Lee, D.S.[Dar-Shyang],
Effective Gaussian Mixture Learning for Video Background Subtraction,
PAMI(27), No. 5, May 2005, pp. 827-832.
IEEE Abstract.
0501
BibRef
Earlier:
Online Adaptive Gaussian Mixture Learning for Video Applications,
SMVP04(105-116).
Springer DOI
0505
Adapt the measure for each frame.
BibRef
Tsai, Y.P.[Yu-Pao],
Ko, C.H.[Cheng-Hung],
Hung, Y.P.[Yi-Ping],
Shih, Z.C.[Zen-Chung],
Background Removal of Multiview Images by Learning Shape Priors,
IP(16), No. 10, October 2007, pp. 2607-2616.
IEEE DOI
0711
BibRef
Earlier: A2, A1, A4, A3:
A New Image Segmentation Method for Removing Background of Object
Movies by Learning Shape Priors,
ICPR06(I: 323-326).
IEEE DOI
0609
BibRef
Zha, Y.F.[Yu-Fei],
Bi, D.Y.[Du-Yan],
Yang, Y.[Yuan],
Learning complex background by multi-scale discriminative model,
PRL(30), No. 11, 1 August 2009, pp. 1003-1014,.
Elsevier DOI
0909
Background subtraction, Multi-scale, Kernel density estimation;
AdaBoost, Markov random field
See also Graph-based transductive learning for robust visual tracking.
BibRef
Bouwmans, T.[Thierry],
Subspace Learning for Background Modeling: A Survey,
RPCS(2), No. 3, November 2009, pp. 223-234.
WWW Link.
1001
Survey, Motion Detection.
BibRef
Zhao, C.[Cong],
Wang, X.G.[Xiao-Gang],
Cham, W.K.[Wai-Kuen],
Background Subtraction via Robust Dictionary Learning,
JIVP(2011), No. 2011, pp. xx-yy.
DOI Link
1103
BibRef
Haines, T.S.F.[Tom S. F.],
Xiang, T.[Tao],
Active Rare Class Discovery and Classification Using Dirichlet
Processes,
IJCV(106), No. 3, February 2014, pp. 315-331.
WWW Link.
1402
BibRef
Earlier:
Background Subtraction with Dirichlet Processes,
ECCV12(IV: 99-113).
Springer DOI
1210
BibRef
Earlier:
Delta-Dual Hierarchical Dirichlet Processes:
A pragmatic abnormal behaviour detector,
ICCV11(2198-2205).
IEEE DOI
1201
BibRef
And:
Active Learning using Dirichlet Processes for Rare Class Discovery and
Classification,
BMVC11(xx-yy).
HTML Version.
1110
BibRef
Haines, T.S.F.[Tom S. F.],
Xiang, T.[Tao],
Background Subtraction with Dirichlet Process Mixture Models,
PAMI(36), No. 4, April 2014, pp. 670-683.
IEEE DOI
1404
Bayes methods
BibRef
Maddalena, L.[Lucia],
Petrosino, A.[Alfredo],
The 3dSOBS+ algorithm for moving object detection,
CVIU(122), No. 1, 2014, pp. 65-73.
Elsevier DOI
1404
BibRef
Earlier:
The SOBS algorithm: What are the limits?,
CDW12(21-26).
IEEE DOI
1207
BibRef
And:
3D Neural Model-Based Stopped Object Detection,
CIAP09(585-593).
Springer DOI
0909
Self-Organizing Background Subtraction .
Background subtraction.
BibRef
Ferone, A.,
Maddalena, L.,
Neural Background Subtraction for Pan-Tilt-Zoom Cameras,
SMCS(44), No. 5, May 2014, pp. 571-579.
IEEE DOI
1405
cameras
BibRef
Lee, S.H.[Se-Ho],
Kang, J.W.[Je-Won],
Kim, C.S.[Chang-Su],
Compressed Domain Video Saliency Detection Using Global and Local
Spatiotemporal Features,
JVCIR(35), No. 1, 2016, pp. 169-183.
Elsevier DOI
1602
Video saliency detection
BibRef
Lee, S.H.[Se-Ho],
Kim, J.H.[Jin-Hwan],
Choi, K.P.[Kwang Pyo],
Sim, J.Y.[Jae-Young],
Kim, C.S.[Chang-Su],
Video saliency detection based on spatiotemporal feature learning,
ICIP14(1120-1124)
IEEE DOI
1502
Feature extraction
BibRef
Lee, D.Y.[Dae-Youn],
Ahn, J.K.[Jae-Kyun],
Kim, C.S.[Chang-Su],
Fast background subtraction algorithm using two-level sampling and
silhouette detection,
ICIP09(3177-3180).
IEEE DOI
0911
BibRef
Babaee, M.[Mohammadreza],
Dinh, D.T.[Duc Tung],
Rigoll, G.[Gerhard],
A deep convolutional neural network for video sequence background
subtraction,
PR(76), No. 1, 2018, pp. 635-649.
Elsevier DOI
1801
Background subtraction
BibRef
Zhu, X.[Xuan],
Zhang, C.[Chao],
Xue, J.P.[Jia-Ping],
Guo, Z.P.[Zhen-Peng],
Wang, R.Z.[Rong-Zhi],
Jin, Y.Y.[Yu-Ying],
Background subtraction via time continuity and texture consistency
constraints,
JOSA-A(36), No. 9, September 2019, pp. 1495-1504.
DOI Link
1912
Image processing, Machine vision, Matrix methods,
Neural networks, Object detection, Optical flow
BibRef
Nguyen, T.P.[Tien Phuoc],
Pham, C.C.[Cuong Cao],
Ha, S.V.U.[Synh Viet-Uyen],
Jeon, J.W.[Jae Wook],
Change Detection by Training a Triplet Network for Motion Feature
Extraction,
CirSysVideo(29), No. 2, February 2019, pp. 433-446.
IEEE DOI
1902
Feature extraction, Adaptation models, Computational modeling,
Training, Image color analysis, Dynamics, Image segmentation,
video analysis
BibRef
Nguyen, T.T.[Thuy Tuong],
Jeon, J.W.[Jae Wook],
Real-Time Background Compensation for PTZ Cameras Using GPU Accelerated
and Range-Limited Genetic Algorithm Search,
PSIVT11(I: 85-96).
Springer DOI
1111
BibRef
Zhao, C.,
Basu, A.,
Dynamic Deep Pixel Distribution Learning for Background Subtraction,
CirSysVideo(30), No. 11, November 2020, pp. 4192-4206.
IEEE DOI
2011
Bayes methods, Deep learning, Convolution, Training,
Feature extraction, Videos, Convolutional neural networks,
random permutation
BibRef
Xue, Z.,
Yuan, X.,
Yang, Y.,
Denoising-Based Turbo Message Passing for Compressed Video Background
Subtraction,
IP(30), 2021, pp. 2682-2696.
IEEE DOI
2102
Message passing, Image coding, Approximation algorithms,
Sparse matrices, Estimation, Optical imaging, Neural networks, turbo principle
BibRef
Vijayan, M.[Midhula],
Raguraman, P.[Preeth],
Mohan, R.,
A Fully Residual Convolutional Neural Network for Background
Subtraction,
PRL(146), 2021, pp. 63-69.
Elsevier DOI
2105
Background subtraction, Background image,
Fully residual convolutional neural network (FR-CNN), Optical flow
BibRef
Watanabe, R.[Ryosuke],
Chen, J.[Jun],
Konno, T.[Tomoaki],
Naito, S.[Sei],
Accurate Background Subtraction Using Dynamic Object Presence
Probability in Sports Scenes,
ICPR21(2521-2528)
IEEE DOI
2105
Deep learning, Pose estimation, Probability,
Object recognition, Optical flow, Tuning
BibRef
Giraldo, J.H.,
Bouwmans, T.,
Semi-Supervised Background Subtraction Of Unseen Videos:
Minimization of the Total Variation Of Graph Signals,
ICIP20(3224-3228)
IEEE DOI
2011
Videos, TV, Minimization, Semisupervised learning,
Change detection algorithms, Classification algorithms, Training,
unseen videos
BibRef
Minematsu, T.,
Shimada, A.,
Taniguchi, R.i.,
Rethinking Background And Foreground In Deep Neural Network-Based
Background Subtraction,
ICIP20(3229-3233)
IEEE DOI
2011
Image segmentation, Training, Neural networks, Automobiles,
Visualization, Feature extraction, Deep neural network
BibRef
Tezcan, M.O.,
Ishwar, P.,
Konrad, J.,
BSUV-Net: A Fully-Convolutional Neural Network for Background
Subtraction of Unseen Videos,
WACV20(2763-2772)
IEEE DOI
2006
Videos, Training, Prediction algorithms, Lighting, Semantics,
Neural networks, Computational modeling
BibRef
Choo, S.[Sungkwon],
Seo, W.[Wonkyo],
Jeong, D.J.[Dong-Ju],
Cho, N.I.[Nam Ik],
Learning Background Subtraction by Video Synthesis and Multi-scale
Recurrent Networks,
ACCV18(VI:357-372).
Springer DOI
1906
BibRef
Bakkay, M.C.,
Rashwan, H.A.,
Salmane, H.,
Khoudour, L.,
Puigtt, D.,
Ruichek, Y.[Yassine],
BSCGAN: Deep Background Subtraction with Conditional Generative
Adversarial Networks,
ICIP18(4018-4022)
IEEE DOI
1809
Training, Generators, Machine learning, Convolution,
Generative adversarial networks, Computational modeling, PSNR,
deep learning
BibRef
Gao, Y.,
Cai, H.,
Zhang, X.,
Lan, L.,
Luo, Z.,
Background Subtraction via 3D Convolutional Neural Networks,
ICPR18(1271-1276)
IEEE DOI
1812
convolution, feedforward neural nets,
image classification, traffic engineering computing,
3D Convolutional Neural Networks
BibRef
Lim, K.,
Jang, W.D.,
Kim, C.S.,
Background subtraction using encoder-decoder structured convolutional
neural network,
AVSS17(1-6)
IEEE DOI
1806
feature extraction, feedforward neural nets, image coding,
image motion analysis, image segmentation, object detection,
Neural networks
BibRef
Scherzinger, A.[Aaron],
Klemm, S.[Sören],
Berh, D.[Dimitri],
Jiang, X.Y.[Xiao-Yi],
CNN-Based Background Subtraction for Long-Term In-Vial FIM Imaging,
CAIP17(I: 359-371).
Springer DOI
1708
BibRef
Roy, K.,
Kim, J.,
Iqbal, M.T.B.,
Makhmudkhujaev, F.,
Ryu, B.,
Chae, O.,
An adaptive fusion scheme of color and edge features for background
subtraction,
AVSS17(1-6)
IEEE DOI
1806
feature extraction, learning (artificial intelligence),
object detection, video signal processing, video surveillance,
Shape
BibRef
Sobral, A.[Andrews],
Baker, C.G.[Christopher G.],
Bouwmans, T.[Thierry],
Zahzah, E.H.[El-Hadi],
Incremental and Multi-feature Tensor Subspace Learning Applied for
Background Modeling and Subtraction,
ICIAR14(I: 94-103).
Springer DOI
1410
BibRef
Fard, H.O.[Hamidreza Odabai],
Chaouch, M.[Mohamed],
Pham, Q.C.[Quoc-Cuong],
Vacavant, A.[Antoine],
Chateau, T.[Thierry],
Joint hierarchical learning for efficient multi-class object
detection,
WACV14(261-268)
IEEE DOI
1406
Detectors;
classifying a dominating background label.
BibRef
Vacavant, A.[Antoine],
Chateau, T.[Thierry],
Wilhelm, A.[Alexis],
Lequièvre, L.[Laurent],
A Benchmark Dataset for Outdoor Foreground/Background Extraction,
BMC12(I:291-300).
Springer DOI
1304
Dataset, Foreground Extraction. Surveillance applications.
BibRef
Dhome, Y.[Yoann],
Tronson, N.[Nicolas],
Vacavant, A.[Antoine],
Chateau, T.[Thierry],
Gabard, C.[Christophe],
Goyat, Y.[Yann],
Gruyer, D.[Dominique],
A benchmark for Background Subtraction Algorithms in monocular vision:
A comparative study,
IPTA10(66-71).
IEEE DOI
1007
BibRef
Singh, A.[Abhishek],
Jaikumar, P.[Padmini],
Mitra, S.K.[Suman K.],
A Sampling-Resampling Based Bayesian Learning Approach for Object
Tracking,
ICCVGIP08(442-449).
IEEE DOI
0812
BibRef
Jaikumar, P.,
Singh, A.,
Mitra, S.K.,
Background Subtraction in Videos using Bayesian Learning with Motion
Information,
BMVC08(xx-yy).
PDF File.
0809
BibRef
Wang, L.[Lu],
Wang, L.[Lei],
Wen, M.[Ming],
Zhuo, Q.[Qing],
Wang, W.Y.[Wen-Yuan],
Background Subtraction using Incremental Subspace Learning,
ICIP07(V: 45-48).
IEEE DOI
0709
BibRef
Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Dynamic Background Subtraction, Moving Camera Background Subtraction .