Spatiotemporal Background Subtraction Using Minimum Spanning Tree and Optical Flow
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Gang Wang | Mingliang Chen | Ming-Hsuan Yang | Qing Li | Qingxiong Yang | Ming-Hsuan Yang | G. Wang | Qingxiong Yang | Mingliang Chen | Qing Li
[1] Ferdinand van der Heijden,et al. Efficient adaptive density estimation per image pixel for the task of background subtraction , 2006, Pattern Recognit. Lett..
[2] Qi Tian,et al. Foreground object detection from videos containing complex background , 2003, MULTIMEDIA '03.
[3] Bohyung Han,et al. Sequential Kernel Density Approximation and Its Application to Real-Time Visual Tracking , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[4] Mohan M. Trivedi,et al. Detecting Moving Shadows : Formulation , Algorithms and Evaluation , 2001 .
[5] Alex Pentland,et al. A Bayesian Computer Vision System for Modeling Human Interactions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..
[6] L. Davis,et al. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance , 2002, Proc. IEEE.
[7] Larry S. Davis,et al. Real-time foreground-background segmentation using codebook model , 2005, Real Time Imaging.
[8] Larry S. Davis,et al. Non-parametric Model for Background Subtraction , 2000, ECCV.
[9] Gang Wang,et al. Tree Filtering: Efficient Structure-Preserving Smoothing With a Minimum Spanning Tree , 2014, IEEE Transactions on Image Processing.
[10] Takayoshi Yamashita,et al. Towards Robust Object Detection: Integrated Background Modeling Based on Spatio-temporal Features , 2009, ACCV.
[11] Ahmed M. Elgammal,et al. A Framework for Feature Selection for Background Subtraction , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[12] Marko Heikkilä,et al. A texture-based method for modeling the background and detecting moving objects , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[13] Lucia Maddalena,et al. A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications , 2008, IEEE Transactions on Image Processing.
[14] Ahmed M. Elgammal,et al. Online Moving Camera Background Subtraction , 2012, ECCV.
[15] J. Marron,et al. Edge-Preserving Smoothers for Image Processing , 1998 .
[16] Bohyung Han,et al. Adaptive Background Modeling and Subtraction: A Density-Based Approach with Multiple Features , 2009 .
[17] Hongbin Zha,et al. Computer Vision - ACCV 2009, 9th Asian Conference on Computer Vision, Xi'an, China, September 23-27, 2009, Revised Selected Papers, Part III , 2010, Asian Conference on Computer Vision.
[18] Azriel Rosenfeld,et al. Tracking Groups of People , 2000, Comput. Vis. Image Underst..
[19] Atsushi Shimada,et al. Object Detection Using Local Difference Patterns , 2010, ACCV.
[20] Kentaro Toyama,et al. Wallflower: principles and practice of background maintenance , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[21] Qingxiong Yang,et al. A non-local cost aggregation method for stereo matching , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[22] 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.
[23] M. A. Aczel,et al. THE MAXIMUM CAPACITY THROUGH A NETWORK , 2016 .
[24] Atsushi Shimada,et al. Background Modeling Based on Bidirectional Analysis , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[25] 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.
[26] 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).
[27] Mohan M. Trivedi,et al. Detecting Moving Shadows: Algorithms and Evaluation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..
[28] Fred Godtliebsen,et al. Edge-Preserving Smoothers for Image Processing: Rejoinder , 1998 .
[29] Adam Finkelstein,et al. PatchMatch: a randomized correspondence algorithm for structural image editing , 2009, SIGGRAPH 2009.
[30] Matthieu Guillaumin,et al. Segmentation Propagation in ImageNet , 2012, ECCV.
[31] Bohyung Han,et al. SEQUENTIAL KERNEL DENSITY APPROXIMATION THROUGH MODE PROPAGATION: APPLICATIONS TO BACKGROUND MODELING , 2004 .
[32] Christopher M. Bishop,et al. Non-linear Bayesian Image Modelling , 2000, ECCV.
[33] Benjamin Höferlin,et al. Evaluation of background subtraction techniques for video surveillance , 2011, CVPR 2011.
[34] Shengping Zhang,et al. Dynamic background modeling and subtraction using spatio-temporal local binary patterns , 2008, 2008 15th IEEE International Conference on Image Processing.
[35] Nigel J. B. McFarlane,et al. Segmentation and tracking of piglets in images , 1995, Machine Vision and Applications.
[36] Hailin Jin,et al. Fast Edge-Preserving PatchMatch for Large Displacement Optical Flow , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[37] Radim Sára,et al. A Weak Structure Model for Regular Pattern Recognition Applied to Facade Images , 2010, ACCV.
[38] I. Haritaoglu,et al. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance , 2002 .
[39] Bohyung Han,et al. Density-Based Multifeature Background Subtraction with Support Vector Machine , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.