Mixture of Gaussians - Part 1

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

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

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.

List of Publications on Background Modeling using Mixture of Gaussians for Foreground Detection

N. Friedman, S. Russell, “Image Segmentation in Video Sequences: A Probabilistic Approach”, Proceedings Thirteenth Conference on Uncertainty in Artificial Intelligence, UAI 1997, pages 175-181, 1997.

C. Stauffer, E. Grimson, “Adaptive background mixture models for real-time tracking”, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 1999, pages 246-252, 1999.

S. Atev, O. Masoud, N. Papanikolopoulos, “Practical Mixtures of Gaussians with Brightness Monitoring”, IEEE Conference on Intelligent Transportation Systems, Proceedings, ITS 2004, pages 423-428, 2004.

Q. Zang, R. Klette, “Parameter Analysis for Mixture of Gaussians”, CITR Technical Report 188, Auckland University, 2006.

B. Han, X. Lin, “Update the GMMs via adaptive Kalman filtering”, Proceedings of SPIE - The International Society for Optical Engineering, Volume 5960, Issue 3, pages 1506-1515, 2005.

H. Yang, Y. Tan, J. Tian, J. Liu, “Accurate dynamic scene model for moving object detection”, International Conference on Image Processing , ICIP 2007, Volume VI, pages 157-160, 2007.

W. Zhang , X. Fang, X. Yang, Q. Wu, “Spatiotemporal Gaussian mixture model to detect moving objects in dynamic scenes”, Journal of Electronic Imaging, Volume 16, Issue 2, April 2007.

P. Tang, L. Gao, Z. Liu, “Salient Moving Object Detection Using Stochastic Approach Filtering”, Fourth International Conference on Image and Graphics, ICIG 2007, pages 530-535, 2007.

M. Harville, “A framework for high-level feedback to adaptive, per-pixel, mixture-of-Gaussian background models”, Proceedings of the 7th European Conference on Computer Vision, ECCV 2002, pages 543 -560, Copenhagen, Denmark, May 2002.

M. Cristani, V. Murino, “A spatial sampling mechanism for effective background subtraction”, VISAPP 2007, Volume 2, pages 403-410, Barcelona, Spain, March 2007.

Z. Zivkovic, “Improved adaptive Gaussian mixture model for background subtraction”, International Conference Pattern Recognition, Volume 2, pages 28-31, 2004.

Z. Zivkovic, F. Heijden, “Recursive unsupervised learning of finite mixture models”, IEEE Transaction on Pattern Analysis and Machine Intelligence, Volume 5, No. 26, pages 651-656, 2004.

J. Cheng, J. Yang, Y. Zhou, Y. Cui, “Flexible background mixture models for foreground segmentation”, Image and Vision Computing, Volume 24, pages 473-482, 2006.

A. Shimada, D. Arita, R. Taniguchi, “Dynamic Control of Adaptive Mixture-of-Gaussians Background Model”, AVSS 2006, page 5, Sydney, Australia, November 2006.

R. Tan, H. Huo, J. Qian, T. Fang , “Traffic Video Segmentation using Adaptive-K Gaussian Mixture Model”, The International Workshop on Intelligent Computing, IWICPAS 2006, LNCS 4153, pages125-134, Xi'An, China, August 2006.

L. Carminati, J. Benois-Pinau, “Gaussian Mixture Classification for Moving Object Detection in Video Surveillance Environment “, IEEE International Conference on Image Processing, ICIP 2005, 2005.

V. Morellas, I. Pavlidis, P. Tsiamyrtzis, “DETER: detection of events for threat evaluation and recognition”, Machine Vision and Applications, Volume 15, pages 29-45, June 2003.

D. Lee, “Online Adaptive Gaussian Mixture Learning for Video Applications”, ECCV Workshop on Statistical Methods for Video Processing, Prague, Czech, May 2004.

Y. Zhang, Z. Liang, Z. Hou, H. Wang, M. Tan, “An Adaptive Mixture Gaussian Background Model with Online Background Reconstruction and Adjustable Foreground Mergence Time for Motion Segmentation”, ICIT 2005, pages 23-27, December 2005.

M. Amintoosi, F. Farbiz, M. Fathy, M. Analoui, N. Mozayani, “QR decomposition-based algorithm for background subtraction”, ICASSP 2007, 2007.

A. Lepisk, “The use of Optic Flow within Background Subtraction”, Master Thesis, Royal Institute of Technology, Nada, Sweden, January 2005.

B. Han, X. Lin, “Update the GMMs via adaptive Kalman filtering”, Proceedings of SPIE - The International Society for Optical Engineering, Volume 5960, Issue 3, pages 1506-1515, 2005.

H. Wang, D. Suter, ”A Re-Evaluation of Mixture-of-Gaussian Background Modeling”, ICASSP 2005, Pennsylvania, USA, pages 1017-1020, March 2005.

J. Lindstrom, F. Lindgren, K. Ltrstrom, J. Holst, U. Holst, “Background and Foreground Modeling Using an Online EM Algorithm”, IEEE International Workshop on Visual Surveillance VS 2006 in conjunction with ECCV 2006, May 2006.

C. Stauffer, E. Grimson, “Learning Patterns of Activity Using Real-Time Tracking”, IEEE Transactions on Pattern Recognition and Machine Intelligence, PAMI 2000, Volume 22, pages 747-757, 2000.

J. Landabaso, M. Pardas, “Cooperative Background Modelling using Multiple Cameras Towards Human Detection in Smart-Rooms”, EUSIPCO 2006, Florence, Italy, September 2006.

D. Park, J. Kim, J. Kim, S. Cho, S. Chung, “Motion Detection in Complex and Dynamic Backgrounds”, PSIVT 2006, pages 545-552, Hsinchu, Taiwan, December 2006.

A. Mittal, D. Huttenlocher, “Scene Modeling for Wide Area Surveillance and Image Synthesis”, CVPR 2000, Volume 2, pages 160-167, Hilton Head, South Carolina, June 2000.

Q. Zang, R. Klette, “Evaluation of an Adaptive Composite Gaussian Model in Video Surveillance”, CITR Technical Report 114, Auckland University, August 2002.

B. White, M. Shah, “Automatically Tuning Background Subtraction Parameters Using Particle Swarm Optimization”, IEEE International ICME 2007, pages 1826-1829, Beijing, China, 2007.

P. KaewTraKulPong, R. Bowden, “An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection”, AVBS 2001, Kingston, UK, September 2001.

P. KaewTraKulPong , R. Bowden, “Adaptive Visual System for Tracking Low Resolution Color Targets”, BMVC 2001, Volume 1, pages 243-252, Manchester UK, September 2001.

P. KaewTraKulPong, R. Bowden, “A Real-Time Adaptive Visual Surveillance System for Tracking Low Resolution Color Targets In Dynamically Changing Scenes”, Journal of Image and Vision Computing, Volume 21, Issue 10, pages 913-929, September 2003.

D. Lee, “ Improved Adaptive Mixture Learning for Robust Video Background Modeling”, IAPR Workshop on Machine Vision for Applications, Nara, Japan, pages 443-446, December 2002.

M. Harville, G. Gordon, J. Woodfill, "Foreground segmentation using adaptive mixture models in color and depth", Proceedings of the IEEE Workshop on Detection and Recognition of Events in Video, Vancouver, Canada, July 2001.

F. Porikli, “Human Body Tracking by Adaptive Background Models and Mean-Shift Analysis”, IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS 2003), March 2003.

Y. Liu, H. Ai, G. Xu, “Moving object detection and tracking based on background subtraction”, Proceedings of SPIE ,Volume 4554, pages 62-66, 2001.

A. Pnevmatikakis, L. Polymenakos, “2D Person Tracking Using Kalman Filtering and Adaptive Background Learning in a Feedback Loop”, Proceedings of the CLEAR Workshop 2006, LNCS 4122, pages 151-160, 2006.

A. Stergiou, A. Pnevmatikakis, L. Polymenakos, “The AIT Outdoor tracker for Vehicle and Pedestrians in CLEAR 2007”, Proceedings of the CLEAR, Workshop 2007, pages 148-159, 2007.

P. Power, J. Schoonees, “Understanding Background Mixture Models for Foreground Segmentation”, Imaging and Vision Computing New Zealand, Auckland, NZ, November 2002.

M. Leotta, J. Mundy, “Learning Background and Shadow Appearance with 3-D Vehicle Models”, BMVC 2006, Edimburgh, September 2006.

Y. Ren, C. Chua, Y. Ho, “Motion Detection with Non-stationary Background”, ICIAP 2002, pages 78-83, 2002.

D. Lee, “Improved Adaptive Mixture Learning for Robust Video Background Modeling”, IAPR Workshop on Machine Vision for Applications, Nara, Japan, pages 443-446, December 2002.

Y. Sun, B. Li, B. Yuan, Z. Miao, C. Wan, “Better Foreground Segmentation for Static Cameras via New Energy Form and Dynamic Graph-cut”, ICPR 2006, 2006.

J. Landabaso, M. Pardas, L. Xu, “Hierarchical Representation of Scenes using Activity Information”, ICASSP 2005, pages 677-680, Philadelphia, USA, March 2005.

S. Yang, C. Hsu, “Background Modeling from GMM Likelihood Combined with Spatial and Color Coherency”, ICIP 2006, 2006.

D. Lee, “Effective Gaussian Mixture Learning for Video Background Subtraction”, PAMI 2005,Volume 27, pages 827-832 2005.

P. Withagen, F. Groen, K. Schutte, “EMswitch: a multi-hypothesis approach to EM background modeling”, Proceedings of the IEEE Advanced Concepts for Intelligent Vision Systems, ACIVS 2003, September 2003.

M. Haque, M. Murshed, M. Paul, “A Hybrid Object Detection Technique from Dynamic Background Using Gaussian Mixture Models”, IEEE International Workshop on Multimedia Signal Processing, MMSP 2008, pages 915-920, Cairns, Queensland, Australia, October 2008.

M. Haque, M. Murshed, M. Paul, “Improved Gaussian Mixtures for Robust Object Detection by Adaptive Multi-Background Generation”, International Conference on Pattern Recognition, ICPR 2008, Tampa, Florida, USA, December 2008.

M. Haque, M. Murshed, M. Paul, “On Stable Dynamic Background Generation Technique using Gaussian Mixture Models for Robust Object Detection”, IEEE International Conference On Advanced Video and Signal Based Surveillance, AVSS 2008, pages 41-48, Santa Fe, USA, September 2008.

X. Fang, W. Xiong, B. Hu, L. Wang, “A Moving Object Detection Algorithm Based on Color Information”, IST 2006, Journal of Physics, Volume 48, pages 384–387, 2006.

D. Pokrajac, L. Latecki, “Spatiotemporal Blocks-Based Moving Objects Identification and Tracking”, IEEE Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS 2003, pages 70-77, October 2003.

H. Bhaskar, L. Mihaylova, S. Maskell, “Automatic Target Detection Based on 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.

G. Stijnman , R. van den Boomgaard, “Background estimation in video sequences”, Technical Report 10, Intelligent Sensory Information Systems Group, University of Amsterdam, January 2000.

M. Xu, T. Ellis, “Illumination-invariant motion detection using color mixture models” British Machine Vision Conference, BMVA, Manchester, pages 163-172, September 2001.

W. Wang, R. Wu, “Fusion of luma and chroma GMMs for HMM-based object detection “, First Pacific Rim Symposium on Advances in Image and Video Technology, pages 573-81, Hsinchu, Taiwan, December 2006.

S. Yang, C. Hsu, “Background Modeling from GMM Likelihood Combined with Spatial and Color Coherency”, ICIP 2006, pages 2801-2804, Atlanta, USA, 2006.

N. Setiawan, S. Hong, J. Kim, C. Lee, ”Gaussian Mixture Model in Improved HLS Color Space for Human Silhouette Extraction”, 16th International Conference on Artificial Reality and Telexistence , ICAT 2006, pages 732-741, Hangzhou, China, 2006.

F. Kristensen, P. Nilsson, V. Öwall, “Background Segmentation Beyond RGB”, ACCV 2006, pages 602-612, Hyderabad, Indian, 2006.

H. Ribeiro, A. Gonzaga, “Hand Image Segmentation in Video Sequence by GMM: a comparative analysis”, 19th Brazilian Symposium on Computer Graphics and Image Processing, SIBGRAPI 2006, pages 357-364, Brazil, October 2006.

O. Javed , K. Shafique, M. Shah, “A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information”, IEEE Workshop on Motion and Video Computing, WMVC 2002, page 22, Orlando, December 2002.

V. Jain, B. Kimia, J. Mundy, ”Background modelling based on subpixel edges”, ICIP 2007, Volume VI, pages 321-324, San Antonio, USA, September 2007.

Y. Tian, A. Hampapur, “Robust Salient Motion Detection with Complex Background for Real-time Video Surveillance”, IEEE Computer Society Workshop on Motion and Video Computing, CVPR 2005, Volume 2, pages 30-35, Breckenridge, Colorado, January 2005.

Y. Tian, M. Lu, A. Hampapur, “Robust and efficient foreground analysis for real-time video surveillance”, IEEE International Conference on Computer Vision and Pattern Recognition, CVPR 2005, pages 1182-1187, June 2005.

G. Gordon, T. Darrell, M. Harville, J. Woodfill, “Background estimation and removal based on range and color”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 459-464, June 1999.

D. Silvestre, “Video surveillance using a time-of-flight camera”, PhD thesis, Informatics and Mathematical Modelling, Technical University of Denmark, 2007.

P. Dickinson, A. Hunter, “Scene Modeling Using An Adaptive Mixture of Gaussians in Color and Space”, IEEE Conference on Advanced Video and Signal based Surveillance, Como, Italy, September 2005.

W. Wang, W. Gao, J. Yang, D. Chen, “Modeling Background from Compressed Video”, ICCV 2005, pages 161-168, Beijing, China, October 2005.

P. Kumar, K. Sengupta, “Foreground background segmentation using temporal and spatial markov processes”, Department of Electrical and Computer Engineering, National University of Singapore, November 2000.

D. Zhou, H. Zhang, “Accurate Segmentation of Moving Objects in Image Sequence Based on Spatio-Temporal Information”, ICMA 2006, pages 543-548, Luoyang, China, June 2006.

K. Schindler, H. Wang, “Smooth Foreground-Background Segmentation for Video Processing”, ACCV 2006, Hyderabad, India, Volume 3852, pages 581-590, January 2006.

Y. Sun, B. Yuan, “Hierarchical GMM to handle sharp changes in moving object detection”, Electronic Letters, Volume 40, No 13, pages 801-802, June 2004.

J. Park, A. Tabb, A. Kak, “Hierarchical Data Structure for Real Time Background Subtraction”, ICIP 2006, pages 1849-1852, Atlanta, USA, October 2006.

Y. Chen, C. Chen, C. Huang, Y. Hung,”Efficient hierarchical method for background subtraction”, Pattern Recognition, Volume 40 , Issue 10, pages 2706-2715, October 2007.

Y. Zhou, Y. Gong, H. Tao, “Background modeling using time dependent Markov random field with image pyramid”, Proceedings IEEE Motion 2005, January 2005.

Q. Zang, R. Klette, “Robust Background Subtraction and Maintenance”, 17th International Conference on Pattern Recognition, ICPR 2004, Volume 2, pages 90-93, 2004.

Q. Zhong, L. Dai, Y. Song, R. Wang, “A hierarchical motion detection algorithm with the fusion of the two types of motion information”, Pattern Recognition and Artificial Intelligence, Volume 18, Issue 5, pages 552-557, October 2005.

M. Cristani, M. Bicego, V. Murino, “Integrated Region- and Pixel-based Approach to Background Modeling”, Workshop on Motion and Video Computing, MOTION 2002, pages 3-8, 2002.

M. Cristani, V. Murino, “Background subtraction with adaptive spatio-temporal neighborhood analysis”, 3rd International Conference on Computer Vision Theory and Applications, VISAPP 2008, Funchal, Portugal, January 2008.

T. Su, J. Hu, “Background Removal in Vision Servo System using Gaussian Mixture Model Framework”, ICNSC 2004, Volume 1, pages 70-75, March 2004.

J. Hu, T. Su, “Robust Background Subtraction with Shadow and Highlight Removal for Indoor Surveillance”, Journal on Advances in Signal Processing, Volume 2007, pages 1-14, 2007.

F. Porikli, “Detection of Temporarily Static regions by Processing Video at Different Frame Rates”, IEEE Conference on Advanced Video and Signal Based Surveillance, AVSS 2007, pages 236-241, 2007.

H. Yang, Y. Tan, J. Tian, J. Liu, “Accurate dynamic scene model for moving object detection”, ICIP 2007, Volume 6, pages 157-160, 2007.

F. Porikli, O. Tuzel, “Bayesian Background Modeling for Foreground Detection”, ACM International Workshop on Video Surveillance and Sensor Networks, VSSN 2005, pages 55-28, November 2005.

M. Xu, T. Ellis, “Color-invariant motion detection under fast illumination changes”, 2nd IAPR European Workshop on Advanced Video-based Surveillance Systems, Kingston, pages 335-345, September 2001.

S. Nadimi, B. Bhanu, “Multistrategy fusion using mixture model for moving object detection”, Proceedings International Conference on Multisensor Fusion and Integration for Intelligent Systems, pages 317-322, Baden-Baden, Germany, August 2001.

S. Nadimi, B. Bhanu, “Physics-based cooperative sensor fusion for moving object detection”, Proceedings IEEE Workshop on Learning in Computer Vision and Pattern Recognition, Washington, DC, June 2004.

C. Conaire, N. O'Connor, E. Cooke, A. Smeaton, “Multispectral Object Segmentation and Retrieval in Surveillance Video”, ICIP 2006, pages 2381-2384, 2006.

M. Harville, “A framework for high-level feedback to adaptive, per-pixel, mixture-of-Gaussian background models”, Proceedings of the 7th European Conference on Computer Vision, Copenhagen, Denmark, May 2002.

L. Taycher, J. Fisher, T. Darrell, “Incorporating Object Tracking Feedback into Background Maintenance Framework”, Proceedings of the IEEE Workshop on Motion and Video Computing, Volume 2, pages 120-125, 2005.

D. Turdu, H. Erdogan, “Improved post-processing for GMM based adaptive background modelling”, 22nd International International Symposium on Computer and Information Sciences, ISCIS 2007, pages 1-6, November 2007.

D. Parks, S. Fels, “Evaluation of Background Subtraction Algorithms with Post-processing”, IEEE International Conference on Advanced Video and Signal-based Surveillance, AVSS 2008, September 2008.

P. Atrey, V. Kumar, A. Kumar, M. Kankanhalli, “Experiential sampling based foreground/background segmentation for video surveillance”, ICME 2006, pages 1809-1812, Toronto, Canada, July 2006.

D. Magee, “Tracking Multiple Vehicles using Foreground, Background and Motion Models”, Image and Vision Computing, volume 22, pages 143-155, 2004.

J. Zuo, Q. Pan, Y. Liang, H. Zhang, Y. Cheng, ”Model Switching Based Adaptive Background Modeling Approach”, Acta Automatica Sinica, Volume 33, Issue 5, pages 467-473, 2007.

Z. Tang, Z. Miao, “Fast Background Subtraction Using Improved GMM and Graph Cut”, First International Congress on Image and Signal Processing, CISP 2008, Sanya, Hainan, China, May 2008.

H. Jiang, H. Ardo, V. Owall, “Hardware accelerator design for video segmentation with multi-modal background modeling”, International Symposium on Circuits and Systems, ISCAS 2005, Volume 2, pages 1142- 1145, May 2005.

K. Appiah, A. Hunter, “A Single-Chip FPGA Implementation of Real-time Adaptive Background Model”, IEEE 2005 Conference on Field-Programmable Technology, FPT 2005, National University of Singapore, Singapore, December 2005.

Al-Mazeed, M. Nixon, S. Gunn, “Classifiers Combination for Improved Motion Segmentation”, Proceedings of International Conference on Image Analysis and Recognition, ICIAR 2004, pages 363-371, Porto, Portugal, 2004.

X. Fang, W. Xiong, B. Hu, L. Wang, “A Moving Object Detection Algorithm Based on Color Information”, Journal of Physics, Volume 48, pages 384–387, 2006.

J. Pan, Q. Liao, X. Lin, "Automatic extraction of moving object in video sequences", Tsinghua Science and Technology, pages 190-193, 2001.

J. Zhang, C. Chen, “Moving Objects Detection and Segmentation in Dynamic Video Backgrounds”, Conference on Technologies for Homeland Security, pages 64-69, Woburn, USA, May 2007.

J. Salas, P. Martínez, J. Gonzàlez, “Background Updating with the use of Intrinsic Curves”, International Conference on Image Analysis and Recognition, ICIAR 2006, Póvoa de Varzim, Portugal, September 2006.

Y. Liang, Z. Wang, X. Xu, X. Cao, “Background Pixel Classification for Motion Segmentation using Mean Shift Algorithm“, International Conference on Machine Learning and Cybernetics, ICMLC 2007, pages 1693-1698, Hong Kong, China, 2007.

D. Zhou, H. Zhang, “Modified GMM background modeling and optical flow for detection of moving objects”, IEEE International Conference on Systems, Man and Cybernetics, pages 2224-2229, Hawaii, USA, October 2005.

M. Greiffenhagen, V. Ramesh, H. Niemann, “The systematic design and analysis cycle of a vision system: A case study in video surveillance”, International Conference on Computer Vision and Pattern Recognition, CVPR 2001, 2001.

F. Campbell-West, P Miller, H. Wang, “Independent moving object detection using a color background model”, AVSS 2006, Sydney, Australia, November 2006.

X. Gao, T. Boult, F. Coetzee, V. Ramesh, “Error analysis of background adaption”, International Conference on Computer Vision and Pattern Recognition, CVPR 2000, Volume 1, pages 503-510, June 2000.

L. Xu, “Robust detection and tracking of multiple objects in cluttered scenes”, British Machine Vision Association Meetings, BMVA 2004, March 2004.

L. Teixeira, J. Cardoso, L. Corte-Real, “Object segmentation using background modelling and cascaded change detection”, Journal of Multimedia, Volume 2, Issue 5, pages 55-65, 2007.

F. Achkar, A. Amer, “Hysteresis-based selective Gaussian mixture models for real-time background maintenance” SPIE Symposium on Electronic Imaging, Conference on Visual Communications and Image, San Jose, CA, USA, January 2007.

N. Rao, H. Di, G. Xu, “Joint correspondence and background modeling based on tree dynamic programming”, International Conference on Pattern Recognition, ICPR 2006, 2006, 425-428.

H. Zen, S. Lai, “Adaptive foreground object extraction for real-time video surveillance with lighting variations”, ICASSP 2007, Volume 1, pages 1201-1204, 2007.

A. Utasi, L. Czúni, “Reducing the Foreground Aperture Problem in Mixture of Gaussians Based Motion Detection”, 6th EURASIP Conference Focused on Speech and Image Processing, Multimedia Communications and Services EC-SIPMCS 2007, Maribor, Slovenia, 2007.

G. Dalley, J. Migdal, W. Grimson “Background Subtraction for Temporally Irregular Dynamic Textures”, WACV 2008, Colorado, USA, January 2008.

C. Cuevas, L. Salgado, N. Garcia, “A new strategy based on adaptive mixture of Gaussians for real-time moving objects segmentation”, Real Time image Processing, SPIE 2008, Volume 6811, January 2008.

S. Cheng, X. Luo, S. Bhandarkar, “A Multiscale Parametric Background Model for Stationary Foreground Object Detection”, Workshop on Motion and Video Computing, WMVC 2007, Austin, USA, February 2007