Features used in the literature are computed on entities of different size:
Pixel (Most of the papers)
Keypoint (2 papers)
D. Avola, M. Bernardi, M. Cascio, L. Cinque, G. Foresti, C. Massaroni, "A New Descriptor for Keypoint-Based Background Modeling", ICIAP 2019, 2019.
X. Zhao, G. Wang, Z. He, D. Liang, S. Zhang, J. Tan, “Unsupervised inner-point-pairs model for unseen-scene and online moving object detection”, The Visual Computer, February 2022.
Superpixel (13 papers)
J. Lim, B. Han, "Generalized background subtraction using superpixels with label integrated motion estimation", European Conference on Computer Vision, ECCV 2014, pages 173-187, 2014.
D. Giordano, F. Murabito, S. Palazzo, C. Spampinato,"Superpixel based video object segmentation using perceptual organization and location prior", International Conference on Computer Vision and Pattern Recognition, CVPR 2015, pages 4814-4822, 2015.
S. Javed, S. Oh, A. Sobral, T. Bouwmans, S. Jung, "Background Subtraction via Superpixel-based Online Matrix Decomposition with Structured Foreground
Constraints", Workshop on Robust Subspace Learning and Computer Vision, ICCV 2015, Santiago, Chile, December 2015.
S. Javed, A. Mahmood, T. Bouwmans, S. Jung, "Superpixels based Manifold Structured Sparse RPCA for Moving Object Detection", International Workshop on Activity Monitoring by Multiple Distributed Sensing, BMVC 2017, London, UK, September 2017.
S. Erfanian Ebadi, E. Izquierdo, "Foreground Segmentation via Dynamic Tree-Structured Sparse RPCA", European Conference on Computer Vision, ECCV 2016, 2016.
S. Erfanian Ebadi, E. Izquierdo, "Foreground Detection with Dynamic Tree-Structured Sparse RPCA", IEEE Transactions on Pattern Analysis and Machine Intelligence", 2017.
M. Chen, Q. Yang, Q. Li, G. Wang, M. Yang, “Spatiotemporal GMM for Background Subtraction with Superpixel Hierarchy”, IEEE Transactions on Pattern
Analysis and Machine Intelligence, 2017.
C. Zhao, T. Zhang, Q. Huang, X. Zhang, D. Yang, Y. Qu, S. Huang, "Background Subtraction based on Superpixels under Multi-scale in Complex Scenes", Chinese Conference on Pattern Recognition CCPR 2016, pages,392-403, 2016.
W. Fang, T. Zhang, C. Zhao, D. Soomro, R. Taj, H. Hu, "Background Subtraction based on Random Superpixels under Multiple Scales for Video Analytics", IEEE Access, June 2018.
Y. Chen, Z. Sun, K. Lam, "An Effective Sub-Superpixel-Based Approach for Background Subtraction", IEEE Transactions on Industrial Electronics, 2019.
L. Gao, Y. Huang, A. Maier, "Superpixel-Based Background Recovery from Multiple Images", Preprint, 2019.
R. Kalsotra, S. Arora, “Superpixels-Guided Background Modeling Approach for Foreground Detection”, Recent Innovations in Computing, pages 305-315, March 2022.
J. Feng, P. Liu, Y. Kim, Foreground Detection Based on Superpixel and Semantic Segmentation Hindawi Computational Intelligence and Neuroscience, September 2022.
Block (2 papers)
D. Lin, D. Cao, H. Zeng, “Improving Motion State Change Object Detection by Using Block Background Context”, UCKI 2014, 2014.
M. Savas¸ H. Demirel, B. Erkal, “Moving object detection using an adaptive background subtraction method based on block-based structure in dynamic scene”, Optik 2018, pages 605-618, 2018.
Cluster (4 papers)
H. Bhaskar, L. Mihaylova, S. Maskell, “Background Modeling Using Adaptive Cluster Density Estimation”, LNCS from the 3rd German Workshop on Sensor Data Fusion: Trends, Solutions, Applications, Universität Bremen, Germany, September 2007.
H. Bhaskar, L. Mihaylova, A. Achim, “Object Detection Based on Adaptive Background Subtraction Using Alpha Stable Distribution”, The Institution of Engineering and Technology Conference on Target Tracking and Data Fusion, UK, April 2008.
H. Bhaskar, L. Mihaylova, A. Achim, “Video foreground detection based on symmetric alpha-stable mixture models”, IEEE Transactions on Circuits, Systems and Video Technology, March 2010.
D. Park, H. Byun, “Object-wise multilayer background ordering for public area surveillance”, International Conference on Advanced Video and Signal Based Surveillance, AVSS 2009, pages 484-489, September 2009.
Region (9 papers)
S. Zhang, H. Yao, S. Liu, X. Chen, W. Gao, “A covariance-based method for dynamic background subtraction”, ICPR 2008, pages 1-4, 2008.
P. Chiranjeevi, S. Sengupta, “Moving object detection in the presence of dynamic backgrounds using intensity and textural features”, Journal of Electronics and Imaging, December 2011.
P. Chiranjeevi, S. Sengupta, “Spatially correlated background subtraction, based on adaptive background maintenance”, Journal of Visual Communication and Image Representation, June 2012.
M. Izadi, P. Saeedi, “Robust region-based background subtraction and shadow removing using color and gradient information”, International Conference on Pattern Recognition, ICPR 2008, pages 1-5, December 2008.
P. Varcheie, M. Sills-Lavoie, G. Bilodeau, “An Efficient Region-Based Background Subtraction Technique”, CRV 2008, 2008.
P. Varcheie, M. Sills-Lavoie, G. Bilodeau, “A Multiscale Region-Based Motion Detection and Background Subtraction Algorithm”, Sensors 2010, pages 1041-1061, 2010.
M. Balcilar, A. Coskun Sonmez, “Region Based Fuzzy Background Subtraction Using Choquet Integral”, Adaptive and Natural Computing Algorithms, Volume 7824, 2013, pages 287-296, 2013.
W. Rodhetbhai, P. Lewis “Salient Region Filtering for Background Subtraction”, Advances in Visual Information Systems, Volume 4781, pages 126-135, 2007.
S. Huang, L. Fu, P. Hsiao, “Region-Level Motion-Based Background Modeling and Subtraction Using MRFs”, IEEE Transactions On Image Processing, Volume 16, No. 5, May 2007.
Brick (5 papers)
L. Lin, Y. Xu, X. Liang, “Complex Background Subtraction by Pursuing Dynamic Spatio-temporal Manifolds”, IEEE Transactions on Circuits and Systems for Video Technology, TCSVT 2013, 2013.
Y. Xu, L. Lin, “Moving Object Segmentation by Pursuing Local Spatio-Temporal Manifolds”, Master Thesis, 2012.
Y. Zhao, X. Song; Y. Jia, “On the dimensionality of video bricks under varying illumination”, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012, pages 222-229, June 2012.
Y. Zhao, H. Gong, Y. Jia, S. Zhu, “Background modeling by subspace learning on spatio-temporal patches”, Pattern Recognition Letters, Volume 33, pages 1134-1147, 2012.
Y. Zhao, H. Gong , L. Lin, Y. Jia, “Spatio-Temporal Patches for Night Background Modeling by Subspace Learning”, International Conference on Pattern Recognition, ICPR 2008, pages 1-4, December 2008.
Patch (4 papers)
B. Zhong, H. Yao, S. Liu, “Neighboring image patches embedding for background modeling”, International Conference on Image Processing, ICIP 2009, 2009.
B. Zhong, Y. Chen, Y. Chen, R. Ji, Y. Chen, D. Chen, H. Wang, “Background subtraction driven seeds selection for moving objects segmentation and matting”, Neurocomputing, Volume 103, pages 132-142, March 2013.
H. Fan, J. Guo, J. Li, “Patchnet-based background subtraction algorithm for dynamic scenes video”, ICIC Express Letters, Volume 9, Issue 4, pages 1101-1107, January 2015.
L. Yang, H. Cheng, J. Su, X. Li, “Pixel-to-Model Distance for Robust Background Reconstruction”, IEEE Transactions on Circuits and Systems for Video Technology, April 2015.
Semantic (7 papers)
B. Laugraud, S. Pierard, M. Van Droogenbroeck,"LaBGen-P-Semantic: A First Step for Leveraging Semantic Segmentation in Background Generation", MDPI Journal of Imaging Volume 4, No. 7, Art. 86, 2018.
A. Savakis, A. Shringarpure, “Semantic Background Estimation in Video Sequences”, IEEE International Conference on Signal Processing and Integrated Networks, SPIN 2018, pages 597-601, 2018.
M. Braham, S. Pierard, M. Van Droogenbroeck. “Semantic Background Subtraction”, IEEE International Conference on Image Processing, ICIP 2017, 2017.
A. Cioppa, M. Van Droogenbroeck, M. Braham, “Real-Time Semantic Background Subtraction”, Preprint, February 2020.
C. Lin, B. Yan, W. Tan, "Foreground Detection in Surveillance Video with Fully Convolutional Semantic Network", IEEE International Conference on Image Processing, ICIP 2018, pages 4118-4122, Athens, Greece, October 2018.
D. Zeng, X. Chen, M. Zhu, M. Goesele,A. Kuijper, "Background Subtraction with Real-time Semantic Segmentation", IEEE Access, 2019.
E. Simcek, F. Negin, G. Ozyer, B. Ozyer “Leveraging foreground–background cues for semantically-driven, training-free moving object detection”, Engineering Applications of Artificial Intelligence, Volume 136, October 2024.
Fair Use Policy
As this website gives many information that come from my research, please cite my following papers:
C. Silva, T. Bouwmans, C. Frelicot, 'An eXtended Center-Symmetric Local Binary Pattern for Background Modeling and Subtraction in Videos", VISAPP 2015, Berlin, Germany, March 2015.
C. Silva, T. Bouwmans, C. Frelicot, "Online Weighted One-Class Ensemble for Feature Selection in Background/Foreground Separation", International Conference on Pattern Recognition, ICPR 2016, December 2016.
C. Pacheco, T. Bouwmans, C. Frelicot,"Superpixel-based online wagging one-class ensemble for feature selection in foreground/background separation", Pattern Recognition Letters, 2017.
T. Bouwmans, C. Silva, C. Marghes, M. Zitouni, H. Bhaskar, C. Frelicot, “On the Role and the Importance of Features for Background Modeling and Foreground Detection”, Computer Science Review, Volume 28, pages 26-91, May 2018
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