18.3.4.13.4 Neural Network Guided Background Subtraction, Learning Methods

Chapter Contents (Back)
Subtraction. Background Subtraction. Background. Neural Netowk. Learning.

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


Giraldo, J.H.[Jhony H.], Bouwmans, T.[Thierry],
GraphBGS: Background Subtraction via Recovery of Graph Signals,
ICPR21(6881-6888)
IEEE DOI 2105
Deep learning, Machine learning algorithms, Change detection algorithms, Pipelines, Signal processing 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, Pattern recognition, 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, Conferences, 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.,
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
computer vision, 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 .


Last update:Oct 24, 2021 at 16:35:58