Type-2 Fuzzy Mixture of Gaussians Model: Application to Background Modeling

Baf, Fida El; Bouwmans, Thierry; Vachon, Bertrand

doi:10.1007/978-3-540-89639-5_74

Cited by 85 publications

(16 citation statements)

References 21 publications

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“…However, the results in [9] and [12] indicate that the pixelwise mixture of Gaussians is not effective in modeling dynamic background such as swaying trees and waving water. Eng et al [16,17] partitioned the background frame into blocks and model each block of background colors using hierarchical k-means clustering.…”

Section: Related Workmentioning

confidence: 96%

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Detection of foreground in dynamic scene via two-step background subtraction

Chan

2015

Machine Vision and Applications

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Various computer vision applications such as video surveillance and gait analysis have to perform human detection. This is usually done via background modeling and subtraction. It is a challenging problem when the image sequence captures the human activities in a dynamic scene. This paper presents a method for foreground detection via a two-step background subtraction. Background frame is first generated from the initial image frames of the image sequence and continuously updated based on the background subtraction results. The background is modeled as nonoverlapping blocks of background frame pixel colors. In the first step of background subtraction, the current image frame is compared with the background model via a similarity measure. The potential foregrounds are separated from the static background and most of the dynamic background pixels. In the second step, if a potential foreground is sufficiently large, the enclosing region is compared with the background model again to obtain a refined shape of the foreground. We compare our method with various existing background subtraction methods using image sequences containing dynamic background elements such as trees and water. We show through the quantitative measures the superiority of our method.

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Section: Related Workmentioning

confidence: 96%

“…Foreground regions were then segmented as they cannot be modeled by composition of the background layers. El Baf et al [12] proposed to model the dynamic background using a type-2 fuzzy mixture of Gaussians model to tackle critical situations such as waving trees and moving water.…”

Section: Related Workmentioning

confidence: 99%

Detection of foreground in dynamic scene via two-step background subtraction

Chan

2015

Machine Vision and Applications

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“…In this context, models of type-1 or type-2 Fuzzy Gaussian mixture [13,56,57] allow to take into account this inaccuracy.…”

Section: Fuzzy Approachesmentioning

confidence: 99%

“…Bouwmans et al [13,56,57] based the class labeling on the length of the "log-likelihood" interval at the x level of the current pixel. The pixel is labeled with the label of the "best matching" Gaussian mode, ie.…”

Section: Step 3: Pixel Labelingmentioning

confidence: 99%

Background Subtraction Based on a New Fuzzy Mixture of Gaussians for Moving Object Detection

et al. 2018

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Abstract:Moving foreground detection is a very important step for many applications such as human behavior analysis for visual surveillance, model-based action recognition, road traffic monitoring, etc. Background subtraction is a very popular approach, but it is difficult to apply given that it must overcome many obstacles, such as dynamic background changes, lighting variations, occlusions, and so on. In the presented work, we focus on this problem (foreground/background segmentation), using a type-2 fuzzy modeling to manage the uncertainty of the video process and of the data. The proposed method models the state of each pixel using an imprecise and adjustable Gaussian mixture model, which is exploited by several fuzzy classifiers to ultimately estimate the pixel class for each frame. More precisely, this decision not only takes into account the history of its evolution, but also its spatial neighborhood and its possible displacements in the previous frames. Then we compare the proposed method with other close methods, including methods based on a Gaussian mixture model or on fuzzy sets. This comparison will allow us to assess our method's performance, and to propose some perspectives to this work.

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“…In the literature, many background modeling methods have been developed [13,14] to be robust to these critical situations and can be classified in the following categories: Basic Background Modeling [15][16][17], Statistical Background Modeling [12,18,19], Fuzzy Background Modeling [20,21], Background Estimation [10,11,22]. Reading the literature, one remark can be made: The statistical models offer more robustness to illumination changes and dynamic backgrounds.…”

Section: Fig (1) Background Subtraction: the Pipelinementioning

confidence: 99%

Subspace Learning for Background Modeling: A Survey

Bouwmans¹

2010

CSENG

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Background modeling is often used to detect moving object in video acquired by a fixed camera. Recently, subspace learning methods have been used to model the background in the idea to represent online data content while reducing dimension significantly. The first method using Principal Component Analysis (PCA) was proposed by Oliver et al.[1] and a representative patent using PCA concerns the detection of cars and persons in video surveillance [2]. Numerous improvements and variants were developed over the recent years. The purpose of this paper is to provide a survey and an original classification of these improvements. Firstly, we classify the improvements of the PCA in term of strategies and the variants in term of the used subspace learning algorithms. Then, we present a comparative evaluation of the variants and evaluate them with the state-of-art algorithms (SG, MOG, and KDE) by using the Wallflower dataset.

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Type-2 Fuzzy Mixture of Gaussians Model: Application to Background Modeling

Cited by 85 publications

References 21 publications

Detection of foreground in dynamic scene via two-step background subtraction

Detection of foreground in dynamic scene via two-step background subtraction

Background Subtraction Based on a New Fuzzy Mixture of Gaussians for Moving Object Detection

Subspace Learning for Background Modeling: A Survey

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