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
M. Shah, J. Deng, B. Woodford, “Illumination Invariant Background Model using Mixture of Gaussians and SURF features”, International Workshop on Background Models Challenge, ACCV 2012, Daejeon, Korea, November 2012.
M. Shah, J. Deng, B. Woodford, “Enhancing the Mixture of Gaussians Background Model with Local Matching and Local Adaptive Learning”, International Conference on Image and Vision Computing New Zealand, IVCNZ 2012, Dunedin, New Zealand, November 2012.
P. Holtzhausen, V. Crnojevic, B. Herbst, “An illumination invariant framework for real-time foreground detection”, Journal of Real Time Image Processing, November 2012.
Y. Wang, Y. Liang, L. Zhang, Q. Pan, “Adaptive spatiotemporal background modelling”, IET Computer Vision, Volume 6 , Issue 5, pages 451-458, September 2012.
D. Li, L. Dawei, E. Goodman, “On-line EM Variants for Multivariate Normal Mixture Model in Background Learning and Moving Foreground Detection”, Journal of Mathematical Imaging and Vision, 2012.
R. Liu, J. Tian, “A Mixture Gaussian Model Algorithm of Video Object Subtraction based on Fusion of Contour”, Advances in information Sciences and Service Sciences, AISS 2012, Volume 4, Number 17, September 2012.
Y. Qi, Y. Wang, P. Suo, “A double-subspace adaptive background modeling method based on Gaussian mixture model”, Journal of China University of Petroleum Volume 36, Issue 5, pages 175-178, October 2012.
S. Mukherjee, K. Das, “An Adaptive GMM Approach to Background Subtraction for Application in Real Time Surveillance”, International Journal of Research in Engineering and Technology, Volume 2, Issue 1, pages 25-29, January 2013.
Y. Xia, R. Hu, Z. Wang, T. Lu, “Moving Foreground Detection Based On Spatio-temporal Saliency”, International Journal of Computer Science Issues, IJCSI 2013, Volume 10, Issue 1, No 3, January 2013.
X. Wang, J. Sun, H. Peng, “Foreground Object Detecting Algorithm based on Mixture of Gaussian and Kalman Filter in Video Surveillance”, Journal of computers, pages 693-700, March 2013.
M. Atefian, H. Mahdavi-Nasab, “A Robust Mean-shift Tracking Using GMM Background Subtraction”, 2013.
X. Liu, C. Qi, “Future-data driven modeling of complex backgrounds using mixture of Gaussians”, Neurocomputing, May 2013.
H. Zhou, Y. Gao, G. Yuan, X. Zhang, “A Fast Convergent Adaptive-K Mixture-Of-Gaussian Model for Video Object Segmentation”, Advanced Materials Research, Volumes 694–697, pages 694-697, May 2013.
H. Zhou, Y. Gao, G. Yuan, X. Zhang “A fast convergent adaptive-k mixture-of-Gaussian Model for video object segmentation”, International Conference on Manufacturing Science and Engineering, ICMSE 2013, pages 1919-1924, March 2013.
D. Liu, M. Deng, D. Wang, “Background subtraction based on Gaussian mixture model”, International Conference on Manufacturing Science and Engineering, ICMSE 2013, pages 2021-2026, March 2013.
Y. Wang, Y. Qi, “Memory-based cognitive modeling for robust object extraction and tracking”, Applied Intelligence, Volume 39, pages 614-629, October 2013.
D. Mukherjee, Q. Wu, T. Nguyen, “Gaussian Mixture Model with Advanced Distance Measure based on Support Weights and Histogram of Gradients for Background Suppression”, IEEE Transactions on Industrial Informatics, 2013.
D. Panda, S. Meher, “A Gaussian Mixture Model with Gaussian Weight Learning Rate and Foreground Detection using Neighbourhood Correlation”, 2013.
W. Zhao, X. Zhao, W. Liu, X. Tang, “Long-term background memory based on Gaussian mixture model”, Visual Communications and Image Processing, VCIP 2013, pages 1-5, November 2013.
C. Lin, P. Liu, T. Muindisi, C. Yeh, P. Su, “Non-linear learning for mixture of Gaussians”, Association Annual Summit and Conference on Signal and Information Processing, APSIPA 2013, pages 1-5, November 2013.
Z. Chen, T. Ellis, “Self-adaptive Gaussian mixture model for urban traffic monitoring system, IEEE International Workshop on Visual Surveillance, ICCV 2011, pages 1769-1776, November 2011.
Z. Chen, T. Ellis, “A self-adaptive Gaussian mixture model”, Computer Vision and Image Computing, CVIU 2014, 2014.
G. Deng, K. Guo, “Self-adaptive background modeling research based on change detection and area training”, IEEE Workshop on Electronics, Computer and Applications, pages 59-62, May 2014.
R. Zhang, W. Gong, V. Grzeda, A. Yaworski, M. Greenspan, “Scene Dynamics Estimation for Parameter Adjustment of Gaussian Mixture Models”, IEEE Signal Processing Letters, Volume 21, No. 9, pages 1130-1134, September 2014.
X. Ye and W. Wan, “Fast Background Modelling Using GMM on GPU”, IEEE International Conference on Audio, Language and Image Processing, ICALIP 2014, Shangai, China, July 2014.
Y. Y. Song, N. Fu, X. Li, Q. Liu, “Fast Moving Object Detection Using Improved Gaussian Mixture Models”, IEEE International Conference on Audio, Language and Image Processing, ICALIP 2014, Shangai, China, July 2014.
M. Chethan, S. Uma, B. Ramachandra, “Moving Object Detection Using Background Subtraction and Shadow Removal From Video”, International Journal of Advanced Technology in Engineering and Science, Volume 2, Issue 7, July 2014.
R. Rajagopalan, “A Genetic Algorithm for optimizing background subtraction parameters in computer vision”, 2014.
M. Chen, Q. Yan, Q. Li, G. Wang, M. Yang, “Spatiotemporal Background Subtraction Using Minimum Spanning Tree and Optical Flow”, ECCV 2014, 2014.
S. Varadarajan, P. Miller, H. Zhou, “Spatial mixture of Gaussians for dynamic background modelling”, IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013, pages 63-68, 2013.
S. Varadarajan, H. Wang, P. Miller, H. Zhou, “Regularized Region-based Mixture of Gaussians for Dynamic Background Modelling”, AVSS 2014, Seoul, Korea, August 2014
M. Han, J. Liu, J. Meng, Z. Wang, “A modeling and target detection algorithm based on adaptive adjustment K - ρ for mixture gaussian background“, Journal of Electronics and Information Technology, pages 2023-2027, August 2014.
M. Han, J. Liu, Y. Sun, “A background modeling algorithm based on improved adaptive Mixture Gaussian", Journal of Computers, pages 2239-2244, September 2013.
D. Mukherjee, A. Saha, Q. Wu, W. Jiang, “Dual Gaussian Mixture Model with Pixel History for Background Suppression”, IEEE International Conference on Systems, Man, and Cybernetics, San Diego, USA, October 2014.
Y. Kim, S. Jeong, J. Oh, S. Lee, “Fast MOG (Mixture of Gaussian) Algorithm based on Predicting Model Parameters”, Journal of Arts and Imaging Science, Volume 2, No. 1, February 2015.
N. Katsarakis, A. Pnevmatikakis, Z. Tan, R. Prasad, “Improved Gaussian Mixture Models for Adaptive Foreground Segmentation”, Wireless Personal Communications, May 2015.
Q. Shao, Y. Zhou, L. Li, Q. Chen, “Adaptive background subtraction approach of Gaussian mixture model”, Journal of Image and Graphics, Volume 20, Issue 6, pages 756-776, 2015.
Y. Chen, J. Wang, H. Lu, “Learning sharable models for robust background subtraction”, IEEE International Conference on Multimedia and Expo, ICME 2015,pages 1-6, June 2015.
Z. Iftikhar, P. Premaratne, P. Vial, S. Yang, “Robust Segmentation of Vehicles under Illumination Variations and Camera Movement”, Intelligent Computing Theories and Methodologies, pages 466-476, 2015.
J. Sepulveda, S. Velastin, “F1 Score Assesment of Gaussian Mixture Background Subtraction Algorithms using the MuHAVi Dataset”, ICDP 2015, July 2015.
X. Li, S. Zhu, L. Chen, “Statistical background model-based target detection”, Pattern Analysis and Applications, 2015.
D. Yadav, K. Singh, “Moving Object Detection for Visual Surveillance Using Quasi-euclidian Distance”, International Conference on Computer and Communication Technologies, pages 225-233, September 2015.
Y. Zhang, C. Zhao, J. He, A. Chen, “Vehicles detection in complex urban traffic scenes using Gaussian mixture model with confidence measurement”, IET Intelligent Transport Systems, 2016.
N. Kumar, G. Shobha, “Background Modeling and Foreground Object Detection for Indoor Video Sequence”, International Conference on Data Engineering and Communication Technology, ICDECT 2016, 2016.
X. Hu, J. Zheng, “An Improved Moving Object Detection Algorithm Based on Gaussian Mixture Models”, Open Journal of Applied Sciences, 2016.
A. Thangarajah, Q. Wu, J. Huo, “A unified threshold updating strategy for multivariate Gaussian mixture based moving object detection”, International Conference on High Performance Computing and Simulation, HPCS 2016, pages 570-574, Innsbruck, 2016.
T. Yu, C. Zhang, M. Cohen, Y. Ruy, Y. Wu, “Monocular video foreground/background segmentation by tracking spatial-color Gaussian mixture models”, IEEE Workshop on Motion and Video Computing, 2007.
Y. Wang, K. Loe, J. Wu, “A dynamic conditional random field model for foreground and shadow segmentation”, IEEE Transactions on Pattern Analysis and Machine Intelligence", Volume 2, No. 28, pages 279-289, 2006.
M. Shah, J. Deng, B. Woodford, “Video background modeling: recent approaches, issues and our proposed techniques”, Machine Vision and Applications, Volume 5, No. 5, pages 1105-1119, 2014.
P. Cheng, “Image background reconstruction by Gaussian mixture based model reinforced with temporal-spatial confidence”, Journal of Algorithms and Computational Technology, pages 23-30, January 2016.
L. Ferariu, E. Boghian, “Real-time object detection based on dual stochastic backgrounds with adaptive learning rates”, Buletinul Institutului, Universitatea Tehnica Gheorghe Asachi din Iasi, 2015.
K. Delibasis, T. Goudas, I. Maglogiannis, “A novel robust approach for handling illumination changes in video segmentation”, Engineering Applications of Artificial Intelligence, Volume 49, pages 43-60, 2016.
J. Pulgarin-Giraldo, A. Alvarez-Meza, D. Insuasti-Ceballos, T. Bouwmans, G. Castellanos-Dominguez, “GMM Background Modeling Using Divergence-Based Weight Updating”, Conference Ibero American Congress on Pattern Recognition, CIARP 2016, Lima, Peru, 2016.
Y. Guo, W. Zhu, P. Jiao, J. Chen, “Foreground detection of group-housed pigs based on the combination of Mixture of Gaussians using prediction mechanism and threshold segmentation”, Biosystems Engineering, Volume 125, pages 98-104, September 2014.
C. Wang, Z. Song, “Vehicle detection based on spatial-temporal connection background subtraction”, IEEE International Conference on Information and Automation, pages 320-323, 2011.
R. Ding, X. Liu, W. Cui, Y. Wang, “Intersection foreground detection based on the Cyber-Physical Systems”, IET International Conference on Information Science and Control Engineering, ICISCE 2012, pages 1-7, 2012.
Q. Li, E. Bernal, M. Shreve, R. Loce, “Scene-independent feature- and classifier-based vehicle headlight and shadow removal in video sequences”, IEEE Winter Applications of Computer Vision Workshops, WACVW 2016, pages 1-8. 2016.
P. Hwang, K. Eom, J. Jung, M. Kim “A Statistical Approach to Robust Background Subtraction for Urban Traffic Video”, International Workshop on Computer Science and Engineering, pages 177-181, 2009.
J. Lan ,Y. Jiang, D. Yu, “A new automatic obstacle detection method based on selective updating of Gaussian mixture model”, International Conference on Transportation Information and Safety, ICTIS 2015, pages 21-25, 2015.
X. Li, Y. Wu, “Image object detection algorithm based on improved Gaussian mixture model,” Journal of Multimedia, Volume 9, No. 1, pages 152-158, 2014.
M. Sivagami, T. Revathi, L. Jeganathan, “An optimised background modelling for efficient foreground extraction”, Inderscience International Journal of High Performance Computing and Networking, No. 1, Volume 10, 2017.
M. Radolko, E. Gutzeit, “Video Segmentation via a Gaussian Switch Background Model and Higher Order Markov Random Fields”, International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISAPP 2015, 2015.
M. Chen, Q. Yang, Q. Li, G. Wang, M. Yang, “Spatiotemporal GMM for Background Subtraction with Superpixel Hierarchy”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.
I. Martins, P. Carvalho, L. Corte-Real, J. Alba-Castro, “BMOG: Boosted Gaussian Mixture Model with Controlled Complexity”, IbPRIA 2017, 2017.
B. Kiran, S. Yogamani, “Real-Time Background Subtraction Using Adaptive Sampling and Cascade of Gaussians”, Preprint, 2017.
B. Farou, M. Kouahla, H. Seridi, H. Akdag, “Efficient local monitoring approach for the task of background subtraction”, Engineering Applications of Artificial Intelligence, Volume 64, pages 1-12, September 2017.
C. Li, Z. Bao, X. Wang, J. Tang, "Moving object detection via robust background modeling with recurring patterns voting", Multimedia Tools Applications, pages 1-14, July 2017.
R. Zhang, X. Liu, J. Hu, K. Chang, K. Liu, “A fast method for moving object detection in video surveillance image”, Signal, Image and Video Processing, 2017.
T. Akilan, J. Wu, Y. Yang, "Fusion-based foreground enhancement for background subtraction using multivariate multi-model Gaussian distribution", Information Sciences", pages 414-431, 2018.
T. Akilan, J. Wu, J. Huo, "A unified threshold updating strategy for multivariate Gaussian mixture based moving object detection", International Conference on High Performance Computing and Simulation, HPCS 2016, 2016.
E. Komagal, B Yogameena, “Region MoG and texture descriptor-based motion segmentation under sudden illumination in continuous pan and excess zoom”, Multimedia Tools and Applications, November 2017.
D. Rout, B. Subudhi, T. Veerakumar, S. Chaudhury, "Spatio-contextual Gaussian Mixture Model for Local Change Detection in Underwater Video", Expert Systems With Applications, December 2017.
X. Lu, C. Xu, L. Wang, L. Teng, “Improved Background Subtraction Method for Detecting Moving Objects based on GMM", IEEJ Transactions on Electrical and Electronics Engineering", June 2018.
S. Ali, K. Goyal, J. Singhai, "Moving object detection using self adaptive Gaussian Mixture Model for real time applications”, International Conference on Recent Innovations in Signal processing and Embedded Systems, RISE 2017, Bhopal, India, pages 153-156, 2017.
H. Shi, C. Liu, "A New Global Foreground Modeling and Local Background Modeling Method for Video Analysis", International Conference on Machine Learning and Data Mining in Pattern Recognition, pages 49-63, July 2018.
B. Garcia-Garcia, F. Gallegos-Funes, A. Rosales-Silva, "A Gaussian-Median Filter for Moving Objects Segmentation Applied for Static Scenarios", Intelligent Systems Conference, IntelliSys 2018, pages 478-493, September 2018.
R. Chavan, S. Gengaje, S. Gaikwad, “Multi-Object Detection using Modified GMM-Based Background Subtraction Technique”, International Conference on ISMAC in Computational Vision and Bio-Engineering, pages945-954, 2018.
E. Alsoruji, S. Majumdar, “A Video Segmentation Strategy for Video Processing Applications on Hadoop Clusters”, IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, 2018.
K. Makantasis, A. Doulamis, N. Doulamis, “Variational inference for background subtraction in infrared imagery”, International Symposium on Visual Computing, pages 693-705, June 2015.
A. Nikitakis, I. Papaefstathiou, K. Makantasis, A. Doulamis, “A novel background subtraction scheme for in-camera acceleration in thermal imagery”, Design, Automation and Test in Europe Conference and Exhibition, DATE 2016, Dresden, Germany, pages 1497-1500, 2016.
K. Makantasis, A. Nikitakis, A. Doulamis, N. Doulamis, Y. Papaefstathiou, "Data-Driven Background Subtraction Algorithm for in-Camera Acceleration in Thermal Imagery", IEEE Transactions on Circuits and Systems for Video Technology, 2017.
N. Wafa, S. Hamid, K. Nadjib, "A New Process for Selecting the Best Background Representatives based on GMM", International Journal of Informatics and Applied Mathematics, Volume 1, No. 1, pages 35-46, 2019.
X. Lu, C. Xu, "Novel Gaussian mixture model background subtraction method for detecting moving objects", IEEE International Conference of Safety Produce Informatization, IICSPI 2018, pages 6-10.Chongqing, China, 2018.
H. Kim, "A knowledge based infrared camera system for invisible gas detection utilizing image processing techniques", Journal of Ambient Intelligence and Humanized Computing, pages 1-11, June 2019.
P. Tadiparthi, S. Yerramalle, "Model-based Approach for Effective Segmentation of Images based on Background Subtraction", International Journal of Engineering and Advanced Technology, April 2019.
J. Jeong, J. Choi, “Adaptive Background Modeling Considering Stationary Object and Object Detection Technique based on Multiple Gaussian Distribution”, Journal of the Korea Society of Computer and Information, Volume 23, No.11, pages 51-57, 2018.
X. Jin, P. Niu, L. Liu,"A GMM-Based Segmentation Method for the Detection of Water Surface Floats", IEEE Access, 2019.
J. Zuo, Z. Jia, J. Yang, N. Kasabov, "Moving Target Detection based on Improved Gaussian Mixture Background Subtraction in Video Images", IEEE Access, 2019.
S. Liu, X. Zhao, X. Tang, "Background Subtraction based on Perception-Contained Piecewise Memorizing Framework", Computing and Informatics, Volume 37, pages 865-893, 2018.
S. Song, J. Kim , "SFMOG : Super Fast MOG based Background Subtraction Algorithm", Journal of IKEEE, Volume 23, Issue 4, pages 1415-1422, 2019.
I. Martins, P. Carvalho, L. Corte-Real, J. Alba-Castro, "Texture collinearity foreground segmentation for night videos", Computer Vision and Image Understanding, Volume 23, No. 4, pages 1415-1422, June 2020.
K. Goyal, J. Singhai, "Recursive-learning-based moving object detection in video with dynamic environment", Multimedia Tools and Applications, 2020.
Y. Xu, J. Dong, Z. Han, P. Wang, "Multichannel Correlation Clustering Target Detection", Information and Control, Volume 49, No. 3, pages 335-345, 2020.
T. Subetha, S. Chitrakala, M.Theja, "Fusion-based Gaussian Mixture Model for Background Subtraction from videos", Inderscience International Journal of Computer Applications in Technology, Volume 66, No. 1, November 2021.
R. Chavan, “Object Detection by Automatically Tuning GMM Training Parameters using Cuckoo Search Optimization Technique”, GIS Science Journal, pages 496-510, 2021.
F. Joy, V. Vijayakumar, "An Improved Gaussian Mixture Model with Post-processing for Multiple Object Detection in Surveillance Video Analytics", International Journal of Electrical and Computer Engineering Systems, pages 653-660, 2022.
S. Rakesh, N. Hegde, M. Gopalachari, D. Jayaram, B. Madhu, M. Hameed, R. Vankdothu, L. Kumar, “Moving object detection using modified GMM based background subtraction”, Measurement: Sensors, 2023.
Y. Liu, G. Tang, W. Zou, "Video monitoring of Landslide based on background subtraction with Gaussian mixture model algorithm”, IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021, 2021.
Y. Zhuo, H. Deqiang, Z. Xu, Y. Yu, "Improved Mixed Gaussian Model for Background Subtraction Based on Color Channel Fusion”, Chinese Control Conference, CCC 2023, Tianjin, China, pages 7965-7970, 2023.