A non-parametric pixel-based background modeling for dynamic scenes

Armanfard, Narges; Komeili, Majid; Valizade, M.; Kabir, Ehsanollah; Jalili, Saeed

doi:10.1109/actea.2009.5227894

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…It has acceptable performance in fabric defect detection, texture classification, face recognition and moving region detection. In this method, local features which are considered as neighboring pixels are assigned to the texture features (Armanfard et al, 2009a). LBP has been used by Heikkila and Pietaikinen (2006) to model the history of each pixel in any block.…”

Section: Methodsmentioning

confidence: 99%

Using Multi-Scale Filtering to Initialize a Background Extraction Model

Davarpanah

Khalid

Nan

et al. 2012

Journal of Computer Science

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Problem statement: Probability-based methods which usually work based on the saved history of each pixel are utilized severally in extracting a background image for moving detection systems. Probability-based methods suffer from a lack of information when the system first begins to work. The model should be initialized using an alternative accurate method. Approach: The use of a nonparametric filtering to calculate the most probable value for each pixel in the initialization phase can be useful. In this study a complete system to extract an adaptable gray scale background image is presented. It is a probability-based system and especially suitable for outdoor applications. The proposed method is initialized using a multi-scale filtering method. Results: The results of the experiments certify that not only the quality of the final extracted background is about 10% more accurate in comparison to four recent re-implemented methods, but also the time consumption of the extraction are acceptable. Conclusion: Using multi-scale filtering to initialize the background model and to extract the background using a probability-based method proposes an accurate and adaptable background extraction method which is able to handle sudden and large illumination changes.

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Section: Methodsmentioning

confidence: 99%

Using Multi-Scale Filtering to Initialize a Background Extraction Model

Davarpanah

Khalid

Nan

et al. 2012

Journal of Computer Science

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“…These nonparametric methods overcome most of the problems in model based method but need a huge amount of storage space to keep samples [9,10], especially there exist much periodic-like movement in the background. Kim et al presented a nonparametric background subtraction method called the codebook (CB) method by which sample background values at each pixel can be quantized into codebooks and compressed form of pixel-based background model from a long continuous image sequence is obtained for each pixel [11].…”

Section: Introductionmentioning

confidence: 99%

Fast robust foreground-background segmentation based on variable rate codebook method in Bayesian framework for detecting objects of interest

Zhao

Liu

2014

2014 7th International Congress on Image and Signal Processing

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In this paper, a reliable pixel-based foregroundbackground segmentation technique for detecting object(s) of interest (OOI) from video sequence captured by a fixed camera is proposed. OOIs, used for further tracking or positioning applications, should be detected accurately from those moving (or still) objects even under variable illumination and the corresponding background model need to update quickly. To cope with fast change of the scene, we present an adaptive variable rate codebook update algorithm based on the cache mechanism, which adjusts the time thresholds according to the number of current effective samples in codebook. Then Bayes rule is employed to make the final decision based on prebuilt OOIs' color model and the background model deduced from the codebook. The experiment results have proven the given method's effectiveness.

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“…A 3×3 neighborhood of g 0 b the pixels' intensity values c the Binary number assigned to p i starting from top-left anticlockwise. Picture from[1] …”

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confidence: 99%

A texture descriptor: BackGround Local Binary Pattern (BGLBP)

Davarpanah

Khalid

Abdullah

et al. 2015

Multimed Tools Appl

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Local Binary Pattern (LBP) is invariant to the monotonic changes in the grey scale domain. This property enables LBP to present a texture descriptor being useful in applications dealing with the local illumination changes. However, the existing versions of LBP are not able to handle image illumination changes, especially in outdoor environments. The nonpatterned illumination changes disturb performance of the background extraction methods. In this paper, an extended version of LBP which is called BackGround LBP (BGLBP) is presented. BGLBP is designed for the background extraction application but it is extendable to the other areas as a texture descriptor. BGLBP is an extension of D-LBP, Centre-Symmetric LBP, ULBP, and R-LBP and it has been designed to inherit the positive properties of previous versions. The performance of BGLBP as a part of background extraction method is investigated. In addition, a comparison between BGLBP as a general texture descriptor and a number of LBP versions is conducted.

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A non-parametric pixel-based background modeling for dynamic scenes

Cited by 8 publications

References 20 publications

Using Multi-Scale Filtering to Initialize a Background Extraction Model

Using Multi-Scale Filtering to Initialize a Background Extraction Model

Fast robust foreground-background segmentation based on variable rate codebook method in Bayesian framework for detecting objects of interest

A texture descriptor: BackGround Local Binary Pattern (BGLBP)

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