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
B. Klare, S. Sarka, “Background Subtraction in Varying Illuminations Using an Ensemble Based on an Enlarged Feature Set”, OTCBVS 2009, Miami, Florida, June 2009.
Z. Wei, S. Jiang, Q. Huang, “A Pixel-Wise Local Information-Based Background Subtraction Approach”, International Conference on Multimedia and Expo, ICME 2008, pages 1501-1504, April 2008.
J. Huang, C. Chen, “Learning Moving Cast Shadows for Foreground Detection”, International Workshop on Visual Surveillance, VS 2008, 2008.
T. Zhang, S. Li, S. Xiang, L. Zhang, S. Liu, “Co-Training Based Segmentation of Merged Moving Objects”, International Workshop on Visual Surveillance, VS 2008, 2008.
Y. Tian, R. Feris, A. Hampapur, “Real-Time Detection of Abandoned and Removed Objects in Complex Environments”, International Workshop on Visual Surveillance, VS 2008, 2008.
M. Izadi, P. Saeedi, “Robust Region-Based Background Subtraction and Shadow Removing Using Colour and Gradient Information”, International Conference on Pattern Recognition, ICPR 2008, pages 1-5, Tampa, USA, 2008.
J. Rahman, “Motion Detection for Video Surveillance”, Master Thesis, Department of Computer Science, Högskolan Dalarna, November 2008
A. Shimada, T. Tanaka, D. Arita, R. Taniguchi,, “Spatial-Temporal Integration of Adaptive Gaussian Mixture Background Models”, Korea-Japan Joint Workshop on Frontiers of Computer Vision, FCV 2008, 2008.
L. Niu, N. Jiang, “A Moving Objects Detection Algorithm Based on Improved Background Subtraction”, International Conference on Intelligent Systems Design and Applications, ISDA 2008, Volume 03, pages 604-607, 2008.
W. Wang, J. Yang, W. Gao, “Modeling Background and Segmenting Moving Objects from Compressed Video”, IEEE Transactions on Circuits and Systems for Video Technology, Volume18, Issue 5, pages 670-681, May 2008.
H. Shahid, K. Khan, W. Qazi , “Using modified mixture of Gaussians for background modeling in video surveillance”, International Conference on Advances in Space Technologies, ICAST 2008, Islamabad, Pakistan, November 2008.
S. Ju, X. Chen, G. Xu, “An Improved Mixture Gaussian Models to Detect Moving Object under Real-time Complex Background”, International Conference on Cyberworlds, ICC 2008, pages 730-734, 2008.
B. Zhong, H. Yao, S. Shan, X. Chen, W. Gao, “Hierarchical Background Subtraction using Local Pixel Clustering”, IEEE International Conference on Pattern Recognition, ICPR 2008, Florida, USA, December 2008.
X. Cai, L. Jiang, X. Hao, X. Meng, “A new region Gaussian background model for video surveillance”, International Conference on Natural Computation, pages 123-127, 2008.
B. Yuan, Z. Sun, “Guide background model update with object tracking”, Journal of Computational Information Systems, Volume 4, Issue 4, pages 1635-1642, August 2008.
A. Singh, P. Jaikumar, S. Mitra, M. Joshi, A. Banerjee, “Detection and Tracking of Objects in Low Contrast Conditions”, National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, NCVPRIPG 2008, pages 98-103, India, January 2008.
A. Singh, P. Jaikumar, S. Mitra, M. Joshi, A. Banerjee, “Background Subtraction in Videos using Adaptive Mixture Models with Split-and-Merge Operation”, Journal of the National Academy of Sciences, India.
P. Jaikumar, A. Singh, S. Mitra, “Background Subtraction in Videos using Bayesian Learning with Motion Information”, proceedings of British Machine Vision Conference , BMVC 2008, pages 615-624, Leeds, UK, September 2008.
A. Singh, S. Mitra, “Background Subtraction in Videos using Bayesian Learning of Gaussian Mixture Models”, IEEE Transactions on Image Processing, 2009.
A. Shimada, R. Taniguchi, “Object Detection Based on Gaussian Mixture Predictive Background Model under Varying Illumination”, International Workshop on Computer Vision, MIRU 2008, July 2008.
A. Shimada, R. Taniguchi, “Object Detection Based on Fast and Low-Memory Hybrid Background Model”, IEEJ Transactions on Electronics, Information and Systems, Volume 129-C, Number 5, pages 846-852, May 2009.
C. Lien, C. Hua, Y. Jiang, L. Jang, “Large Area Video Surveillance System with Handoff Scheme among Multiple Cameras”, IAPR Conference on Machine Vision Applications, MVA 2009, Yokohama, Japan, May 2009.
F. Wang, S. Dai, “Adaptive Background Update Based on Mixture Models of Gaussian”, International Conference on Information and Automation, ICIA 2009, Zhuhai, China, June 2009.
Y. Li. C. Xiong, Y. Yin, L. Liu, “Moving Object Detection Based on Edged Mixture Gaussian Models”, International Workshop on Intelligent Systems and Applications, ISA 2009, pages 1-5, May 2009.
T. Huang J. Qiu, T. Sakayori, S. Goto, T. Ikenaga, “Motion Detection Based on Background Modeling and Performance Analysis for Outdoor Surveillance”, International Conference on Computer Modeling and Simulation, ICCMS 2009, pages 38-42, Macau, February 2009.
T. Huang, J. Qiu, T. Sakayori, T. Ikenaga, “Robust Background Segmentation using Background Models for Surveillance Application”, International Conference on Machine Vision Applications, MVA 2009, pages 402-405, 2009.
X. Cai, F. Ali, E. Stipidis, “Background Modeling for Detecting Move-then-stop Arbitrary-long Time Video Objects”, WIAMIS 2009, London, UK, May 2009.
L. Chang, W. Hsu, “Foreground segmentation for static video via multi-core and multi-modal graph cut”, International Conference on Multimedia and Exposition, ICME 2009, New York, USA, July 2009.
M. Al Najjar, S. Ghosh, M. Bayoumi, “A Hybrid Adaptive Scheme Based on Selective Gaussian Modeling for Real-Time Object Detection”, IEEE International Symposium on Circuits and Systems, ISCAS 2009, pages 936-939, Taipei, Taiwan, May 2009.
H. Hu, Z. Li, Z. Qu, D. Wang, “Vision-Based Moving Objects Detection with Background Modeling”, International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2009, Volume 2, pages 436-439, 2009.
Z. Chen, N. Pears, M. Freeman, J. Austin, “Background subtraction in video using recursive mixture models, spatio-temporal filtering and shadow removal”, International Symposium on Visual Computing, ISVC 2009, Las Vegas, December 2009.
S. Xuehua, C. Yu, G. Jianfeng, C. Jingzhu, “A Robust Moving Objects Detection Algorithm Based on Gaussian Mixture Model”, International Conference on Information Technology and Computer Science, ITSC 2009, Volume 1, pages 566-569, 2009.
Y. Rui, S. Xuehua, Y. Shu, “Moving object detection based on an improved Gaussian mixture background model“, Second ISECS International Colloquium on Computing, Communication, Control, and Management, CCCM 2009, Volume 1, pages 12-15, August 2009.
J. Landabaso, J. Pujol-Alcolado, T. Montserrat, D. Marimon, J. Civit, O. Divorra Escoda “A Global Probabilistic Framework for the Foreground, Background and Shadow Classification Task”, International Conference on Image Processing, ICIP 2009, pages 3189-3192, 2009.
B. Zhong, S. Liu, H. Yao, B. Zhang, “Multi-Resolution Background Subtraction For Dynamic Scenes”, ICIP 2009, pages 3193-3196, Cairo, Egypt, November 2009.
H. Li, A. Achim, D. Bull, “GMM-based efficient foreground detection with adaptive region update”, International Conference on Image Processing, ICIP 2009, pages 3181-3184, November 2009.
D. Park, H. Byun, “Object-Wise Multilayer Background Ordering for Public Area Surveillance”, IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2009, September 2009.
H. Zheng, Z. Liu, X. Wang, “Video Segmentation Method with Integrated Multi-features Based on GMM”, International Conference on Intelligence for Modelling Control and Automation, pages 260 – 264, December 2008.
H. Zheng, Z. Liu, X. Wang, “Video Segmentation Method with Integrated Multi-features Based on GMM”, International Conference on Digital Image Processing, DIP 2009, pages 62-66, March 2009.
J. Li, C. Shao, W. Xu, C. Dong, “Real-Time Pedestrian Detection Based on Improved Gaussian Mixture Model”, International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2009,Volume 3, pages 269-272, April 2009.
H. Guo, Y. Dou, T. Tian, J. Zhou, S. Yu, “A Robust Foreground Segmentation Method by Temporal Averaging Multiple Video Frames”, ICALIP 2008, pages 878-882, 2008.
R. Krishna, K. Mc Cusker, N. O'Connor, “Optimising resource allocation for background modeling using algorithm switching”, International Conference on Distributed Smart Cameras, ICDSC 2008, pages 1-7, Palo Alto, CA, USA, September 2008.
Z. Sheng, X. Cui, “An adaptive learning rate GMM for background extraction”, Optoelectronics Letters, Volume 4, Number 6, pages 460-463, November 2008.
H. Guo, Y. Dou, T. Tian, J. Zhou, S. Yu, “A Robust Foreground Segmentation Method by Temporal Averaging Multiple Video Frames”, ICALIP 2008, pages 878-882, 2008.
R. Krishna, K. McCusker, N. O'Connor, “Optimising resource allocation for background modeling using algorithm switching”, International Conference on Distributed Smart Cameras, ICDSC 2008, pages 1-7, Palo Alto, CA, USA, September 2008.
Z. Sheng, X. Cui, “An adaptive learning rate GMM for background extraction”, Optoelectronics Letters, Volume 4, Number 6, pages 460-463, November 2008.
D. Lee, J. Ahn, C. Kim, “Fast Background Subtraction Algorithm using Two-Level Sampling and Silhouette Detection”, ICIP 2009, pages 3177-3180, Cairo, Egypt, November 2009.
Q. Li, Y. Zhang, “Improvement on adaptive mixture Gaussian background model”, Computer Application, Volume 7, pages 2014-2017, 2007.
Z. Wang, X. Tang, “Adaptive background mixture model and shadow removal for traffic images”, 10th Joint Conference on Information Sciences, CVPRIP-IV, pages 916-922, Salt Lake City, USA, July 2007
W. Wang, W. Gao, R. Wang, “A local hierarchical approach for background modeling and moving targets detection”, MIPPR 2009: Automatic Target Recognition and Image Analysis, Proceedings of the SPIE, Volume 7495, pages 1-6, 2009.
J. Liu, D. Zhang, “The Updating Algorithm of Adaptive Gaussian Mixture Background Model”, 2006.
J. Wang, F. He, X. Zhang, Y. Gao, “A Moving Objects Detection Algorithm using Iterative Division and Gaussian Mixture Model”, International Conference on Advanced Computer and Control, ICACC 2010, Volume 5, pages 229-233, 2010.
Y. Wang, Y. Liang, Q. Pan, Y Cheng, C. Zhao, “Spatiotemporal Background Modeling Based On Adaptive Mixture Of Gaussians”, Acta Automatica Sinica,Volume 35, No. 4, April 2009.
X. Liu, H. Liu, Z. Qiang, X. Geng, “Adaptive Background Modeling Based on Mixture Gaussian Model and Frame Subtraction”, Journal of Image and Graphics, April 2008.
C. Yuan, C. Wang, X. Zhang, Y. Liu, “Video Segmentation of Illuminance Abrupt Variation Based on MOGs and Gradient Information”, Journal of Image and Graphics, November 2007.
B. Zhong, H. Yao, S. Liu, “Robust Background Modeling via Standard Variance Feature”, International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010, March 2010.
T. Baloch, “Background Subtraction in Highly Illuminated Indoor Environment”, Master Thesis, Department of Computer Science and Engineering, Indian Institute of Technology, Kanpur, India, 2010.
K. Quast, M. Obermann, A. Kaup, “Real-time Moving Object Detection in Video Sequences using spatio-temporal adaptive Gaussian Mixture Models”, International Conference on Computer Vision Theory and Applications, VISAPP 2010, pages 413-418, Angers, France, May 2010.
J. Molin, “Foreground Segmentation of Moving Objects”, Master Thesis, Department of Electrical Engineering, Linköpings University, Sweden, 2010.
B. Qin, J. Wang, J. Gao, T. Pang, F. Su, “A Traffic Video Background Extraction Algorithm Based on Image Content Sensitivity”, ICSI 2010, pages 603-610, 2010.
Q. Yan, Y. Xu, X. Yang, L. Traversoni, “Real-Time Foreground Detection Based on Tempo-Spatial Consistency Validation and Gaussian Mixture Model”, IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2010, pages 1-4, Shangai, China, March 2010.
T. Huang, X. Fang, J. Qiu, T. Ikenaga, “Adaptively Adjusted Gaussian Mixture Models for Surveillance Applications”, MMM 2010, LNCS 5916, pages 689–694, 2010.
S. Mohamed, N. Tahir, R. Adnan, “Background Modelling and Background Subtraction Performance for Object Detection”, 6th International Colloquium on Signal Processing and Its Applications, CSPA 2010, 2010.
J. Yu, X. Zhou, F. Qian, “Object Kinematic Model: A Novel Approach of Adaptive Background Mixture Models for Video Segmentation”, World Congress on Intelligent Control and Automation, WCICA 2010, pages 6225-6228, Jinan, China, July 2010.
Z. Li, L. Zhong, Y. Liu, “Efficient Foreground Layer Extraction in Video”, Pacific Rim Conference on Multimedia, PCM 2010, Part I, LNCS 6297, pages 319-329, 2010.
C. Huang, R. Wu, “A Multi-layer Scene Model for Video Surveillance Applications”, Pacific Rim Conference on Multimedia, PCM 2010, Part I, LNCS 6297, pages 68-79, 2010.
P. Forczmanski, M. Seweryn, “Surveillance Video Stream Analysis Using Adaptive Background Model and Object Recognition”, International Conference on Computer Vision and Graphics, ICCVG 2010, Part I, LNCS 6374, pages 114-121, 2010.
Y. Zhang, Y. Bai, S. Zhao, “Moving Object Detection Based on Gaussian Mixture Model within the Quotient Space Hierarchical Theory”, Rough Set and Knowledge Technology, RSKT 2010, LNAI 6401, pages 772–777, 2010
J. Park, G. Lee, N. Toan, W. Cho, S. Park, “Moving Object Detection Using Clausius Entropy and Apdative Gaussian Mixture Model,” Journal of The Institute of Electronics Engineers of Korea, Volume 47, No. 1, January 2010.
J. Park, G. Lee, W. Cho, N. Toan, S. Kim, S. Park, “Moving Object Detection based on Clausius Entropy”, IEEE International Conference on Computer and Information Technology, CIT 2010, pages 517-521, June 2010.
J. Suhr, H. Jung, G. Li, J. Kim, “Mixture of Gaussians-based Background Subtraction for Bayer-Pattern Image Sequences”, IEEE Transactions on Circuits and Systems for Video Technology, CSVT 2010, 2010.
M. Shah, J. Deng, B. Woodford, “Localized Adaptive Learning of Mixture of Gaussians Models for Background Extraction”, International Conference of Image and Vision Computing New Zealand, ICVNZ 2010, Queenstown, New Zealand, November 2010.
T. Fabián, “Mixture of Gaussians Exploiting Histograms of Oriented Gradients for Background Subtraction”, International Symposium on Visual Computing, ISVC 2010, Las Vegas, USA, November 2010.
L. Hu, W. Liu, B. Li, W. Xing, “Robust motion detection using histogram of oriented gradients for illumination variations”, International Conference on Industrial Mechatronics and Automation, ICIMA 2010, Volume 2, pages 443-447, Wuhan, China, May 2010.
S. He, Q. Guan, S. Xu, Y. Li, Y. Wu, “Improving mixture Gaussian background model by integrating trace information obtained from Kalman filter”, International Conference on Communications, Circuits and Systems, ICCCAS 2010, pages 378-382, Chengdu, China, July 2010.
J. Kan, J. Tang, K. Li, X. Ou, “Background modeling method based on improved multi-Gaussian distribution”, International Conference on Computer Application and System Modeling, ICCASM 2010, Volume 2, pages 214-218, Taiyuan, China, October 2010.
Z. Qu, M. Yu, J. Liu, “Real-time traffic vehicle tracking based on improved MoG background extraction and motion segmentation”, International Symposium on Systems and Control in Aeronautics and Astronautics, ISSCAA 2010, pages 676-680, Harbin, China, June 2010.
S. Fazli, H. Pour, H. Bouzari, “A Novel GMM-Based Motion Segmentation Method for Complex Background”, GCC 2009, Kuwait, March 2009.
S. Fazli, H. Moradi, H.Bouzari, “Multiple Object Tracking Using Improved GMM-Based Motion Segmentation”, IEEE Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology Conference, ECTI-CON 2009, Pataya, Thailand, May 2009.
S. Fazli, H. Pour, H. Bouzari, “A robust hybrid movement detection method in dynamic background”, International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2009, pages 1134-1137, Pataya, Thailand, May 2009.
W. Wang, H. Qian, P. Chen, S. Chen ,“High level feedback for foreground detection”, IEEE Youth Conference on Information, Computing and Telecommunication, YC-ICT 2009, pages 323-326, Beijing, China, September 2009.
K. Quast, A. Kaup, “AUTO GMM-SAMT: An Automatic Object Tracking System for Video Surveillance in Traffic Scenarios”, EURASIP Journal on Image and Video Processing, Volume 2011, Article ID 814285, 14 pages, 2011.
H. Lin, J. Chuang, T. Liu, “Regularized Background Adaptation: A Novel Learning Rate Control Scheme for Gaussian Mixture Modeling”, IEEE Transaction on Image Processing, 2011.
Y. Qi, Y. Wang, “Memory-based Gaussian Mixture Modeling for Moving Object Detection in Indoor Scene with Sudden Partial Changes”, ICSP 2010, pages 752-755, 2010.
Y. Wang, P. Suo, Y. Qi, “Memorizing GMM to handle sharp changes in moving object segmentation", International Congress on Image and Signal Processing, pages 1832-1835, October 2009.
Y. Qi, Y. Wang, Y. Li “Memory-based Gaussian Mixture Background Modeling”, Acta Automatica Sinica, Volume 36, No. 11, pages 1520-1526, November 2010.
G. Xue, J. Sun, L. Song, “Background Subtraction based on Phase and Distance Transform under Sudden Illumination Change”, International Conference on Image Processing, ICIP 2010, pages 3465-3468, Hong Kong, China, September 2010.
T. Feldmann, L. Diesselberg, A. Worner, “Adaptive Foreground/Background Segmentation Using Multiview Silhouette Fusion”, DAGM 2009, LNCS 5748, pages 522-531, 2009.
T. Feldmann, “Spatio-Temporal Optimization for Foreground/Background Segmentation”, International Workshop on Visual Surveillance, VS 2010, Queenstown, New Zealand, November 2010.
D. Li, L. Dawei, E. Goodman, “Online background learning for illumination-robust foreground detection”, International Conference on Control Automation Robotics and Vision, ICARCV 2010, page 1093, Singapore, Singapore, December 2010.
P. Dickinson, A. Hunter, K. Appiah, “Segmenting video foreground using a multi-class MRF”, International Conference on Pattern Recognition, ICPR 2010, pages 1848-1851, Istanbul, Turkey, August 2010.
H. Zhou, X. Zhang, Y. Gao, P. Yu, “Video background subtraction using improved Adaptive-K Gaussian Mixture Model”, International Conference on Advanced Computer Theory and Engineering, ICACTE 2010, Chengdu, China, August 2010.
Y. Tian, X. Wang, “A fast convergent Gaussian mixture model in moving object detection with shadow elimination”, International Conference on E-Product E-Service and E-Entertainment, ICEEE 2010, Henan, China, November 2010.
L. Li, J. Xu, “Moving human detection algorithm based on Gaussian mixture model“, Chinese Control Conference, CCC 2010, pages 2853-2856, 2010.
Z. Wang, D. Kai, X. Zhang, “Moving target detection algorithm based on Gaussian mixture model”, Proceedings of SPIE, Volume 8878, 2013.
Z. Bin, Y. Liu, “Robust Moving Object Detection and Shadow Removing Based on Improved Gaussian Model and Gradient Information”, International Conference on Multimedia Technology, ICMT 2010, Ningbo, China, October 2010.
Y. Liu, Z. Bin, “The improved moving object detection and shadow removing algorithms for video surveillance”, International Conference on Computational Intelligence and Software Engineering, CISE 2010, Wuhan, China, December 2010.
L. Zhao, X. He, “Adaptive Gaussian mixture learning for moving object detection”, Conference on Broadband Network and Multimedia Technology, IC-BNMT2010, pages 1176-1180, Beijing, China, October 2010.
Y. Li, H. Tian, Y. Zhang, “An improved Gaussian mixture background model with real-time adjustment of learning rate”, International Conference on Information, Networking and Automation, ICINA 2010, Volume1, pages 1512-1515, 2010.
C. Lai, S. Chen, J. Wang, “Gaussian Mixture of Background and Shadow Model”, IPPR Conference on Vision, Graphics and Image Processing, CVGIP 2010, August 2010.
J. Wang, S. Chen, C. Fuh, “Gaussian Mixture of Background and Shadow Model”, ADIS Conference on Computer Graphics, Visualization, Computer Vision, and Image Processing, July 2011.
J. Yang, J. Wang, H. Lu, “A Hierarchical Approach for Background Modeling and Moving Objects Detection”, International Journal of Control, Automation and Systems, pages 940-947, 2010.
J. Shao, Z. Jia, Z. Li, F. Liu, J. Zhao, P. Peng, “A Closed-loop Background Subtraction Approach for Multiple Models based Multiple Objects Tracking”, Journal of Multimedia, Volume 6, No. 1, pages 33-38, February 2011.
X. Zhang, J. Zhou, “Moving target detection in complex scenes based on spatio-temporal domain analysis” , International Congress on Image and Signal Processing, CISP 2010, pages 1520-1523, Yantai, China, October 2010.
Z. Wang, H. Xu, L. Sun, S. Yang, “Background Subtraction in Dynamic Scenes with Adaptive Spatial Fusing”, IEEE International Workshop on Multimedia Signal Processing, pages 1-6, Rio de Janeiro, Brazil, October 2009.
S. Wang, T. Su, S. Lai, “Detecting moving objects from dynamic background with shadow removal”, International Conference on Acoustics, Speech and Signal Processing, ICASSP 2011, Prague, Czeh Republic, May 2011.
W. Zou, D. Zhao, G. Sun, Z. Yu, Y. Wang, “An improved method of target detection based on Gaussian Mixture Model and Average Background Method”, IASTED International Conference on Signal Processing, Pattern Recognition, and Applications, SPPRA 2011, Innsbruck, Austria, February 2011.
H. Wang, J. Wang, H. Ding, Y. Huang, P. Liu, “Moving Target Detection Based on the Improved Gaussian Mixture Model Background Difference Method”, Advanced Materials Research Online, pages 569-574, 2012.
X. Wang, D. Zhao, G. Sun, X. Liu, Y. Wu, “Target Detection Algorithm Based on Improved Gaussian Mixture Model”, International Conference on Electrical, Computer Engineering and Electronics, ICECEE 2015, 2015.
C. Mingzhi, G. Junxiang, “An Improved Background Modeling Method for Target Detection", Future Communication, Computing, Control and Management, pages 117-123, 2012.
H. Wang, P. Miller, “Regularized Online Mixture of Gaussians for background subtraction “, IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2011, Klagenfurt, Austria, September 2011.
P. Li, C. Wang, C. Wang, Y. Liu, “Adaptive Background Model for Arbitrary-Long Stationary Target”, International Conference on Image and Graphics, ICIG 2011, Hefei, Anhui, China, August 2011.
G. Chen, Z. Yu, Q. Wen, Y. Yu, “Improved Gaussian Mixture Model for Moving Object Detection”, AICI 2011, pages 179-186, 2011.
Y. Shi, S. Cheng, S. Quan, J. Chen, D. Chen, “Moving objects detection by Gaussian Mixture Model: A comparative analysis”, Annual Conference on Electrical and Control Engineering, ICECE 2011, pages 1121-1123, September 2011.
Q. Zhu, Y. Xie, J. Gu, L. Wang, “A New Video Object Segmentation Algorithm by Fusion of Spatio-temporal Information Based on GMM Learning”, Advances in Automation and Robotics, Volume 2, pages 641-650, 2011.
B. Jiao, L. Yan, W. Li, “Fast convergent Gaussian Mixture Model in moving objects detection”, IEEE International Conference on Computer Science and Automation Engineering, CSAE 2011, pages 422-425, Shangai, China, June 2011.
Y. Zhang, R. Zhang, S. Song, “Research on GMM Background Modeling and its Covariance Estimation”, Journal on Advanced Materials Research, pages 2327-2333, November 2011.
L. Gao, Y. Fan, N. Chen, Y. Li, X. Li, "Moving Objects Detection Using Adaptive Region-Based Background Model in Dynamic Scenes”, Foundations of Intelligent Systems, Advances in Intelligent and Soft Computing, Volume 122, pages 641-651, 2012.
Z. Mao, A. Gao, W. Wei, L. Sun, S. Chen, “Adaptive Background-Updating and Target Detection in Motion State”, Advances in Automation and Robotics, Volume 2, pages 455-462, 2012.
M. Alvar, A. Sanchez, A. Arranz, “Fast Background Subtraction using Static and Dynamic Gates”, Artificial Intelligence Review, January 2012.
X. Wang, J. Sun, H. Peng, “A Mixture of Gaussian-Based Method for Detecting Foreground Object in Video Surveillance, Computer, Informatics, Cybernetics and Applications, Volume 107, Part 11, pages 1109-1118, 2012.
P. Chen, X. Chen, B. Jin, X. Zhu, “Online EM Algorithm for Background Subtraction”, International Workshop on Information and Electronics Engineering, IWIEE 2012, pages 164-169, 2012.
M. Huang, G. Chen, G. Yang, R. Cao, “An Algorithm of the Target Detection and Tracking of the Video”, International Workshop on Information and Electronics Engineering, IWIEE 2012, pages 2567 -2571, 2012.
P. Liu, J. Liu, X. Tang, “Background subtraction using semantic-based hierarchical GMM”, Electronics Letters, Volume: 4, Issue 14, pages 825–827, July 2012.
M. Wan, X. Qin, L. He, “Background modeling using mixture of Gaussians and Laplacian pyramid decomposition”, International Conference on Soft Computing and Pattern Recognition, pages 33-38, 2011.
S. Jiang, K. Muchtar, C. Lin, L. Kang, C. Yeh, “Background construction by modeling pixel and neighborhood information for video surveillance”, APSIPA Annual Summit and Conference, special session on Intelligent Object Detection and Identification for Visual Surveillance and Security, APSIPA-ASC 2012, Hollywood, CA, USA, December 2012.
M. Haque, M. Murshed, “Background Subtraction for Real-time Video Analytics Based on Multi-hypothesis Mixture-of-Gaussians”, IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2012, China, 2012.
M. Haque, M. Murshed, “Robust Background Subtraction Based on Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed”, IEEE International Workshop on Advances in Automated Multimedia Surveillance for Public Safety, ICME 2012, Australia, 2012.
M. Haque, M. Murshed, “Perception Inspired Foreground Detection”, Technical report, Gippsland School of Information Technology, Monash University, 2012.