Sample based Background Modeling

SAmple CONsensus (SACON)

H. Wang, D. Suter, “Background Subtraction Based on a Robust Consensus Method”, International Conference on Pattern Recognition, ICPR 2006, Hong Kong, China, 2006.

G. Han, X. Cai, J. Wang, "Object detection based on combination of visible and thermal videos using a joint sample consensus background model", Journal of Software, pages 987-994, 2013.

Y. Li, L. Liu, J. Song, Z. Zhang, X. Che, "Combination of local binary pattern operator with sample consensus model for moving objects detection", Infrared Physics and Technology, Volume 92, pages 44-52, 2018.

R. Singh, S. Sharma, “Using Attractive–Repulsive Binary Local Gradient Contours for Sample-Consensus Background Modeling”, Soft Computing for Problem Solving, pages 687-698, October 2021.

Adapting Multi-resolution Background ExtractoR (AMBER)

B. Wang, P. Dudek, “AMBER: Adapting Multi-Resolution Background Extractor”, IEEE International Conference on Image Processing, ICIP 2013, Melbourne, Australia, 2013.

B. Wang, P. Dudek "A Fast Self-tuning Background Subtraction Algorithm", IEEE Workshop on Change Detection in conjunction with CVPR 2014, 2014

Local Illumination based Background Subtraction (LIBS)

K. Hati, P. Sa, B. Maihi, "Intensity Range based Background Subtraction for Effective Object Detection", IEEE Signal Processing Letters, Volume 20, pages 759-762, 2013.

LOcal Binary Similarity segmenTER (LOBSTER)

G. Bilodeau, J. Jodoin, N. Saunier "Change detection in feature space using local binary similarity patterns', International Conference on Computer and Robot Vision CRV 2013, pages 106-112, 2013.

P. St-Charles, G. Bilodeau, "Improving background subtraction using Local Binary Similarity Patterns," IEEE Winter Conference on Applications of Computer Vision, WACV 2014, pages 509-515, 2014.

Self-Balanced SENsitivity SEgmenter (SuBSENSE)

P. St-Charles, G. Bilodeau, R. Bergevin, "Flexible Background Subtraction with Self-Balanced Local Sensitivity", IEEE Change Detection Workshop, CDW 2014, June 2014.

P. St-Charles, G. Bilodeau, R. Bergevin, “SuBSENSE: A Universal Change Detection Method with Local Adaptive Sensitivity”, IEEE Transactions on Image Processing, 2014.

T. Yu , J. Yang, W. Lu, "Combining Background Subtraction and Convolutional Neural Network for Anomaly Detection in Pumping-Unit Surveillance", MDPI Algorithms, 2019.

Pixel-based Adaptive Word Consensus Segmenter (PAWCS)

P. St-Charles, G. Bilodeau, R. Bergevin, “A Self-Adjusting Approach to Change Detection Based on Background Word Consensus", IEEE Winter Conference on Applications of Computer Vision, WACV 2015, 2015.

P. St-Charles, G. Bilodeau, R. Bergevin, "Universal Background Subtraction Using Word Consensus Models", IEEE Transactions on Image Processing, Volume 25, No. 10, pages 4768-4781, October 2016.

H. Luo, B. Li, Z. Zhou, "Improved background subtraction based on word consensus models", International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2017, pages 689-694, Xiamen, China, 2017.

Hybrid Sample-based Background Subtraction (HySamBS)

W. Zhang, L. Huang, "HySamBS: A Hybrid Sample-based Background Subtraction Method", Academic Journal of Computing and Information Science, Volume 3, Issue 2, pages 38-44, 2020.

Pixel to Model (P2M)

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.

Probabilistic Illumination Range Modeling (PIRM)

P. Siva, M Shafiee, F. Li, A. Wong, "PIRM: Fast Background Subtraction Under Sudden, Local Illumination Changes via Probabilistic Illumination Range Modeling" IEEE International Conference on Image Processing, ICIP 2015, pages 789-792, 2015,

In Unity There Is Strength (IUTIS)

S. Bianco, G. Ciocca, R. Schettini, "How far can you get by combining change detection algorithms?", Preprint 2015.

Independent Multimodal Background Subtraction (IMBS)

D. Bloisi, L. Iocchi, "Independent multimodal background subtraction", International Conference on Computational Modeling of Objects Presented in Images: Fundamentals, Methods and Applications, pages 39–44, 2012.

D. Bloisi, A. Pennisi, L. Iocchi, "Parallel Multi-modal Background Modeling", submitted to Pattern Recognition Letters, 2016.

Fast Adaptive Foreground Extraction (FAFEX)

A. Pennisi, F. Previtali, D. Bloisi, L. Iocchi, "Real-Time Adaptive Background Modeling in Fast Changing Conditions", AVSS 2015, 2015.

Weight Sample Background Extractor (WeSamBE)

S Jiang, X Lu, "WeSamBE: A Weight-Sample-Based Method for Background Subtraction", IEEE Transactions on Circuits and Systems for Video Systems, 2017.

ADM-HIPaR

T. Huynh-The, S. Lee, C. Hua, “ADM-HIPaR: An efficient background subtraction approach”, IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017, Lecce, Italy, 2017.

CDoTS

S. Wangsiripitak, W. Rattanapitak, “CDoTS: Change detection on time series background for video foreground segmentation”, International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2017, pages 400-403, Phuket, Thailand, 2017.

SuperBE

A. Chen, M. Biglari-Abhari, K. Wang, “SuperBE: computationally light background estimation with superpixels”, Journal of Real-Time Image Processing, pages 1-17, January 2018.

A. Chen, R. Gupta, A. Borzenko, K. Wang, M. Biglari-Abhari, "Accelerating SuperBE with Hardware/Software Co-Design", MPDI Journal of Imaging, October 2018.

SBS

Y. Chen Z. Sun, K. Lam, "An Effective Sub-Superpixel-Based Approach for Background Subtraction", IEEE Transactions on Industrial Electronics, 2019

M4CD

K. Wang, C. Gou, F. Wang, "M4CD: A Robust Change Detection Method for Intelligent Visual Surveillance", Preprint, 2018.

CANDID

M. Mandal, P. Saxena, S. Vipparthi, S. Murala, "CANDID: Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction", Preprint, 2018.

WisenetMD

S. Lee, S. Kwon, J. Shim, J. Lim, J. Yoo, “WisenetMD: Motion Detection using Dynamic Background Region Analysis”, Preprint, May 2018.

Co-occurrence Probability based Pixel Pairs (CP3)

D. Liang , S. Kaneko, M. Hashimoto, K. Iwata, X. Zhao, Y. Satoh, "Co-occurrence-based Adaptive Background Model for Robust Object Detection", IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2013, September 2013.

D. Liang, S. Kaneko, M. Hashimoto, K. Iwata, X. Zhao, Y. Satoh, "Robust Object Detection in Severe Imaging Conditions using Co-Occurrence Background Model", International Journal of Optomechatronics, pages 14-29, April 2014.

D. Liang, S. Kaneko, M. Hashimoto, K. Iwata, X. Zhao, "Co-occurrence Probability based Pixel Pairs Background Model for Robust Object Detection in Dynamic Scenes", Pattern Recognition, Volume 48, No. 4, pages 1374-1390, 2015.

D. Liang, S. Kaneko, "Improvements and Experiments of a Compact Statistical Background Model", Preprint, 2014.

D. Liang, S. Kaneko and H. Sun, B. Kang, "Adaptive local spatial modeling for online change detection under abrupt dynamic background", IEEE International Conference on Image Processing, ICIP 2017, pages 2020-2024, 2017.

Co-occurrence Pixel-Block Pairs (CPB)

W. Zhou, S. Kaneko, D. Liang, M. Hashimoto, Y. Satoh, "Background Subtraction based on Co-occurence Pixel-Block Pairs for Robust Object Detection in Dynamic Scenes", IIEEJ Transactions on Image Electronics and Visual Computing, Volume 5, No. 2, 2017.

CPB with Hypothesis on Degradation Modification (CPB-HoD)

W. Zhou, S.Kaneko, M. Hashimoto, Y. Satoh, D. Liang, "Foreground Detection based on Co-occurrence Background Model with Hypothesis on Degradation Modification in Background Changes", IEEE Europe-Asia Congress on Mechatronics, 2018.

W. Zhou, S.Kaneko, M. Hashimoto, Y. Satoh, D. Liang, "Co-occurrence Background Model with Hypothesis on Degradation Modification for Robust Object Detection", International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2018, Volume 5, pages 266-273, 2018.

W. Zhou, S. Kaneko, M. Hashimoto, Y. Satoh, D. Liang, "Foreground detection based on co-occurrence background model with hypothesis on degradation modification in dynamic scenes", Signal Processing, Volume 160, pages 66-79, July 2019.

W. Zhou, S. Kaneko, M. Hashimoto, Y. Satoh, D. Liang, "A Co-occurrence Background Model with Hypothesis on Degradation Modification for Object Detection in Strong Background Changes", IAPR International Conference on Pattern Recognition, ICPR 2018, 2018.

W. Zhou, S. Kaneko, Y. Satoh, M. Hashimoto, D. Liang ,"Co-occurrence based Foreground Detection with Hypothesis on Degradation Modification in Severe Imaging Conditions", Proceedings of JSPE, 2018.

W. Zhou, "Co-occurrence Pixel-Block Background Model and its Application to Robust Event Detection", PhD Thesis, Hokkaido University, June 2019.

CPB with Deep Learning

D. Liang, X. Liu, "Coarse-to-fine Foreground Segmentation based on Co-occurrence Pixel-Block and Spatio-Temporal Attention Model", International Conference on Pattern Recognition, ICPR 2021, pages 3807-3813, January 2021.

D. Liang, B. Kang, X. Liu, P. Gao, X. Tan, S. Kaneko, "Cross-scene Foreground Segmentation with Supervised and Unsupervised Model Communication", Pattern Recognition, April 2021.

Angle Co-occurrence Matrices (ACM)

R. Moudgolly, A. Midya, A. Sunaniya, J. Chakraborty, “Dynamic background modeling using intensity and orientation distribution of video sequence”, Multimedia Tools and Applications, pages 1-18, 2019.

Real-time Record Sensitive Background Classifier (RSBC)

S. Roy, A. Ghosh, "Real-time Record Sensitive Background Classifier (RSBC)", Expert Systems With Applications, October 2018.

Locally Statistical Dual-mode (LSD)

T. Huynh-The, C. Hua, N. Tu, D. Kim, “Locally Statistical Dual-Mode Background Subtraction Approach”, IEEE Access, Volume 7, pages 9769-9782, 2019.

Bivariance

G. Zhang, Z. Yuan, Q. Tong, Q. Wang, "A Novel and Practical Scheme for Resolving the Quality of Samples in Background Modeling", MDPI Sensors, March 2019.

Fast-D

A. Hossain, I. Hossain, D. Hossain, N. Thu, E. Huh, "Fast-D: When Non-Smoothing Color Feature Meets Moving Object Detection in Real-Time", IEEE Access 2020, 2020.

FBGS

M. Hossain, V. Nguyen, E. Huh, "The trade‐off between accuracy and the complexity of real‐time background subtraction", IET Image Processing, December 2020.

ODD

M. Hossain, M. Hossain, M. Hossain, N. Thu, S. Hong, E. Huh, "ODD: Background Subtraction Based Effective Moving Object Detection for Dynamic Video", Korea Computer Congress, KCC 2021, January 2021.

SDIES

L. Huang, Y. Feng, L. Cai, W. Zhang, B. Onasanya, “SDIES: A Background subtraction method with sample dynamic indicator and edge similarity”, Italian Journal of Pure and Applied Mathematics, pages 250-267, February 2021.

WCLPE

H. Pan, G. Zhu, C. Peng, Q. Xiao, “Background subtraction for night videos”, PeerJ Computer Science, 2021.