Recent Approaches in Background Modeling for Static Cameras

This chapter gives an overview of recent background modeling and foreground detection, and presents resources, datasets and codes publicly available.

[1]  Fatimah Khalid,et al.  A Block-Based Multi-Scale Background Extraction Algorithm , 2010 .

[2]  Junzhou Huang,et al.  Learning with dynamic group sparsity , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[3]  Charles Guyon,et al.  Robust Principal Component Analysis for Background Subtraction: Systematic Evaluation and Comparative Analysis , 2012 .

[4]  Yuan-Kai Wang,et al.  The Design of Background Subtraction on Reconfigurable Hardware , 2012, 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[5]  Andrés Marino Álvarez-Meza,et al.  Video Segmentation based on Multi-kernel Learning and Feature Relevance Analysis for Object Classification , 2013, ICPRAM.

[6]  Thierry Bouwmans,et al.  Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey , 2011 .

[7]  Gerhard Rigoll,et al.  Background segmentation with feedback: The Pixel-Based Adaptive Segmenter , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[8]  Yue Liu,et al.  Study on background modeling method based on robust principal component analysis , 2011, 2011 International Conference on Electrical and Control Engineering.

[9]  Nizar Bouguila,et al.  A Robust Video Foreground Segmentation by Using Generalized Gaussian Mixture Modeling , 2007, Fourth Canadian Conference on Computer and Robot Vision (CRV '07).

[10]  Zhixun Su,et al.  Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation , 2011, NIPS.

[11]  Xi Chen,et al.  Direct Robust Matrix Factorizatoin for Anomaly Detection , 2011, 2011 IEEE 11th International Conference on Data Mining.

[12]  Martin Kleinsteuber,et al.  pROST: a smoothed $$\ell _p$$ℓp-norm robust online subspace tracking method for background subtraction in video , 2013, Machine Vision and Applications.

[13]  Nizar Bouguila,et al.  Online variational learning of finite Dirichlet mixture models , 2012, Evol. Syst..

[14]  Juan Rosell-Ortega,et al.  A Combined Self-Configuring Method for Object Tracking in Colour Video , 2010, 2010 20th International Conference on Pattern Recognition.

[15]  G. Giannakis,et al.  Sparsity control for robust principal component analysis , 2010, 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers.

[16]  Hadi Sadoghi Yazdi,et al.  A Novel Approach in Video Scene Background Estimation , 2010 .

[17]  Tao Xiang,et al.  Background Subtraction with Dirichlet Processes , 2012, ECCV.

[18]  Ming Ye,et al.  A New Approach of Dynamic Background Modeling for Surveillance Information , 2008, 2008 International Conference on Computer Science and Software Engineering.

[19]  James Theiler,et al.  Local principal component pursuit for nonlinear datasets , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[20]  Marek Wojcikowski,et al.  FPGA-Based Real-Time Implementation of Detection Algorithm for Automatic Traffic Surveillance Sensor Network , 2012, J. Signal Process. Syst..

[21]  Sidney S. Fels,et al.  Evaluation of Background Subtraction Algorithms with Post-Processing , 2008, 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance.

[22]  Thomas S. Huang,et al.  Base selection in estimating sparse foreground in video , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[23]  Takao Nishitani,et al.  A precise and stable foreground segmentation using fine-to-coarse approach in transform domain , 2008, 2008 15th IEEE International Conference on Image Processing.

[24]  Luigi di Stefano,et al.  Coarse-to-fine strategy for robust and efficient change detectors , 2005, IEEE Conference on Advanced Video and Signal Based Surveillance, 2005..

[25]  Tsung-Han Tsai,et al.  Architecture design for a low-cost and low-complexity foreground object segmentation with Multi-model Background Maintenance algorithm , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[26]  Ye-peng Guan,et al.  Wavelet Multi-Scale Transform Based Foreground Segmentation and Shadow Elimination , 2008 .

[27]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Marc Van Droogenbroeck,et al.  ViBE: A powerful random technique to estimate the background in video sequences , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[29]  Martin Bouchard,et al.  A multi-criteria model for robust foreground extraction , 2005, VSSN '05.

[30]  Taniguchi Rin-ichiro,et al.  Object Detection Based on Gaussian Mixture Predictive Background Model under Varying Illumination , 2008 .

[31]  Thierry Bouwmans,et al.  A fuzzy approach for background subtraction , 2008, 2008 15th IEEE International Conference on Image Processing.

[32]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[33]  Wen Hu,et al.  Efficient background subtraction for tracking in embedded camera networks , 2012, IPSN.

[34]  Howida A. Shedeed,et al.  A new technique for background modeling and subtraction for motion detection in real-time videos , 2010, 2010 IEEE International Conference on Image Processing.

[35]  Ming-hui Du,et al.  Student's t-distribution mixture background model for efficient object detection , 2012, 2012 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2012).

[36]  K. S. Venkatesh,et al.  A Multiscale Co-linearity Statistic Based Approach to Robust Background Modeling , 2006, ACCV.

[37]  Vasile Gui,et al.  Foreground/background segmentation with learned dictionary , 2009 .

[38]  Fatih Murat Porikli,et al.  Detection of temporarily static regions by processing video at different frame rates , 2007, 2007 IEEE Conference on Advanced Video and Signal Based Surveillance.

[39]  Roland Miezianko,et al.  Dictionary learning for robust background modeling , 2011, 2011 IEEE International Conference on Robotics and Automation.

[40]  John C. S. Lui,et al.  Online Robust Subspace Tracking from Partial Information , 2011, ArXiv.

[41]  Anand Singh Jalal,et al.  A Robust Background Subtraction Approach Based on Daubechies Complex Wavelet Transform , 2011, ACC.

[42]  Xiaodong Li,et al.  Stable Principal Component Pursuit , 2010, 2010 IEEE International Symposium on Information Theory.

[43]  Wen Gao,et al.  Modeling background from compressed video , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.

[44]  Tieniu Tan,et al.  Foreground Object Detection Using Top-Down Information Based on EM Framework , 2012, IEEE Transactions on Image Processing.

[45]  Thierry Bouwmans,et al.  Foreground detection via robust low rank matrix factorization including spatial constraint with Iterative reweighted regression , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[46]  Thierry Chateau,et al.  A Benchmark Dataset for Outdoor Foreground/Background Extraction , 2012, ACCV Workshops.

[47]  Oncel Tuzel,et al.  Bayesian background modeling for foreground detection , 2005, VSSN@MM.

[48]  Yi-Ping Hung,et al.  Efficient hierarchical method for background subtraction , 2007, Pattern Recognit..

[49]  Q. M. Jonathan Wu,et al.  Real-time Video Segmentation using Student's t Mixture Model , 2012 .

[50]  Lucia Maddalena,et al.  The 3dSOBS+ algorithm for moving object detection , 2014, Comput. Vis. Image Underst..

[51]  Thierry Bouwmans,et al.  Foreground Detection via Robust Low Rank Matrix Decomposition Including Spatio-Temporal Constraint , 2012, ACCV Workshops.

[52]  Sudeep Sarkar,et al.  Background subtraction in varying illuminations using an ensemble based on an enlarged feature set , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[53]  Ying-Hong Liang,et al.  Background Pixel Clissification for Motion Segmentation using Mean Shift Algorithm , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[54]  Chunheng Wang,et al.  Multi-scale Fusion of Texture and Color for Background Modeling , 2012, 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance.

[55]  Tieniu Tan,et al.  A Probabilistic Framework Based on KDE-GMM Hybrid Model for Moving Object Segmentation in Dynamic Scenes , 2008 .

[56]  Manuele Bicego,et al.  Integrated region- and pixel-based approach to background modelling , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[57]  Fengxia Liu,et al.  Background Modeling in Compressed Sensing Scheme , 2011 .

[58]  Senem Velipasalar,et al.  Light-weight salient foreground detection for embedded smart cameras , 2010, Comput. Vis. Image Underst..

[59]  Martin Kleinsteuber,et al.  Robust PCA and subspace tracking from incomplete observations using $$\ell _0$$ℓ0-surrogates , 2012, Comput. Stat..

[60]  Quan Pan,et al.  Real-time and accurate segmentation of moving objects in dynamic scene , 2004, VSSN '04.

[61]  Xiaojun Jing,et al.  FPGA based mixture Gaussian background modeling and motion detection , 2011, 2011 Seventh International Conference on Natural Computation.

[62]  Eduardo Ros Vidal,et al.  Codebook hardware implementation on FPGA for background subtraction , 2012, Journal of Real-Time Image Processing.

[63]  James M. Rehg,et al.  GOSUS: Grassmannian Online Subspace Updates with Structured-Sparsity , 2013, 2013 IEEE International Conference on Computer Vision.

[64]  Davide De Caro,et al.  FPGA Implementation of Gaussian Mixture Model Algorithm for 47 fps Segmentation of 1080p Video , 2013, J. Electr. Comput. Eng..

[65]  Qi Tian,et al.  Statistical modeling of complex backgrounds for foreground object detection , 2004, IEEE Transactions on Image Processing.

[66]  Aswin C. Sankaranarayanan,et al.  SpaRCS: Recovering low-rank and sparse matrices from compressive measurements , 2011, NIPS.

[67]  Luigi di Stefano,et al.  A novel approach to change detection based on a coarse-to-fine strategy , 2005, IEEE International Conference on Image Processing 2005.

[68]  Lars Moland Eliassen,et al.  A Comparison of Learning Based Background Subtraction Techniques Implemented in CUDA , 2009 .

[69]  H. Foroughi,et al.  A novel fuzzy background subtraction method based on cellular automata for urban traffic applications , 2008, 2008 9th International Conference on Signal Processing.

[70]  Pojala Chiranjeevi,et al.  New Fuzzy Texture Features for Robust Detection of Moving Objects , 2012, IEEE Signal Processing Letters.

[71]  Thierry Bouwmans,et al.  Foreground Detection by Robust PCA Solved via a Linearized Alternating Direction Method , 2012, ICIAR.

[72]  Thierry Bouwmans,et al.  Foreground Detection Using the Choquet Integral , 2008, 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services.

[73]  Dimitrios Makris,et al.  A DSP-based system for the detection of vehicles parked in prohibited areas , 2007, 2007 IEEE Conference on Advanced Video and Signal Based Surveillance.

[74]  Dubravko Culibrk,et al.  Multiscale background modelling and segmentation , 2009, 2009 16th International Conference on Digital Signal Processing.

[75]  Xiaochun Cao,et al.  Motion saliency detection using low-rank and sparse decomposition , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[76]  Tao Tao,et al.  Iterative Grassmannian optimization for robust image alignment , 2013, Image Vis. Comput..

[77]  Hoai Bac Le,et al.  GPU Implementation of Extended Gaussian Mixture Model for Background Subtraction , 2010, 2010 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future (RIVF).

[78]  Thierry Bouwmans,et al.  Type-2 Fuzzy Mixture of Gaussians Model: Application to Background Modeling , 2008, ISVC.

[79]  Liming Zhang,et al.  An Incremental Linear Discriminant Analysis Using Fixed Point Method , 2006, ISNN.

[80]  Ying Ding,et al.  Robust moving object detection under complex background , 2010 .

[81]  Jorge Hiraiwa,et al.  An FPGA based Embedded Vision System for Real-Time Motion Segmentation , 2010 .

[82]  Chunhong Pan,et al.  Effective multi-resolution background subtraction , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[83]  Vittorio Murino,et al.  Background Subtraction for Automated Multisensor Surveillance: A Comprehensive Review , 2010, EURASIP J. Adv. Signal Process..

[84]  Horst Bischof,et al.  Why to Combine Reconstructive and Discriminative Information for Incremental Subspace Learning , 2006 .

[85]  Trevor Darrell,et al.  Background estimation and removal based on range and color , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[86]  Tieniu Tan,et al.  Modeling Complex Scenes for Accurate Moving Objects Segmentation , 2010, ACCV.

[87]  Deborah Estrin,et al.  Background Subtraction on Distributions , 2008, ECCV.

[88]  Stuart C. Schwartz,et al.  A transform domain approach to real-time foreground segmentation in video sequences , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[89]  Wonjun Kim,et al.  Background Subtraction for Dynamic Texture Scenes Using Fuzzy Color Histograms , 2012, IEEE Signal Processing Letters.

[90]  Leizer Schnitman,et al.  Highway Traffic Congestion Classification using Holistic Properties , 2013 .

[91]  Thomas S. Huang,et al.  Robust estimation of foreground in surveillance videos by sparse error estimation , 2008, 2008 19th International Conference on Pattern Recognition.

[92]  Concetto Spampinato,et al.  Adaptive Background Modeling Integrated With Luminosity Sensors and Occlusion Processing for Reliable Vehicle Detection , 2011, IEEE Transactions on Intelligent Transportation Systems.

[93]  Andrés Marino Álvarez-Meza,et al.  Video Segmentation Framework Based on Multi-kernel Representations and Feature Relevance Analysis for Object Classification , 2013, ICPRAM.

[94]  Konrad Schindler,et al.  Smooth Foreground-Background Segmentation for Video Processing , 2006, ACCV.

[95]  Dacheng Tao,et al.  GoDec: Randomized Lowrank & Sparse Matrix Decomposition in Noisy Case , 2011, ICML.

[96]  Thierry Chateau,et al.  A Benchmark Dataset for Foreground/Background Extraction , 2012, ACCV 2012.

[97]  Meng Wu,et al.  Foreground estimation based on robust linear regression model , 2011, 2011 18th IEEE International Conference on Image Processing.

[98]  Xiaogang Wang,et al.  Background Subtraction via Robust Dictionary Learning , 2011, EURASIP J. Image Video Process..

[99]  Moongu Jeon,et al.  A New Framework for Background Subtraction Using Multiple Cues , 2012, ACCV.

[100]  Eduardo Ros,et al.  Background subtraction model based on color and depth cues , 2013, Machine Vision and Applications.

[101]  Thierry Bouwmans,et al.  Foreground detection based on low-rank and block-sparse matrix decomposition , 2012, 2012 19th IEEE International Conference on Image Processing.

[102]  Dit-Yan Yeung,et al.  Bayesian Robust Matrix Factorization for Image and Video Processing , 2013, 2013 IEEE International Conference on Computer Vision.

[103]  Hai Tao,et al.  Fast Linear Discriminant Analysis Using Binary Bases , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[104]  Gongguo Tang,et al.  Robust principal component analysis based on low-rank and block-sparse matrix decomposition , 2011, 2011 45th Annual Conference on Information Sciences and Systems.

[105]  Wen Gao,et al.  Hierarchical background subtraction using local pixel clustering , 2008, 2008 19th International Conference on Pattern Recognition.

[106]  Fatih Murat Porikli,et al.  Changedetection.net: A new change detection benchmark dataset , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[107]  Qionghai Dai,et al.  Low-Rank Structure Learning via Nonconvex Heuristic Recovery , 2010, IEEE Transactions on Neural Networks and Learning Systems.

[108]  Yue Wang,et al.  Fuzzy rule-based system for dynamic texture and color based background subtraction , 2012, 2012 IEEE International Conference on Fuzzy Systems.

[109]  Eduardo Ros,et al.  Background Subtraction Based on Color and Depth Using Active Sensors , 2013, Sensors.

[110]  Saturnino Maldonado-Bascón,et al.  Background Pixel Classification for Motion Detection in Video Image Sequences , 2003, IWANN.

[111]  Wen Hu,et al.  Poster abstract: Efficient background subtraction for tracking in embedded camera networks , 2012, 2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN).

[112]  Lucia Maddalena,et al.  A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications , 2008, IEEE Transactions on Image Processing.

[113]  Minglun Gong,et al.  Realtime background subtraction from dynamic scenes , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[114]  Michael Harville,et al.  Foreground segmentation using adaptive mixture models in color and depth , 2001, Proceedings IEEE Workshop on Detection and Recognition of Events in Video.

[115]  Luis Salgado,et al.  Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers , 2014, J. Vis. Commun. Image Represent..

[116]  Xingzhi Luo,et al.  A Multiscale Parametric Background Model for Stationary Foreground Object Detection , 2007, 2007 IEEE Workshop on Motion and Video Computing (WMVC'07).

[117]  Luis Salgado,et al.  Advanced background modeling with RGB-D sensors through classifiers combination and inter-frame foreground prediction , 2014, Machine Vision and Applications.

[118]  Thierry Bouwmans,et al.  Background subtraction via incremental maximum margin criterion: a discriminative subspace approach , 2012, Machine Vision and Applications.

[119]  Josef Kittler,et al.  Incremental Linear Discriminant Analysis Using Sufficient Spanning Set Approximations , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[120]  Andrew Hunter,et al.  Accelerated hardware video object segmentation: From foreground detection to connected components labelling , 2010, Comput. Vis. Image Underst..

[121]  Alan F. Smeaton,et al.  Multispectral Object Segmentation and Retrieval in Surveillance Video , 2006, 2006 International Conference on Image Processing.

[122]  Mohand Saïd Allili,et al.  Online Video Foreground Segmentation using General Gaussian Mixture Modeling , 2007, 2007 IEEE International Conference on Signal Processing and Communications.

[123]  Reinhard Klette,et al.  Robust background subtraction and maintenance , 2004, ICPR 2004.

[124]  Jingdong Wang,et al.  A Probabilistic Approach to Robust Matrix Factorization , 2012, ECCV.

[125]  Atsushi Shimada,et al.  Hybrid Background Model Using Spatial-Temporal LBP , 2009, 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance.

[126]  Donghui Wang,et al.  Background subtraction based on nonparametric Bayesian estimation , 2011, International Conference on Digital Image Processing.

[127]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[128]  Namrata Vaswani,et al.  Real-time Robust Principal Components' Pursuit , 2010, 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[129]  Junzhou Huang,et al.  Learning with structured sparsity , 2009, ICML '09.

[130]  Dale Schuurmans,et al.  Real-Time Discriminative Background Subtraction , 2011, IEEE Transactions on Image Processing.

[131]  Yin Zhang,et al.  Adaptive foreground and shadow segmentation using hidden conditional random fields , 2007 .

[132]  John Shawe-Taylor,et al.  Canonical Correlation Analysis: An Overview with Application to Learning Methods , 2004, Neural Computation.

[133]  Mingmin Zhang,et al.  Background Modeling Fusing Local and Global Cues for Temporally Irregular Dynamic Textures , 2012 .

[134]  Deborah Estrin,et al.  Warping background subtraction , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[135]  Bohyung Han,et al.  Density-Based Multifeature Background Subtraction with Support Vector Machine , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[136]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[137]  Horst Bischof,et al.  Incremental LDA Learning by Combining Reconstructive and Discriminative Approaches , 2007, BMVC.

[138]  Yihua Tan,et al.  Accurate Dynamic Scene Model for Moving Object Detection , 2007, 2007 IEEE International Conference on Image Processing.

[139]  Wei Shen,et al.  Moving object detection in framework of compressive sampling , 2010 .

[140]  Feng Wu,et al.  Moving-object Detection Based on Sparse Representation and Dictionary Learning , 2012 .

[141]  Mubarak Shah,et al.  A hierarchical approach to robust background subtraction using color and gradient information , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[142]  Hua Li,et al.  IMMC: incremental maximum margin criterion , 2004, KDD.

[143]  Minglun Gong,et al.  Real-time foreground segmentation on GPUs using local online learning and global graph cut optimization , 2008, 2008 19th International Conference on Pattern Recognition.

[144]  Benjamin Höferlin,et al.  Evaluation of background subtraction techniques for video surveillance , 2011, CVPR 2011.

[145]  Pojala Chiranjeevi,et al.  Detection of moving objects using fuzzy correlogram based background subtraction , 2011, 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).

[146]  Soraia Raupp Musse,et al.  Background Subtraction and Shadow Detection in Grayscale Video Sequences , 2005, XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05).

[147]  Lucia Maddalena,et al.  The SOBS algorithm: What are the limits? , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[148]  Xiaowei Zhou,et al.  Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[149]  Fatimah Khalid,et al.  Using Multi-Scale Filtering to Initialize a Background Extraction Model , 2012 .

[150]  F. Porikli,et al.  Change Detection by Frequency Decomposition: Wave-Back , 2005 .

[151]  Thierry Bouwmans,et al.  Moving Object Detection via Robust Low Rank Matrix Decomposition with IRLS Scheme , 2012, ISVC.

[152]  Viktor Öwall,et al.  A Hardware Architecture for Real-Time Video Segmentation Utilizing Memory Reduction Techniques , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[153]  Jwu-Sheng Hu,et al.  Robust Background Subtraction with Shadow and Highlight Removal for Indoor Surveillance , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[154]  Cewu Lu,et al.  Online Robust Dictionary Learning , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[155]  Hongxun zhang,et al.  Fusing Color and Texture Features for Background Model , 2006, FSKD.

[156]  Antonio Gentile,et al.  Multimodal Mean Adaptive Backgrounding for Embedded Real-Time Video Surveillance , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[157]  Yaser Sheikh,et al.  Bayesian modeling of dynamic scenes for object detection , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[158]  Brian C. Lovell,et al.  Robust Foreground Object Segmentation via Adaptive Region-Based Background Modelling , 2010, 2010 20th International Conference on Pattern Recognition.

[159]  Jun Zhang,et al.  A Robust Technique for Background Subtraction in Traffic Video , 2009, ICONIP.

[160]  Volkan Cevher,et al.  Compressive Sensing for Background Subtraction , 2008, ECCV.

[161]  Larry S. Davis,et al.  Non-parametric Model for Background Subtraction , 2000, ECCV.

[162]  Xiaoming Yuan,et al.  Sparse and low-rank matrix decomposition via alternating direction method , 2013 .

[163]  Lucia Maddalena,et al.  Multivalued Background/Foreground Separation for Moving Object Detection , 2009, WILF.

[164]  Kentaro Toyama,et al.  Wallflower: principles and practice of background maintenance , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[165]  Yang Wang,et al.  A Dynamic Hidden Markov Random Field Model for Foreground and Shadow Segmentation , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[166]  Mário A. T. Figueiredo,et al.  Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems , 2007, IEEE Journal of Selected Topics in Signal Processing.

[167]  Atsushi Shimada,et al.  Object Detection under Varying Illumination Based on Adaptive Background Modeling Considering Spatial Locality , 2009, PSIVT.

[168]  Senem Velipasalar,et al.  Resource-Efficient Salient Foreground Detection for Embedded Smart Cameras br Tracking Feedback , 2010, 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[169]  Luisa F. Polanía,et al.  Robust tracking and anomaly detection in video surveillance sequences , 2012, Defense, Security, and Sensing.

[170]  Sham M. Kakade,et al.  Robust Matrix Decomposition With Sparse Corruptions , 2011, IEEE Transactions on Information Theory.

[171]  Rita Cucchiara,et al.  Fast Background Initialization with Recursive Hadamard Transform , 2010, 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[172]  Ankush Mittal,et al.  Real-time moving object detection algorithm on high-resolution videos using GPUs , 2012, Journal of Real-Time Image Processing.

[173]  Shireen Elhabian,et al.  Moving Object Detection in Spatial Domain using Background Removal Techniques - State-of-Art , 2008 .

[174]  Irene Y. H. Gu,et al.  Efficient Adaptive Background Subtraction Based on Multi-resolution Background Modelling and Updating , 2007, PCM.

[175]  Luca Iocchi,et al.  Background modeling in the maritime domain , 2013, Machine Vision and Applications.

[176]  Constantine Caramanis,et al.  Robust PCA via Outlier Pursuit , 2010, IEEE Transactions on Information Theory.

[177]  Horst Bischof,et al.  TRICam - An Embedded Platform for Remote Traffic Surveillance , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[178]  Chandrika Kamath,et al.  Robust Background Subtraction with Foreground Validation for Urban Traffic Video , 2005, EURASIP J. Adv. Signal Process..

[179]  B. Bhanu,et al.  Physics-Based Cooperative Sensor Fusion for Moving Object Detection , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[180]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..