SL-PCA

Note:  This list of publications comes from my research. Please cite my following paper:

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

List of Publications on Background Modeling using SL-PCA for Foreground Detection

N. Oliver, B. Rosario, A. Pentland, “A Bayesian Computer Vision System for Modeling Human Interactions”, International Conference on Vision Systems, ICVS 1999, Gran Canaria, Spain,  January 1999.

N. Oliver, B. Rosario, A. Pentland, “A Bayesian Computer Vision System for Modeling Human Interactions”, PAMI Special Issue on Visual Surveillance and Monitoring, PAMI 2000, August 2000.

J. Rymel, J. Renno, D. Greenhill, J. Orwell, G. Jones, "Adaptive Eigen-Backgrounds for Object Detection", IEEE International Conference on Image Processing, ICIP 2004, Suntec City, Singapore, October 2004.

Y. Li, L. Xu, J. Morphett, R. Jacobs, “An Integrated Algorithm of Incremental and Robust PCA”, IEEE International Conference on Image Processing, ICIP 2003, Barcelona, Spain, September 2003.

Y. Li, L. Xu, J. Morphett, R. Jacobs, “On incremental and robust subspace learning”, Proceedings International Workshop on Statistical and Computational Theories of Vision, SCTV2003, Nice, France, October 2003.

Y. Li, “On incremental and robust subspace learning”, Pattern Recognition, PR 2004, Volume 37, Issue 7, pages 1509-1518, 2004.

D. Skocaj , A. Leonardis, “Weighted and Robust Incremental Method for Subspace Learning “,   IEEE International Conference on Computer Vision, ICCV 2003, Volume 2,  page 1494, 2003.

D. Skocaj, A. Leonardis, “Incremental and robust learning of subspace representations”, Image and Vision Computing, IVC 2006, pages 1-12, 2006.

J. Zhang, Y. Zhuang , “Adaptive Weight Selection for Incremental Eigen-Background Modeling”, IEEE International Conference on Multimedia and Expo, ICME 2007, pages 851-854, Beijing, China, July 2007.

L. Wang, L. Wang, Q.  Zhuo, H. Xiao, W. Wang, “Adaptive Eigenbackground for Dynamic Background Modeling”, Intelligent Computing in Signal Processing and Pattern Recognition, Lecture Notes in Control and Information Sciences, Volume 345, pages 670-675, 2006.

L. Wang, L. Wang, M. Wen, Q. Zhuo, W. Wang, “Background subtraction using incremental subspace learning”, IEEE International Conference on Image Processing, ICIP 2007, Volume 5, pages 45-48, September 2007.

R. Li, Y. Chen, X. Zhang, “Fast Robust Eigen-Background Updating For Foreground Detection”, IEEE International Conference on Image Processing, ICIP 2006, pages 1833-1836, Atlanta, USA, October 2006.

Z. Xu,  P. Shi, I. Gu, “An Eigenbackground Subtraction Method using Recursive Error Compensation”, Advances in Multimedia Information Processing, PCM 2006,  2006.

Z. Xu, I. Gu, P. Shi, “Recursive error-compensated dynamic eigenbackground learning and adaptive background subtraction in video”, Optical Engineering, Volume 47, Issue 5, May 2008.

S. Kawabata, S. Hiura, K. Sato, “Real-Time Detection of Anomalous Objects in Dynamic Scene”, International Conference on Pattern Recognition, ICPR 2006, Volume 3, pages 1171 – 1174, Hong-Kong, August 2006.

B. Han, R. Jain, “Real-Time Subspace-Based Background Modeling Using Multi-channel Data”, International Symposium on Visual Computing, ISVC 2007, pages 162-172, November 2007.

Y. Zhao, H. Gong, L. Lin, Y. Jia, ”Spatio-temporal Patches for Night Background Modeling by Subspace Learning”, ICPR 2008, 2008.

Y. Zhao, H. Gong, Y. Jia, S. Zhu, “Background modeling by Subspace Learning on Spatio-Temporal Patches”, Pattern Recognition Letters, Volume 33, Issue 9, pages 1134-1147, July 2012.

X. Wu, Y. Wang, J. Li, “Video Background Segmentation Using Adaptive Background Models”, International Conference on Image Analysis and Processing, ICIAP 2009, pages 623–632, September 2009.

X. Wu, L. Yang, C. Yang, “Real-time Foreground Segmentation Based on a Fused Background Model”, International Conference on Computer and Automation Engineering, ICCAE 2010, pages 585-588, February 2010.

J. Zhang, Y. Tian, Y. Yang, C. Zhu, “Robust Foreground Segmentation Using Subspace Based Background Model”, Asia-Pacific Conference on Information Processing, APCIP 2009, Volume 2, pages 214-217, July 2009.

X. La, G. Zhao, H. Meng, “A New Method for Selecting Gradient Weight in Incremental Eigen-Background Modeling”, International Conference on Information and Automation, ICIA 2009, pages 801-805, Zhuhai, China, June 2009.

Y. Dong, T. X. Han, G.N. DeSouza, “Illumination invariant foreground detection using multi-subspace learning”, Journal International of Knowledge-Based and Intelligent Engineering Systems, Volume 14, Number 1, pages 31-41, 2010.

Y. Dong, G. N. DeSouza, “Adaptive Learning of Multi-Subspace for Foreground Detection under Illumination Changes”, Journal of Computer Vision and Image Understanding, CVIU 2011, pages 31-49, 2011.

Y. Kawanishi, I. Mitsugami, M. Mukunoki, M. Minoh, “Background Image Generation Keeping Lighting Condition of Outdoor Scenes”, International Conference on Security Camera Network, Privacy Protection and Community Safety, SPC2009, October 2009.

Y. Kawanishi, I. Mitsugami, M. Mukunoki, M. Minoh, “Background image generation by preserving lighting condition of outdoor scenes”, Procedia - Social and Behavioral Science, Volume 2, No.1, pages 129-136, March 2010.

L. Wu, Y. Wang, Y. Liu, “Multiple targets tracking with Robust PCA-based background subtraction and Mean-shift driven particle filter”, International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, pages 244-247, Changchun, China, August 2010.

C. Quivy, I. Kumazawa, “Background Images Generation Based on the Nelder-Mead Simplex Algorithm Using the Eigenbackground Model”, International Conference on Image Analysis and Recognition, ICIAR 2011, pages 21-29, June 2011.

Z. Hu, Y. Wang, Y. Tian, T. Huang, “Selective Eigenbackgrounds Method for Background Subtraction in Crowed Scenes”, International Conference in Image Processing, ICIP 2011, September 2011.

T. Cooke, “Eigenpatch based background subtraction”, Digital Image Computing: Techniques and Application, DICTA 2011, Queensland, Australia, December 2011.

J. Kim, B. Kang, S. Ahn, H. Kim, S. Kim, “A Real-time Object Detection System Using Selected Principal Components”, Multimedia and Ubiquitous Engineering, Lecture Notes in Electrical Engineering Volume 240, pages 367-376, 2013.

X. Cao, B. Pan, S. Zheng, C. Zhang, “Motion object detection method based on piecemeal principal component analysis of dynamic background updating”, ICLMC 2008, Volume 5, pages 2932-2937, July 2008.

C. Zhang, B. Pan, S. Zheng, X. Cao, “Motion object detection of video based on principal component analysis”, ICLMC 2008, Volume 5, pages 2938-2943, July 2008.

K. Hughes, V. Grzeda, M. Greenspan, “Eigenbackground Bootstrapping”, International Conference on Computer and Robot Vision, CRV 2013, pages 196-201, May 2013.

K. Hugues, “Subspace Bootstrapping and Learning for Background Subtraction”, PhD thesis, Queen's University, Canada, 2013.

P. Ziubinski, P. Garbat, J. Zawistowski, “Local Eigen Background Substraction”, Image Processing and Communications Challenges, Advances in Intelligent Systems and Computing Volume 233, pages199-204, 2014.

N. Shah, A. Pingale, N. George, “Object detection using Eigen-background subtraction with adaptive threshold”, IEEE International Conference on Communication and Signal Processing, Tamil Nadu, India, April 2014.

T. Takashi, Z. Thi, H. Hiromitsu, “A new incremental principal component analysis with a forgetting factor for background estimation”, SPIE 9249, Electro-Optical and Infrared Systems: Technology and Applications, October 2014.

J. Seo, S. Kim, “Recursive On-line (2D)²PCA and Its Application to Long-term Background Subtraction”, IEEE Transactions on Multimedia, 2014

J. Seo, S. Kim, “Dynamic Background Subtraction via Sparse Representation of Dynamic Textures in a Low-dimensional Subspace”, Signal, Image and Video Processing, September 2014.1,

G. Xue, L. Song, J. Sun, J. Zhou, "Foreground detection: Combining background subspace learning with object smoothing model", IEEE International Conference on Multimedia and Expo, ICME 2013, July 2013.

B. Chen, S. Huang, “An Advanced Moving Object Detection Algorithm for Automatic Traffic Monitoring in Real-World Limited Bandwidth Networks”, IEEE Transactions on Multimedia, Volume 16, No.3, pages 837-847, April 2014.

Z. Zhou, Z. Jin, “Two-dimension principal component analysis-based motion detection framework with subspace update of background”, IET Computer Vision, 2016.

Y. Sanchez-Silva, B. Hernandez-Sanabria, H. Perez-Meana, G. Sanchez-Perez, K. Toscano-Medina, J. Olivres-Mercado, M. Nakano Miyatake, L. Castro-Madrid, V. Sanchez-Silva, "Change Detection for Video Sequences based on Incremental Subspace Learning", New Trends in Intelligent Software Methodologies, 2018.

L. Rosas-Arias, J. Portillo-Portillo, A. Hernandez-Suarez, J. Olivares-Mercado, G. Sanchez-Perez, K. Toscano-Medina, H. Perez-Meana, A. Orozco, L. García Villalba, "Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach", MDPI Sensors, 2019.

A. Djerida, Z. Zhao, J. Zhao, "Background subtraction in dynamic scenes using the dynamic principal component analysis", IET Image Processing, Volume 14, No. 2, pages 245-255, 2020.

H. Huang, X. Liu, T. Zhang,  B. Yang, "Regression PCA for Moving Objects Separation", IEEE Global Communications Conference, GLOBECOM 2020 , pages 1-6, Taipei, Taiwan, 2020.

M. Amintoosi, F. Farbiz, “Eigenbackground Revisited: Can We Model the Background with Eigenvectors?”, Preprint, April 2021.

N. Shi, R. Kontar, "Personalized PCA: Decoupling shared and unique features",  Journal of Machine Learning Research, Volume 25, No. 4, pages 1-82, February 2024.