QR Decomposition-Based Algorithm for Background Subtraction

Amintoosi, Mahmood; Farbiz, Farzam; Fathy, Mahmoud; Analoui, Morteza; Mozayani, Nasser

doi:10.1109/icassp.2007.366102

Cited by 10 publications

(15 citation statements)

References 11 publications

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“…In this section we first briefly discuss our QR decomposition algorithm for background modeling [8]. Then we propose its online version and the hybrid algorithm of QR decomposition background modeling and the MoG method.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…In [8] we presented a background modeling algorithm based on the assumption that each small block in the image would reveal the background for at least a short interval of the sequence, and this short interval must be longer than any foreground interval. This is a usual case in a road situation.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper we improve our previous algorithm [8] in two ways i) to develop an online version of the background modeling algorithm, and ii) to consider mixture of Gaussian in background modeling. These two modifications not only improve the modeling performance, but also improve the processing speed.…”

Section: Introductionmentioning

confidence: 99%

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A QR decomposition based mixture model algorithm for background modeling

Amintoosi

Farbiz

2007

2007 6th International Conference on Information, Communications &Amp; Signal Processing

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This paper presents a new algorithm for background modeling in a sequence of images, even if there are foreground objects in each frame. We develop a QR decomposition based algorithm to remove foreground pixels from the image and then we construct the background model using Mixture of Gaussian algorithm, MoG. We split the image into small blocks and construct the background blocks using R-values taken from QR decomposition which indicate the degree of significance of the decomposed parts. The simulation results show the better performance of the proposed algorithm in compare with conventional methods on modeling static background images.

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Section: The Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A QR decomposition based mixture model algorithm for background modeling

Amintoosi

Farbiz

2007

2007 6th International Conference on Information, Communications &Amp; Signal Processing

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“…Another approach proposed by Amintoosi et al [82] consists in a QRdecomposition based algorithm. To be more robust when large parts of the background are occluded by moving objects and parts of the background are never seen, Lepisk [83] proposes to use the optic flow to reason about if the background has been seen or not.…”

Section: Initialization Of the Weight The Mean And The Variancementioning

confidence: 99%

“…Auto Gain Control [67] Auto White Balance [68] Automatic Exposure Correction [69] Gradual Illumination Changes (TD) [1, 24, 38, 63, 70-73, 74, 75] Sudden Illumination Changes (LS) [24,61,63,65,67,68,70,71,[74][75][76][77][78][79][80][81] Bootstrapping during initialization (B) [59,82,83] Bootstrapping during running (B) [84 -88] Camouflage (C) [42,72,73,[88][89][90][91][92] Foreground Aperture (FA) [93] Moved background objects (MBO) [60, 63, 70, 74, 75, 80 ,85, 87, 88] Inserted background objects (IBO) [60,63,70,74,75,85,87,88] Multimodal background (MB) [ …”

Section: Critical Situations Referencesmentioning

confidence: 99%

Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey

Bouwmans¹,

Baf²,

Vachon³

2010

CSENG

162

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Abstract:Mixture of Gaussians is a widely used approach for background modeling to detect moving objects from static cameras. Numerous improvements of the original method developed by Stauffer and Grimson [1] have been proposed over the recent years and the purpose of this paper is to provide a survey and an original classification of these improvements. We also discuss relevant issues to reduce the computation time. Firstly, the original MOG are reminded and discussed following the challenges met in video sequences. Then, we categorize the different improvements found in the literature. We have classified them in term of strategies used to improve the original MOG and we have discussed them in term of the critical situations they claim to handle. After analyzing the strategies and identifying their limitations, we conclude with several promising directions for future research.

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Effective background modelling and subtraction approach for moving object detection

Liu

Yuan

et al. 2015

IET Computer Vision

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This study presents a hierarchical background modelling and subtraction approach for real‐time detection of moving objects. At the first level, a novel pixel‐wise background modelling method is proposed for coarse detection. The method can dynamically assign the optimal number of components for each pixel with the borrow–lend strategy. And a flexible learning rate which is variable and different for each component is presented to adapt to scene changes. Additionally, a new mechanism using a framework of finite state machine is introduced to maintain and update the background models. At the second level, in order to deal with sudden illumination changes, a block‐wise foreground validation approach is adopted for refined detection. The authors compare the proposed approach with state‐of‐the‐art methods and experimental results under various scenes demonstrate the robustness and effectiveness of the proposed approach.

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QR Decomposition-Based Algorithm for Background Subtraction

Cited by 10 publications

References 11 publications

A QR decomposition based mixture model algorithm for background modeling

A QR decomposition based mixture model algorithm for background modeling

Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey

Effective background modelling and subtraction approach for moving object detection

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