Fuzzy integral for moving object detection

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

doi:10.1109/fuzzy.2008.4630604

Cited by 80 publications

(11 citation statements)

References 25 publications

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“…To take into account these problems of robustness and adaptation, many background modeling methods have been developed and the most recent surveys can be found in [2,7,8]. These background modeling methods can be classified in the following categories: Basic Background Modeling [9][10][11], Statistical Background Modeling [12,1,13], Fuzzy Background Modeling [14,15] and Background Estimation [16][17][18]. The models the most used are the statistical ones: The first way to represent statistically the background is to assume that the history over time of intensity values of a pixel can be modeled by a single Gaussian [9].…”

Section: Introductionmentioning

confidence: 99%

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

Baf

Bouwmans

Vachon

2008

Advances in Visual Computing

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Abstract. Background modeling is a key step of background subtraction methods used in the context of static camera. The goal is to obtain a clean background and then detect moving objects by comparing it with the current frame. Mixture of Gaussians Model [1] is the most popular technique and presents some limitations when dynamic changes occur in the scene like camera jitter, illumination changes and movement in the background. Furthermore, the MGM is initialized using a training sequence which may be noisy and/or insufficient to model correctly the background. All these critical situations generate false classification in the foreground detection mask due to the related uncertainty. To take into account this uncertainty, we propose to use a Type-2 Fuzzy Mixture of Gaussians Model. Results show the relevance of the proposed approach in presence of camera jitter, waving trees and water rippling.

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Section: Introductionmentioning

confidence: 99%

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

Baf

Bouwmans

Vachon

2008

Advances in Visual Computing

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“…The identification of fuzzy measures played an important role in facilitating the Choquet integral's ability to handle realistic problems. While the Choquet integral has been used for different fields of MCDM, such as feature selection [20], image detection [27], and prediction [28], the huge number of estimated fuzzy measures usually hinders its possible applications. Even though the concept of k-additive, usually 2-additive, fuzzy measures has been proposed to reduce the number of fuzzy measures, it is still a problem when considering really high-dimensional data.…”

Section: Discussionmentioning

confidence: 99%

Forming a Hierarchical Choquet Integral with a GA-Based Heuristic Least Square Method

Chen

Huang

2019

Mathematics

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Identifying the fuzzy measures of the Choquet integral model is an important component in resolving complicated multi-criteria decision-making (MCDM) problems. Previous papers solved the above problem by using various mathematical programming models and regression-based methods. However, when considering complicated MCDM problems (e.g., 10 criteria), the presence of too many parameters might result in unavailable or inconsistent solutions. While k-additive or p-symmetric measures are provided to reduce the number of fuzzy measures, they cannot prevent the problem of identifying the fuzzy measures in a high-dimension situation. Therefore, Sugeno and his colleagues proposed a hierarchical Choquet integral model to overcome the problem, but it required the partition information of the criteria, which usually cannot be obtained in practice. In this paper, we proposed a GA-based heuristic least mean-squares algorithm (HLMS) to construct the hierarchical Choquet integral and overcame the above problems. The genetic algorithm (GA) was used here to determine the input variables of the sub-Choquet integrals automatically, according to the objective of the mean square error (MSE), and calculated the fuzzy measures with the HLMS. Then, we summed these sub-Choquet integrals into the final Choquet integral for the purpose of regression or classification. In addition, we tested our method with four datasets and compared these results with the conventional Choquet integral, logit model, and neural network. On the basis of the results, the proposed model was competitive with respect to other models.

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“…Different methods are explored in this context. Most of them are classified as background subtraction methods [8][9][10][11][12], [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Where, the Background Modeling is the principal step in these different methods.…”

Section: Related Workmentioning

confidence: 99%

“…The running average (RA) is commonly used to update the background model [14]. Then Different color spaces have been tested to improve the insensitivity of the detection to the illumination changes.…”

Section: Related Workmentioning

confidence: 99%

A block-based background model for moving object detection

Elharrouss

Abbad

Moujahid

et al. 2017

ELCVIA

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Detecting the moving objects in a video sequence using a stationary camera is an important task for many computer vision applications. This paper proposes a background subtraction approach. As first step, the background is initialized using the block-based analysis before being updated in each incoming frame. Our background frame is generated by collecting the blocks background candidates. The block candidate selection is based on probability density function (pdf) computation. After that, we compute the absolute difference between the background frame and each frame of sequence. A noise filter is applied using the Structure/Texture decomposition in order to minimize the noise caused by background subtraction operation. The binary motion mask is formed using an adaptive threshold that was deduced from the weighted mean and variance calculation. To assure the correspondence between the current frame and the background frame, an adaptation of background model in each incoming frame is realized. After comparing results obtained from the proposed method to other existing ones, it was shown that our approach attains a higher degree of efficacy.Key Words: Motion detection, Background subtraction, Background model, Background update, Video surveillance. IntroductionMotion detection is a paramount study field of computer vision. Its purpose is to extract the moving objects at time t in a video captured using a stationary camera. The motion detection is used for many applications. Among these applications there are video surveillance, human-machine interaction, the recognition of sign language specific to robotics applications and many others.The approaches mainly used for motion detection can be classified into three categories: the time difference methods, the analysis of optical flow, and the background subtraction methods. For the first one, the time difference being the calculation of the difference between two or more consecutive images in order to extract the moving area [1], [2], but the problem in this approach is that the detected objects are incomplete and poorly presented. The second approach is the calculation of the optical flow [3], [4] which provides all information about the movement, but the real-time implementation is difficult and calculation of

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Fuzzy integral for moving object detection

Cited by 80 publications

References 25 publications

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

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

Forming a Hierarchical Choquet Integral with a GA-Based Heuristic Least Square Method

A block-based background model for moving object detection

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