18.3.4.8.4 Dynamic Background Subtraction, Moving Camera Background Subtraction

Chapter Contents (Back)
Background. Subtraction.

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

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, Electronic mail, Image sequences, Information science, Lighting. Moving object detection. Background subtraction 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.[Yuhan], Chen, Y.[Youbin],
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, Computer vision, 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, Three-dimensional displays, Training, Videos, Deep neural network, multi-label action recognition BibRef

Yong, H.W.[Hong-Wei], Meng, D.[Deyu], 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


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

Yang, K., Chen, F., Liu, D., Chen, Z., Li, W.,
Surveillance video coding with dynamic textural background detection,
ICIP17(2736-2740)
IEEE DOI 1803
Bit rate, Decoding, Dynamics, Encoding, Heuristic algorithms, Histograms, Surveillance, HEVC, HVS, detection, dynamic texture, surveillance video 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

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.[Yuhan], Guo, Z.H.[Zhen-Hua], Chen, Y.[Youbin],
Background Subtraction with Dynamic Noise Sampling and Complementary Learning,
ICPR14(2341-2346)
IEEE DOI 1412
Algorithm design and analysis 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.[Huijun], 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

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.[Shaohui],
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

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 .


Last update:Oct 15, 2018 at 09:19:25