Background Modeling in Video Sequences

2014 ◽  
pp. 195-205
Author(s):  
Piotr Graszka
Keyword(s):  
Background Modeling ◽  
Video Sequences

scholarly journals Background Modeling for Video Sequences by Stacked Denoising Autoencoders

2018 ◽  
pp. 341-350 ◽  
Author(s):  
Jorge García-González ◽  
Juan M. Ortiz-de-Lazcano-Lobato ◽  
Rafael M. Luque-Baena ◽  
Miguel A. Molina-Cabello ◽  
Ezequiel López-Rubio
Keyword(s):  
Background Modeling ◽  
Video Sequences

scholarly journals Optimization of the Regularization in Background and Foreground Modeling

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Si-Qi Wang ◽  
Xiang-Chu Feng
Keyword(s):  
Background Modeling ◽  
Low Rank ◽  
Data Matrix ◽  
Video Sequences ◽  
Approximation Model ◽  
Sparse Model ◽  
Detailed Explanation ◽  
Novel Model ◽  
Typical Method ◽  
Better Than

Background and foreground modeling is a typical method in the application of computer vision. The current general “low-rank + sparse” model decomposes the frames from the video sequences into low-rank background and sparse foreground. But the sparse assumption in such a model may not conform with the reality, and the model cannot directly reflect the correlation between the background and foreground either. Thus, we present a novel model to solve this problem by decomposing the arranged data matrixDinto low-rank backgroundLand moving foregroundM. Here, we only need to give the priori assumption of the background to be low-rank and let the foreground be separated from the background as much as possible. Based on this division, we use a pair of dual norms, nuclear norm and spectral norm, to regularize the foreground and background, respectively. Furthermore, we use a reweighted function instead of the normal norm so as to get a better and faster approximation model. Detailed explanation based on linear algebra about our two models will be presented in this paper. By the observation of the experimental results, we can see that our model can get better background modeling, and even simplified versions of our algorithms perform better than mainstream techniques IALM and GoDec.

scholarly journals Compressive Background Modeling for Foreground Extraction

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yong Wang ◽  
Qian Lu ◽  
Dianhong Wang ◽  
Wei Liu
Keyword(s):  
Background Subtraction ◽  
Background Modeling ◽  
Video Sequences ◽  
Computationally Efficient ◽  
Subtraction Method ◽  
Foreground Extraction ◽  
Image Frame ◽  
Computer Vision Applications ◽  
Background Image ◽  
Classical Background

Robust and efficient foreground extraction is a crucial topic in many computer vision applications. In this paper, we propose an accurate and computationally efficient background subtraction method. The key idea is to reduce the data dimensionality of image frame based on compressive sensing and in the meanwhile apply sparse representation to build the current background by a set of preceding background images. According to greedy iterative optimization, the background image and background subtracted image can be recovered by using a few compressive measurements. The proposed method is validated through multiple challenging video sequences. Experimental results demonstrate the fact that the performance of our approach is comparable to those of existing classical background subtraction techniques.

A Fuzzy System for Background Modeling in Video Sequences

2013 ◽  
pp. 184-192 ◽  
Author(s):  
Elisa Calvo-Gallego ◽  
Piedad Brox ◽  
Santiago Sánchez-Solano
Keyword(s):  
Fuzzy System ◽  
Background Modeling ◽  
Video Sequences

FOREGROUND DETECTION IN VIDEO SEQUENCES WITH PROBABILISTIC SELF-ORGANIZING MAPS

2011 ◽  
Vol 21 (03) ◽  
pp. 225-246 ◽  
Author(s):  
EZEQUIEL LÓPEZ-RUBIO ◽  
RAFAEL MARCOS LUQUE-BAENA ◽  
ENRIQUE DOMÍNGUEZ
Keyword(s):  
Vision System ◽  
Statistical Correlation ◽  
Background Modeling ◽  
Video Sequences ◽  
Foreground Detection ◽  
Computer Vision System ◽  
Correlation Measure ◽  
Self Organizing Maps ◽  
Background Models ◽  
Better Than

Background modeling and foreground detection are key parts of any computer vision system. These problems have been addressed in literature with several probabilistic approaches based on mixture models. Here we propose a new kind of probabilistic background models which is based on probabilistic self-organising maps. This way, the background pixels are modeled with more flexibility. On the other hand, a statistical correlation measure is used to test the similarity among nearby pixels, so as to enhance the detection performance by providing a feedback to the process. Several well known benchmark videos have been used to assess the relative performance of our proposal with respect to traditional neural and non neural based methods, with favourable results, both qualitatively and quantitatively. A statistical analysis of the differences among methods demonstrates that our method is significantly better than its competitors. This way, a strong alternative to classical methods is presented.

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