Surveys

Traditionnal General Surveys (3 papers)

A. Mc Ivor, “Background Subtraction Techniques”, International Conference on Image and Vision Computing New Zealand,  IVCNZ 2000, Hamilton, New Zealand, November 2000.

M. Piccardi, “Background subtraction techniques: a review”, IEEE  International Conference on Systems, Man and Cybernetics, October 2004.

S. Cheung, C. Kamath, “Robust Background Subtraction with Foreground Validation for Urban Traffic Video”, EURASIP Journal of Applied Signal Processing, Special Issue on Advances in Intelligent Vision Systems: Methods and Applications, New York, USA, 2005.

Recent General Surveys (4 papers)

S. Elhabian, K.  El-Sayed, S. Ahmed, “Moving Object Detection in Spatial Domain using Background Removal Techniques - State-of-Art”, Recent Patents on Computer Science, Volume 1, Number 1, pages 32-54, January 2008.

M. Cristani, M. Farenzena, D. Bloisi, V. Murino, “Background Subtraction for Automated Multisensor Surveillance: A Comprehensive Review”, EURASIP Journal on Advances in Signal Processing, 24 pages, Volume 2010, 2010.

H. Hassanpour, M. Sedighi, A. Manashty, “Video Frame’s Background Modeling: Reviewing the Techniques”, Journal of Signal and Information, Volume 2, No. 2, pages 72-78, May 2011.

T. Bouwmans, “Traditional and Recent Approaches in Background Modeling for Foreground Detection: An Overview”, Computer Science Review, 2014. [pdf]


Statistical Background Modeling (3 papers)

T. Bouwmans, F. El Baf, B. Vachon, “Statistical Background Modeling for Foreground Detection: A Survey”, Handbook of Pattern Recognition and Computer Vision, World Scientific Publishing, Volume 4, Part 2, Chapter 3, pages 181-199, January 2010. [pdf]

T. Bouwmans, "Recent Advanced Statistical Background Modeling for Foreground Detection: A Systematic Survey", Recent Patents on Computer Science, Volume 4, No. 3 September 2011. [pdf]

T. Bouwmans, F. El Baf, B. Vachon, “Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey”, Recent Patents on Computer Science, Volume 1, No 3, pages 219-237, November 2008. [pdf]

Fuzzy Background Modeling (1 paper)

T. Bouwmans, “Background Subtraction For Visual Surveillance: A Fuzzy Approach”, Chapter 5 in Handbook on Soft Computing for Video Surveillance, Taylor and Francis Group, March 2012. [pdf]


Subspace Learning (4 papers)

T. Bouwmans, “Subspace Learning for Background Modeling: A Survey”, Recent Patents on Computer Science, Volume 2, No 3, pages 223-234, November 2009. [pdf]

T. Bouwmans, E. Zahzah, “Robust PCA via Principal Component Pursuit: A Review for a Comparative Evaluation in Video Surveillance”, Special Isssue on Background Models Challenge, Computer Vision and Image Understanding, CVIU 2014, Volume 122, pages 22–34, May 2014. [pdf]

T. Bouwmans, A. Sobral, S. Javed, S. Jung, E. Zahzah, "Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset", Computer Science Review, Volume 23, pages 1-71, February 2017. 

N. Vaswani, T. Bouwmans, S. Javed, P. Narayanamurthy, “Robust Subspace Learning: Robust PCA, Robust Subspace Tracking and Robust Subspace Recovery”, IEEE Signal Processing Magazine, Volume 35, No. 4, pages 32-55, July 2018. 

Deep Learning (5 papers)

T. Bouwmans, S. Javed, M. Sultana, S. Jung, “Deep Neural Network Concepts in Background Subtraction: A Systematic Review and A Comparative Evaluation”, Neural Networks, 2019. 

J. Giraldo, H. Le, T. Bouwmans, "Deep Learning based Background Subtraction: A Systematic Survey", 6th Handbook of "Pattern Recognition and Computer Vision", Edited by C.H Chen, World Scientific Publishing, March 2020. 

M. Mandal, S. Vipparthi, "An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs", IEEE Transactions on Intelligent Transportation Systems, 2021.

B. Hou, Y. Liu, N. Ling, Y. Ren, L. Liu, "A Survey of Efficient Deep Learning Models for Moving Object Segmentation", Preprint, October 2022.

R. Jiang, R. Zhu, H. Su, Y. Li,  Y. Xie, W. Zou, "Deep learning-based moving object segmentation: Recent progress and research prospects", Machine Intelligence Research, 2023.


Discrete Wavelet Transform (1 paper)

S. Biswas, J. Sil, N. Sengupta, “Background Modeling and Implementation using Discrete Wavelet Transform: a Review”, Journal  ICGST-GVIP, Volume 11, Issue 1, pages 29-42, March 2011.

Graph Signal Processing (2 papers)

J. Giraldo, S. Javed, M. Sultana, S. Jung, T. Bouwmans, "The Emerging Field of Graph Signal Processing for Moving Object Segmentation", International Workshop on Frontiers of Computer Vision, IW-FCV 2021, Daegu, South Korea, February 2021. 

J. Giraldo, T. Bouwmans, “Moving Objects Detection in Video Processing: A Graph Signal Processing Approach for Background Subtraction”, Chapter 15, Handbook on "Artificial Intelligence Technologies, Applications, and Challenges", Edited by L. Sharma and P. Garg, CRC Press, Taylor and Francis Group, November 2021.