Kernel Density Estimation

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

T. Bouwmans, F. El Baf, B. Vachon, “Statistical Background Modeling for Foreground Detection: A Survey”, Handbook of Pattern Recognition and Computer Vision, World Scientific Publishing, Volume 4, Part 2, Chapter 3, pages 181-199, January 2010.

List of Publications on Background Modeling using Kernel Density Estimation for Foreground Detection

A. Elgammal, D. Harwood, L. Davis, “Non-parametric Model for Background Subtraction”, Frame Rate Workshop, IEEE 7th International Conference on Computer Vision, ICCV 1999, Kerkyra, Greece, September 1999.

A. Elgammal, D. Harwood, L. Davis, “Non-parametric Model for Background Subtraction”, 6th European Conference on Computer Vision, ECCV 2000, Dublin, Ireland, June 2000.

A. Elgammal, R. Duraiswami, D. Harwood, L. Davis “Background and Foreground Modeling using Non-parametric Kernel Density Estimation for Visual Surveillance”, Proceedings of the IEEE, July 200

A. Elgammal, “Efficient Non parametric Kernel Density Estimation for Real-time Computer Vision”, PhD Thesis, Department of computer science, University of Maryland, College Park, 2002.

A. Tavakkoli, “Automatic Video Object Plane Extraction using non-Parametric Kernel Density Estimation”, Mathematical Methods in Computer Vision, University of Nevada, Reno, NV, May 2005.

A. Tavakkoli, M. Nicolescu, G. Bebis, “Automatic Robust Background Modeling Using Multivariate Non-Parametric Kernel Density Estimation for Visual Surveillance”, International Symposium on Visual Computing, ISVC 2005, Lake Tahoe, Nevada, December 2005.

A. Tavakkoli, M. Nicolescu, G. Bebis, “Automatic Statistical Object Detection for Visual Surveillance”, IEEE Southwest Symposium on Image Analysis and Interpretation, Denver, Colorado, SSIAI 2006, March 2006.

C. Ianasi, V. Gui, C. Toma, D. Pescaru, “A Fast Algorithm for Background Tracking in Video Surveillance, Using Nonparametric Kernel Density Estimation”, Facta Universitatis, Series: Electronics and Energetics, Volume 18, No. 1, pages 127-144, April 2005.

T. Tanaka, A. Shimada, D. Arita, R. Taniguchi, “Non-parametric Background and Shadow Modeling for Object Detection”, ACCV 2007, pages 159-168, Tokyo, Japan, November 2007.

T. Tanaka, A. Shimada, D. Arita, R. Taniguchi, “A Fast Algorithm for Adaptive Background Model Construction Using Parzen Density Estimation”, AVSS 2007, pages 528-533, London, UK, September 2007.

Z. Zivkovic ,“Efficient adaptive density estimation per image pixel for the task of background subtraction “, Pattern Recognition Letters, Volume 27, No 7, pages 773-780, January 2006.

S. Cvetkovic, P. Bakker, J. Schirris, P. de With, “Background Estimation and Adaptation Model With Light-Change Removal for Heavily Down-Sampled Video Surveillance Signals”, ICIP 2006, pages 1829-1832, 2006.

S. Witherspoon, M. Zhang, “Negative Coefficient Polynomial Kernel Density Estimation for Visualization”, International Conference on Modeling and Simulation, page 567, Montreal, Canada, June 2007.

R. Ramezani, P. Angelov, X. Zhou, “A Fast Approach to Novelty Detection in Video Streams using Recursive Density Estimation”, 4th International IEEE Symposium on Intelligent Systems, Volume 2, pages 142-147, Varna, Bulgaria, September 2008.

P. Sadeghi-Tehran, P. Angelov, R. Ramezani, “A fast approach to autonomous real-time novelty detection, multi objects identification and tracking in video stream using recursive density estimation”, International Conference on Information Processing and Uncertainty Management, IPMU 2010, pages 30-43, July 2010.

Y. Mao, P. Shi, “Multimodal background model with noise and shadow suppression for moving object detection”, Journal of Southeast University, Volume 20, No.4, pages 423-426, December 2004.

Y. Mao, P. Shi , “Diversity sampling based kernel density estimation for background modeling”, Journal of Shanghai University, Volume 9, Issue 6, Pages 506-509, December 2005.

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.

A. Tavakkoli, M. Nicolescu, G. Bebis, “An Adaptive Recursive Learning Technique for Robust Foreground Object Detection”, ECCV 2006, Graz, Austria, May 2006.

A. Tavakkoli, M. Nicolescu, G. Bebis, “Robust Recursive Learning for Foreground Region Detection in Videos with Quasi-Stationary Backgrounds”, ICPR 2006, Hong Kong, August 2006.

A. Tavakkoli, R. Kelly, C. King, M. Nicolescu, M. Nicolescu, G. Bebis, “A Vision-Based Approach for Intent Recognition”, International Symposium on Visual Computing, Lake Tahoe, Nevada, November 2007.

A. Tavakkoli, M. Nicolescu, G. Bebis, M. Nicolescu, “Non-parametric Statistical Background Modeling for Efficient Foreground Region Detection”, Journal of Machine Vision and Applications, April 2008.

Y. Sheikh, M. Shah, “Bayesian Object Detection in Dynamic Scenes”, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2005, June 2005.

Y. Sheikh, M. Shah, “Bayesian Modeling of Dynamic Scenes for Object Detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 27, No. 11, pages 1778-1792, November 2005.

X. Zhang, J. Yang, "Foreground segmentation based on selective foreground model", Electronics Letters, Volume 44, No. 14, page 851, 2008.

B. Antic, V. Crnojevic, “Joint Domain-Range Modeling of Dynamic Scenes with Adaptive Kernel Bandwidth”, ACIVS 2007, 2007.

A. Mittal, “Motion-Based Background Subtraction using Adaptive Kernel Density Estimation”, CVPR 2004, Washington, USA, July 2004.

T. Parag, A. Elgammal, A. Mittal, “A Framework for Feature Selection for Background Subtraction”, CVPR 2006, June 2006.

P. Pahalawatta, D. Depalov, T. Pappas, A. Katsaggelos, “Detection, Classification, and Collaborative Tracking of Multiple Targets Using Video Sensors”, International Workshop on Information Processing for Sensor Networks, IPSN 2003, pages 529-544, Palo Alto, CA, USA, April 2003.

B. Orten, M. Soysal, A. Alatan, “Person Identification in Surveillance Video by Combining Mpeg-7 Experts”, WIAMIS 2005, pages 352-355, Montreux, Switzerland, April 2005.

T. Tanaka, A. Shimada, D. Arita, R. Taniguchi, “Object Segmentation under Varying Illumination based on Combinational Background Modeling”, Proceedings of the 4th Joint Workshop on Machine Perception and Robotics, MPR 2008, 2008.

T. Tanaka, A. Shimada, D. Arita, R. Taniguchi, Y. Tomiura, “Use of fast algorithm for adaptive background modeling with Parzen density estimation to detect objects “, Journal of the Institute of Image Information and Television Engineers, Volume 62, Issue 12, pages 2045-2052, December 2008.

A. Shimada, R. Taniguchi, “Object Detection under Varying Illumination based on Adaptive Background Modeling Considering Spatial Locality”, International Workshop on Computer Vision, MIRU 2008, July 2008.

T. Tanaka, A. Shimada, D. Arita, R. Taniguchi, “Object Detection under Varying Illumination based on Adaptive Background Modeling Considering Spatial Locality”, PSVIT 2009, Lecture Notes In Computer Science, Volume 5414, pages 645-656, Tokyo, Japan, January 2009.

T. Tanaka, A. Shimada, D. Arita, R. Taniguchi, “Object Segmentation Based on Adaptive Background Model Considering Spatio-temporal Features”, Pattern Recognition and Media Understanding, PRMU 2009, Tohoku Institute of Technology, Japan, March 2009

Z. Li, X. Tian, Y. Chen, “Background modeling based on region segmentation”, 7th World Congress on Publication Intelligent Control and Automation, WCICA 2008, pages 3613-3618, June 2008.

Z. Li, P. Jiang, H. Ma, J. Yang, D. Tang, “A model for dynamic object segmentation with kernel density estimation based on gradient features”, Image and Vision Computing, 2009.

J. Gu, Z. Liu, Z. Zhang, “Novel moving object segmentation algorithm using kernel density estimation and edge information”, Journal of Computer-Aided Design and Computer Graphics, Volume 21, Issue 2, pages 223-228, February 2009.

D. Culibrk, D. Socek, O. Marques, B. Furht, “Automatic kernel width selection for neural network based video object segmentation”, VISAPP 2007, Barcelona, Spain, March 2007.

S. Erdis, “Automatic Time Scale Selection for Sample Based Background Modeling”, Master Thesis, Örebro University, Sweden, 2007.

R. Garnett, “Modeling Local Video Statistics for Anomaly Detection”, Masters Thesis, Department of Computer Science and Engineering, Washington University, USA, May 2004.

J. McHugh, J. Konrad, V. Saligrama, P. Jodoin, “Probabilistic methods for adaptive background subtraction”, IEEE Signal Processing Letters, March 2008.

J. McHugh, “Probabilistic methods for adaptive background subtraction”, Master Thesis, Boston University, USA, 2008.

G. Wang, D. Liang, X. Wang, Y. Wang, “A multimodal background model based on a binned kernel density estimation”, Computer Applications, Volume 27, No. 5, May 2007.

T. Ko, S. Soatto, D. Estrin, “Background Subtraction on Distributions”, 10th European Conference on Computer Vision, ECCV 2008, October 2008.

H. Zhou, Z. Zeng, J. Zhou, “Motion Detection with Background Clutter Suppression Based on KDE Model”, ICIC 2008, pages 466-473, 2008.

H. Kim, Suryanto, D. Kim, D. Zhang, S. Ko, “Fast Object Detection Method for Visual Surveillance”, ITC-CSSC 2008, 2008.

B. Han, D. Comaniciu, L. Davis, “Sequential kernel density approximation through mode propagation: applications to background modeling”, Proceeding Asian Conference on Computer Vision, ACCV 2004, 2004.

B. Han, D. Comaniciu, Y. Zhu, L. Davis, “Sequential Kernel Density Approximation and Its Application to Real-Time Visual Tracking”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007.

H. Wang, M. Ren, J. Yang, “Background Modeling Method Based on Sequential Kernel Density Approximation”, Chinese Conference on Pattern Recognition, CCPR 2008, pages 1-6, October 2008.

N. Martel-Brisson, A. Zaccarin, “Unsupervised Approach for Building Non-Parametric Background and Foreground Models of Scenes with Significant Foreground Activity”, ACM Workshop on Vision Networks for Behavior Analysis, VNBA 2008, pages 93-100, Vancouver, British Columbia, Canada, October 2008.

Z. Liu, W. Chen, K. Huang, T. Tan, “A Probabilistic Framework Based on KDE-GMM Hybrid Model for Moving Object Segmentation in Dynamic Scenes”, VS 2008, 2008.

K. Sung, Y. Hwang, I. Kweon, “Robust Background Maintenance for Dynamic Scenes with Global Intensity Level Changes”, International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2008, pages 759-762, 2008.

Y. Hwang, K. Sung, J. Chae, Y. Park, I. Kweon, “Robust background maintenance by estimating global intensity level changes for dynamic scenes”, Intelligent Service Robotics, ISR 2009, Volume 2, No. 3, pages 187-194, 2009.

A. Elgammal, “Efficient Non parametric Kernel Density Estimation for Real-time Computer Vision”, PhD Thesis, Department of computer science, University of Maryland, College Park, 2002.

S. Mahamud, “Comparing Belief Propagation and Graph Cuts for Novelty Detection”, CVPR 2006, Volume 1, pages 1154-1159, 2006.

J. Hu, J. Jiang, M. Qi, “A Motion Detection Approach Based on Context Modeling”, CEPS 2006, pages 78-80, March 2006.

O. Akman, “Multi-Camera Video Surveillance Detection, Occlusion Handling, Tracking and Event Recognition”, PhD Thesis, Middle East Technical University, 2007.

P. Sadeghi-Tehran, P. Angelov, R. Ramezani, “A fast approach to autonomous real-time novelty detection, multi objects identification and tracking in video stream using recursive density estimation” International Conference on Information Processing and Uncertainty Management, IPMU 2010, pages 30-43, Dortmund, Germany, July 2010.

R. Vemulapalli, R. Aravind, “Spatio-temporal nonparametric background modeling and subtraction”, International Conference on Computer Vision Workshops, ICCV 2009, pages1145-1152, Kyoto, Japan, September 2009.

E. Zhou, C. Liu, L. Zhang, S. Gong, Q. Liu, “Foreground object detection based on time information window adaptive kernel density estimation”, Journal on Communications, Tongxin Xuebao, Volume 32, Issue 3, pages 106-114, 2011.

Q. Zhu, G. Liu, Y. Xie, “Dynamic Video Segmentation via a Novel Recursive Kernel Density Estimation”, International Conference on Image and Graphics, ICIG 2011, Hefei, Anhui, China, August 2011.

Q. Zhu, G. Liu, Z. Wang, H Chen, Y. Xie, “A novel video object segmentation based on recursive Kernel Density Estimation”, ICINFA 2011, 2011.

A. Kolawole, A. Tavakkoli, “Robust Foreground Detection in Videos using Adaptive Color Histogram Thresholding and Shadow Removal”, International Symposium Visual Computing, Las Vegas, USA, September 2011.

J. Park, C. Lee, “Improved Background Modeling Through Color De-Correlation”, European Signal Processing Conference, EUSIPCO 2011, Barcelona, Spain, September 2011.

J. Park, C. Lee, “Bayesian rule-based complex background modelling and foreground detection”, SPIE Optical engineering, Volume 49, No. 2, February 2010.

C. Cuevas, R. Mohedano, N. García, “Kernel bandwidth estimation for moving object detection in non-stabilized cameras”, Optical Engineering Letters, April 2012.

Q. Zhu, Z. Zhang, Y. Xie, “A Recursive Kernel Density Learning Framework for Robust Foreground Object Segmentation”, Applied Mathematics and Information Sciences, Volume 6, No. 1, pages 363-369, 2012.

J. Qiao, H. Zhu, J. Shi, “Fast kernel density estimation method for background modeling”, Computer Engineering and Applications, Volume 48, Issue 5, pages 192-193, 2012.

C. Wan, C. Wang, K. Zhang , “MRKDSBC: A Distributed Background Modeling Algorithm Based on MapReduce”, International Symposium in Neural Network, ISNN 2012, Part I, LNCS 7367, pages 668-677, 2012.

Y. Mao, M. Chen, Q. Meng, “Improved Kernel Density Background Estimation with Diversity Sampling and Neighbor Information for Traffic Monitoring”, Recent Advances in Computer Science and Information Engineering, Lecture Notes in Electrical Engineering, 2012, Volume 128, pages 281-286, 2012.

J. Lee, M. Park, “An Adaptive Background Subtraction Method Based on Kernel Density Estimation”, Sensors 2012, Volume 12, pages 279-300, September 2012.

X. Xu, Z. Huang, Q. Guo, A. Li, Q. Mo, “A Multi-Layer Background Subtraction Based on Gaussian Pyramid for Moving Objects Detection”, Journal of Control Engineering and Technology, JCET 2012, Volume 2, Issue 4, pages 160-167, October 2012.

J. Hao, C. Li, Z. Kim, Z. Xiong, “Spatio-Temporal Traffic Scene Modeling for Object Motion Detection”, IEEE Transactions on Intelligent Transportation Systems, 2012.

P. Jiang, W. Jin, “Adaptive foreground detection based on weighted kernel density estimation”, Journal of Southwest Jiaotong University, Volume 47, Issue 5, pages 769-775, October 2012.

Q. Zhu, Z. Song, Y. Xie, “An Efficient r-KDE Model for the Segmentation of Dynamic Scenes”, international Conference on Pattern Recognition, ICPR 2012, pages 198-201, Tsukuba, Japan, November 2012.

J. Huang, Z. Li, N. Tang, “A Background Subtraction Method with Spatial-temporal Information Analysis for False Detection Suppression”, Journal of Computational Information Systems, Volume 9, Issue 2, pages 565-574, 2013.

J. Peng, J. Weidong, “Statistical background subtraction with adaptive threshold”, International Congress on Image and Signal Processing, CISP 2012, pages 123-127, October 2012.

C. Cuevas, N. García, “Tracking-based non-parametric background-foreground classification in a chromaticity-gradient space”, International Conference on Image Processing, ICIP 2010, Hong Kong, China, September 2010.

C. Cuevas, N. García, “Automatic Bandwidth Estimation Strategy for High-Quality Non-Parametric Modeling Based Moving Object Detection”, International Conference on Image Processing, ICIP 2011, September 2011.

C. Cuevas, R. Mohedano, N. García, “Versatile bayesian classifier for moving object detection by non-parametric background-foreground modeling”, International Conference on Image Processing, ICIP 2012, pages 313-316, Orlando, Florida, USA, October 2012.

C. Cuevas, R. Mohedano, F. Jaureguizar, N. García, “High-quality real-time moving object detection by non-parametric segmentation”, IEEE Electronics Letters, Volume 46, Issue 13, pages 910-911, June 2010.

C. Cuevas, R. Mohedano, N. García, “Adaptable Bayesian classifier for spatio-temporal non-parametric moving object detection strategies”, Optics Letters, June 2012.

C. Cuevas, N. García, “Improved background modeling for real-time spatio-temporal non-parametric moving object detection strategies”, Image and Vision Computing, June 2013.

C. Cuevas, D. Berjon, F. Moran, N. García, "GPGPU implementation of an improved nonparametric background modeling for moving object detection strategies",

IEEE International Conference on Consumer Electronics, ICCE 2013, pages 23-24, 2013.

D. Berjon, C. Cuevas, F. Moran, N. Garcia, "Region-based Moving Object Detection Using Spatially Conditioned Nonparametric Models in a GPU", IEEE International Conference on Consumer Electronics, ICCE 2014, pages 363-364, January 2014.

C. Cuevas, R. Martinez, D. Berjon, N. Garcia, "Detection of Stationary Foreground Objects Using Multiple Nonparametric Background-Foreground Models on a Finite State Machine", IEEE Transactions on Image Processing, Volume 26, No. 3, pages 1127-1142, March 2017.

C. Cuevas, R. Martinez, D. Berjon, N. Garcia, "Detection of Stationary Foreground Objects Using Multiple Nonparametric Background-Foreground Models on a Finite State Machine", IEEE International Conference on Image Processing, ICIP 2017, September 2017.

D. Berjon, C. Cuevas, F. Moran, N. Garcia, "Real-time nonparametric background subtraction with tracking-based foreground update", Pattern Recognition, September 2017.

Y. Soh, Y. Hae, A. Mehmood, R. Hadi Ashraf, I. Kim, “Performance Evaluation of Various Functions for Kernel Density Estimation”, Open Journal of Applied Sciences, Volume 3, No. 1B, pages 58-64, March 2013.

T. Rui, Z. Yang, Y. Zhou, H. Fang, J. Zhu, Target detection based on kernel density estimation combined with correlation coefficient, Pacific-Rim Conference on Multimedia, PCM 2013, pages 769-778, Nanjing; China, December 2013.

Q. Zhu, L. Shao, Q. Li, Y. Xie, “Recursive kernel density estimation for modeling the Background and segmenting moving objects”, ICASSP 2013, 2013.

C. Spampinato, S. Palazzo, D. Giordano, “Kernel Density Estimation Using Joint Spatial-Color-Depth Data for Background Modeling”, ICPR 2014, 2014.

P. Angelov, A. Wilding, “RTSDE: Recursive Total-Sum-Distances-based Density Estimation Approach and its Application for Autonomous Real-Time Video Analytics”, IEEE Symposium on Evolving and Autonomous Learning Systems, EALS 2014, 2014.

P. Kowaleczko, P. Rokita, “Enhancing Tracking Capabilities of KDE Background Subtraction-Based Algorithm Using Edge Histograms”, International Conference on Computer Recognition Systems CORES 2015, pages 745-754, 2015.

S. Chakraborty, M. Paul, M. Murshed, M. Ali, “Adaptive weighted non-parametric background model for efficient video coding”, NeuroComputing, 2016.

M. Narayana, A. Hanson, E. Learned-Miller, “Background modeling using adaptive pixelwise kernel variances in a hybrid feature space”, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011, pages 2104-2111, 2012

M. Narayana, E. Learned-Miller, A. Hanson, “Improvements in joint domain-range modeling for background subtraction”, British Machine Vision Conference, BMVA 2012, 2012.

M. Narayana, E. Learned-Miller, A. Hanson, “Background subtraction - separating the modeling and the inference”, Machine Vision and Applications, 2014.

Z. Chen, R. Wang, Z. Zhang, H. Wang, L. Xu, "Background-Foreground Interaction for Moving Object Detection in Dynamic Scenes", Information Sciences, January 2019.

X. Yang ,T. Feng, "KNN Non-Parametric Kernel Density Estimation Method for Motion Foreground Detection Based on Gaussian Filtering", International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2019, pages 93-96, Hangzhou, China, 2019.

S. Sahoo, P. Nanda, "Adaptive Feature Fusion and Spatio-Temporal Background Modeling in KDE Framework for Object Detection and Shadow Removal", IEEE Transactions on Circuits and Systems for Video Technology, Volume 32, No. 3, pages 1103-1118, March 2022.