A New Background Subtraction Method Using Texture and Color Information

Yuan, Guowu; Gao, Yun; Xu, Dan; Jiang, Murong

doi:10.1007/978-3-642-25944-9_70

Cited by 3 publications

(5 citation statements)

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“…Multi Block LBP calculates a LBP value for each block instead of each pixel separately. ULBP [587] is not rotation invariant. Finally, there are many bins in the CS-LBP model.…”

Section: -Window-based Lbp (Wb-lbp)mentioning

confidence: 99%

“…1. Spectral and temporal informations such as Spatial-Color Binary Patterns (SCBP) [635], Opponent Color LBP (OCLBP) [296], Uniform LBP (ULBP) [587], Scene Adaptive LBP (SALBP) [379], Intensity LBP (iLBP) [516], eXtended Center-Symmetric [453] and Multi-Channel Scale Invariant LTP (MC-SILTP) [332]. In the following sub-sections, we brieftly describe each texture feature in each category and the reader can see how the texture features are fused with other features in Section 16.…”

Section: Texture Featuresmentioning

confidence: 99%

“…The LBP operator produces long feature set since it only adopts the first-order gradient information between center pixel and its neighbors. -Uniform Local Binary Pattern (ULBP): Yuan et al [587] proposed in a K histograms model a combination of color feature, i.e hue, and an uniform local binary pattern (ULBP) texture to be robust to shadows. The results from each features are combined with a weighted average.…”

Section: Local Binary Pattern (Lbp)mentioning

confidence: 99%

“…-BackGround Local Binary Pattern (BGLBP): Davarpanah et al [103] developed a BackGround LBP (BGLBP) which has been designed to inherit the positive properties of Direction Local Binary Pattern (D-LBP), CS-LBP [484], ULBP [587], and RI-LBP [588]. Experimentals results on the I2R dataset [306] show that BGLBP outperforms several variants of LBP such as the original LBP, ULBP and RI-LBP in presence of illumination changes and dynamic backgrounds.…”

Section: -Window-based Lbp (Wb-lbp)mentioning

confidence: 99%

“…It has been shown that the addition of texture to the color feature could improve robustness towards illumination changes and shadows as in [89]. For texture features, most of the authors used 1) the LBP [89][286] or one of its variants (ULBP [587],DLBP [555]), 2) the LTP [618][617] or one of its variants (SILTP [369][240]), or 3) statistical feature [86][151] or fuzzy statistical features [83][87]. In literature, different fusions scheme were used such as AND operator [312] [87]).…”

Section: Two Featuresmentioning

confidence: 99%

See 4 more Smart Citations

On the Role and the Importance of Features for Background Modeling and Foreground Detection

Bouwmans¹,

Silva²,

Marghes³

et al. 2016

Preprint

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Background modeling has emerged as a popular foreground detection technique for various applications in video surveillance. Background modeling methods have become increasing efficient in robustly modeling the background and hence detecting moving objects in any visual scene. Although several background subtraction and foreground detection have been proposed recently, no traditional algorithm today still seem to be able to simultaneously address all the key challenges of illumination variation, dynamic camera motion, cluttered background and occlusion. This limitation can be attributed to the lack of systematic investigation concerning the role and importance of features within background modeling and foreground detection. With the availability of a rather large set of invariant features, the challenge is in determining the best combination of features that would improve accuracy and robustness in T. Bouwmans Lab. MIA,

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“…Multi Block LBP calculates a LBP value for each block instead of each pixel separately. ULBP [587] is not rotation invariant. Finally, there are many bins in the CS-LBP model.…”

Section: -Window-based Lbp (Wb-lbp)mentioning

confidence: 99%

Section: Texture Featuresmentioning

confidence: 99%

Section: Local Binary Pattern (Lbp)mentioning

confidence: 99%

Section: -Window-based Lbp (Wb-lbp)mentioning

confidence: 99%

Section: Two Featuresmentioning

confidence: 99%

See 3 more Smart Citations

On the Role and the Importance of Features for Background Modeling and Foreground Detection

Bouwmans¹,

Silva²,

Marghes³

et al. 2016

Preprint

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show abstract

On the role and the importance of features for background modeling and foreground detection

Bouwmans

Silva

Marghes

et al. 2018

Computer Science Review

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Background Subtraction in Real Applications: Challenges, Current Models and Future Directions

Bouwmans

García-García²

2019

Preprint

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Computer vision applications based on videos often require the detection of moving objects in their first step. Background subtraction is then applied in order to separate the background and the foreground. In literature, background subtraction is surely among the most investigated field in computer vision providing a big amount of publications. Most of them concern the application of mathematical and machine learning models to be more robust to the challenges met in videos. However, the ultimate goal is that the background subtraction methods developed in research could be employed in real applications like traffic surveillance. But looking at the literature, we can remark that there is often a gap between the current methods used in real applications and the current methods in fundamental research. In addition, the videos evaluated in large-scale datasets are not exhaustive in the way that they only covered a part of the complete spectrum of the challenges met in real applications. In this context, we attempt to provide the most exhaustive survey as possible on real applications that used background subtraction in order to identify the real challenges met in practice, the current used background models and to provide future directions. Thus, challenges are investigated in terms of camera, foreground objects and environments. In addition, we identify the background models that are effectively used in these applications in order to find potential usable recent background models in terms of robustness, time and memory requirements.

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A New Background Subtraction Method Using Texture and Color Information

Cited by 3 publications

References 4 publications

On the Role and the Importance of Features for Background Modeling and Foreground Detection

On the Role and the Importance of Features for Background Modeling and Foreground Detection

On the role and the importance of features for background modeling and foreground detection

Background Subtraction in Real Applications: Challenges, Current Models and Future Directions

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