framework for Online Background Subtraction with
Freely Moving Cameras,
ICCV17(5142-5151)
IEEE DOI
1802
Bayes methods, Markov processes, graph theory, image filtering,
image motion analysis, image segmentation, image sequences,
Trajectory
BibRef
Tufano, F.[Francesco],
Angelino, C.V.[Cesario Vincenzo],
Cicala, L.[Luca],
Visual Target Detection and Tracking in UAV EO/IR Videos by Moving
Background Subtraction,
ACIVS16(547-558).
Springer DOI
1611
BibRef
Mukojima, H.,
Deguchi, D.,
Kawanishi, Y.,
Ide, I.,
Murase, H.,
Ukai, M.,
Nagamine, N.,
Nakasone, R.,
Moving camera background-subtraction for obstacle detection on
railway tracks,
ICIP16(3967-3971)
IEEE DOI
1610
Cameras
BibRef
Bechar, I.,
Lelore, T.,
Bouchara, F.,
Guis, V.,
Grimaldi, M.,
Object segmentation from a dynamic background using a pixelwise
rigidity criterion and application to maritime target recognition,
ICIP14(363-367)
IEEE DOI
1502
Cameras
BibRef
Gallego, J.[Jaime],
Bertolino, P.[Pascal],
Foreground object segmentation for moving camera sequences based on
foreground-background probabilistic models and prior probability maps,
ICIP14(3312-3316)
IEEE DOI
1502
Cameras
BibRef
Berger, M.[Matthew],
Seversky, L.M.[Lee M.],
Subspace Tracking under Dynamic Dimensionality for Online Background
Subtraction,
CVPR14(1274-1281)
IEEE DOI
1409
background subtraction;subspace tracking
BibRef
Ge, W.F.[Wei-Feng],
Dong, Y.H.[Yu-Han],
Guo, Z.H.[Zhen-Hua],
Chen, Y.B.[You-Bin],
Background Subtraction with Dynamic Noise Sampling and Complementary
Learning,
ICPR14(2341-2346)
IEEE DOI
1412
Algorithm design and analysis
BibRef
Molina-Giraldo, S.[Santiago],
Video Segmentation Framework by Dynamic Background Modelling,
CIAP13(I:843-852).
Springer DOI
1311
BibRef
Shi, X.[Xun],
Tsotsos, J.K.[John K.],
Background subtraction via early recurrence in dynamic scenes,
ICPR12(3172-3175).
WWW Link.
1302
BibRef
Elqursh, A.[Ali],
Elgammal, A.M.[Ahmed M.],
Video figure ground labeling,
ICPR12(2472-2475).
WWW Link.
1302
BibRef
Earlier:
Online Moving Camera Background Subtraction,
ECCV12(VI: 228-241).
Springer DOI
1210
BibRef
Marie, R.[Romain],
Potelle, A.[Alexis],
Mouaddib, E.[El_Mustapha],
Dynamic background subtraction using moments,
ICIP11(2369-2372).
IEEE DOI
1201
BibRef
Gong, M.L.[Ming-Lun],
Cheng, L.[Li],
Incorporating estimated motion in real-time background subtraction,
ICIP11(3265-3268).
IEEE DOI
1201
BibRef
Earlier: A2, A1:
Realtime background subtraction from dynamic scenes,
ICCV09(2066-2073).
IEEE DOI
0909
See also Real-time foreground segmentation on GPUs using local online learning and global graph cut optimization.
BibRef
Sheikh, Y.[Yaser],
Javed, O.[Omar],
Kanade, T.[Takeo],
Background Subtraction for Freely Moving Cameras,
ICCV09(1219-1225).
IEEE DOI
0909
BibRef
Zhong, B.N.[Bi-Neng],
Liu, S.H.[Shao-Hui],
Yao, H.X.[Hong-Xun],
Zhang, B.C.[Bao-Chang],
Multl-resolution background subtraction for dynamic scenes,
ICIP09(3193-3196).
IEEE DOI
0911
BibRef
Jin, Y.X.[Yu-Xin],
Tao, L.M.[Lin-Mi],
Di, H.J.[Hui-Jun],
Rao, N.I.[Naveed I.],
Xu, G.Y.[Guang-You],
Background modeling from a free-moving camera by Multi-Layer Homography
Algorithm,
ICIP08(1572-1575).
IEEE DOI
0810
BibRef
Visentini, I.[Ingrid],
Commentary Paper 1 on 'On Stable Dynamic Background Generation
Technique Using Gaussian Mixture Models for Robust Object Detection',
AVSBS08(49-49).
IEEE DOI
0809
See also On Stable Dynamic Background Generation Technique Using Gaussian Mixture Models for Robust Object Detection.
BibRef
Sofka, M.[Michal],
Commentary Paper 2 on 'On Stable Dynamic Background Generation
Technique Using Gaussian Mixture Models for Robust Object Detection',
AVSBS08(50-51).
IEEE DOI
0809
See also On Stable Dynamic Background Generation Technique Using Gaussian Mixture Models for Robust Object Detection.
BibRef
Lee, H.[Huang],
Wu, C.[Chen],
Aghajan, H.,
Nonstationary Background Removal Via Multiple Camera Collaboration,
ICDSC07(321-327).
IEEE DOI
0709
BibRef
Zhong, B.N.[Bi-Neng],
Yao, H.X.[Hong-Xun],
Shan, S.G.[Shi-Guang],
Chen, X.L.[Xi-Lin],
Gao, W.[Wen],
Hierarchical background subtraction using local pixel clustering,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Zhang, S.P.[Sheng-Ping],
Yao, H.X.[Hong-Xun],
Liu, S.H.[Shao-Hui],
Chen, X.L.[Xi-Lin],
Gao, W.[Wen],
A covariance-based method for dynamic background subtraction,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Zhang, S.P.[Sheng-Ping],
Yao, H.X.[Hong-Xun],
Liu, S.H.[Shao-Hui],
Dynamic background modeling and subtraction using spatio-temporal local
binary patterns,
ICIP08(1556-1559).
IEEE DOI
0810
BibRef
Zhang, S.P.[Sheng-Ping],
Yao, H.X.[Hong-Xun],
Liu, S.H.[Shao-Hui],
Dynamic Background Subtraction Based on Local Dependency Histogram,
VS08(xx-yy).
0810
BibRef
Dalley, G.[Gerald],
Migdal, J.[Joshua],
Grimson, W.E.L.[W. Eric L.],
Background Subtraction for Temporally Irregular Dynamic Textures,
WACV08(1-7).
IEEE DOI
0801
BibRef
Shimada, A.[Atsushi],
Yoshinaga, S.[Satoshi],
Taniguchi, R.I.[Rin-Ichiro],
Adaptive Background Modeling for Paused Object Regions,
VS10(12-22).
Springer DOI
1109
BibRef
Tanaka, T.[Tatsuya],
Shimada, A.[Atsushi],
Arita, D.[Daisaku],
Taniguchi, R.I.[Rin-Ichiro],
A fast algorithm for adaptive background model construction using
parzen density estimation,
AVSBS07(528-533).
IEEE DOI
0709
BibRef
Park, D.Y.[Dae-Yong],
Kim, J.B.[Jun-Beom],
Kim, J.[Jaemin],
Cho, S.W.[Seong-Won],
Chung, S.T.[Sun-Tae],
Motion Detection in Complex and Dynamic Backgrounds,
PSIVT06(545-552).
Springer DOI
0612
BibRef
Adam, A.,
Rivlin, E.,
Shimshoni, I.,
Aggregated Dynamic Background Modeling,
ICIP06(3313-3316).
IEEE DOI
0610
BibRef
Boulanger, J.,
Kervrann, C.,
Bouthemy, P.,
Estimation of Dynamic Background for Fluorescence Video-Microscopy,
ICIP06(2509-2512).
IEEE DOI
0610
BibRef
Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Motion Sequence, Color Models for Background Subtraction .