Abstract: Background subtraction is one of the basic low-level operations in video analysis. The aim is to separate static
information called “background” from the moving objects called “foreground”. The background needs to be
modeled and updated over time to allow robust foreground detection. Recently, reconstructive subspace
learning models, such as principal component analysis (PCA) have been used to model the background by
significantly reducing the data’s dimension. This approach is based on the assumption that the main
information contained in the training sequence is the background meaning that the foreground has a low
contribution. However, this assumption is only verified when the moving objects are either small or far away
from the camera. Furthermore, the reconstructive representations strive to be as informative as possible in
terms of well approximating the original data. Their objective is mainly to encompass the variability of the
training data and so they give more effort to model the background in an unsupervised manner than to precisely
classify pixels as foreground or background in the foreground detection. On the other hand, discriminative
methods are usually less adapted to the reconstruction of data; although they are spatially and computationally
much more efficient and often give better classification results compared with the reconstructive methods.
Based on this fact, we propose the use of a discriminative subspace learning model called incremental
maximum margin criterion (IMMC). The objective is first to enable a robust supervised initialization of the
background and secondly a robust classification of pixels as background or foreground. Furthermore, IMMC
also allows us an incremental update of the eigenvectors and eigenvalues. Experimental results on different
datasets demonstrate the performance of this proposed approach in the presence of illumination changes.
Abstract: Background modeling is currently used to detect moving objects in video acquired from static cameras. Numerous statistical methods have been developed over the recent years. The aim of this paper is firstly to provide an extended and updated survey of the recent researches and patents which concern statistical background modeling and secondly to achieve a comparative evaluation. For this, we firstly classified the statistical methods in term of category. Then, the original methods are reminded and discussed following the challenges met in video sequences. We classified their respective improvements in term of strategies used. Furthermore, we discussed them in term of the critical situations they claim to handle. Finally, we conclude with several promising directions for future research. The survey also discussed relevant patents.
Abstract: 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.
Abstract: Gaussian Mixture Models (GMMs) are the most popular techniques in background modeling but present some limitations when some dynamic changes occur like camera jitter, illumination changes, movement in the background. Furthermore, the GMM are 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. In this context, we propose to model the background by using a Type-2 Fuzzy Gaussian Mixture Models. The interest is to introduce descriptions of uncertain parameters in the GMM. Experimental validation of the proposed method is performed and presented on a diverse set of RGB and infrared videos. Results show the relevance of the proposed approach.
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.
Abstract: The analysis and understanding of video sequences is currently quite an active research field. Many applications such as video surveillance, optical motion capture or those of multimedia need to first be able to detect the objects moving in a scene filmed by a static camera. This requires the basic operation that consists of separating the moving objects called "foreground" from the static information called "background". Many background subtraction methods have been developed (Bouwmans et al. (2010); Bouwmans et al. (2008)). A recent survey (Bouwmans (2009)) shows that subspace learning models are well suited for background subtraction. Principal Component Analysis (PCA) has been used to model the background, by significantly reducing the data’s dimension. To perform PCA, different Robust Principal Components Analysis (RPCA) models have been recently developped in the literature. The background sequence is then modeled by a low rank subspace that can gradually change over time, while the moving foreground objects constitute the correlated sparse outliers. However, authors compare their algorithm only with the PCA (Oliver et al. (1999)) or another RPCA model. Furthermore, the evaluation is not made with the datasets and the measures currently used in the field of background subtraction. Considering all of this, we propose to evaluate RPCA models in the field of video-surveillance. Contributions of this chapter can be summarized as follows:
• A survey regarding robust principal component analysis
• An evaluation and comparison on different video surveillance datasets
The rest of this paper is organized as follows: In Section 2, we firstly provide the survey on robust principal component analysis. In Section 3, we evaluate and compare robust principal component analysis in order to achieve background subtraction. Finally, the conclusion is established in Section 4.
Abstract: Developing a background subtraction method, researchers must design each step and choose the features in relation to the critical situations they want to handle. All these critical situations generates imprecision and uncertainties in all the process of background subtraction. Therefore, some authors have recently introduced fuzzy concepts in the different steps of background subtraction as follows:
• Fuzzy Background Modeling: The main challenge consists in modeling multimodal background. The algorithm usually used is the Gaussian Mixture Models. The parameters are determined using a training sequence which contains insufficient or noisy data. So, the parameters are not well determined. In this context, Type-2 Fuzzy Gaussian Mixture Models are used to model uncertainties when dynamic backgrounds occurs.
• Fuzzy Foreground Detection: In this case, a saturing linear function is used to avoid crips decision in the classification of the pixels as background or foreground. The background model can be unimodal such as the running average or multi-modal such as the background modeling with confidence measure. Another approach consists in aggregating different features such as color and texture features by using the Sugeno integral or the Choquet integral. Fuzzy foreground detection is more robust to illumination changes and shadows than crisp foreground detection.
• Fuzzy Background Maintenance: The idea is to update the background following the membership of the pixel at the class background or foreground. This membership comes from the fuzzy foreground detection. This fuzzy adaptive background maintenance allows to deal robustly with illumination changes and shadows.
• Fuzzy Post-Processing: Fuzzy inference can be used between the previous and the current foreground masks to perform the detection of the moving objects as developed recently by Sivabalakrishnan and Manjula.
Abstract: Background modeling is often used in the context of moving objects detection from static cameras. Numerous methods have been developed over the recent years and the most used are the statistical ones. The purpose of this chapter is to provide a recent survey of these different statistical methods. For this, we have classified them in term of generation following the years of publication and the statistical tools used. We then focus on the first generation methods: Single Gaussian, Mixture of Gaussians, Kernel Density Estimation and Subspace Learning using PCA. These original methods are reminded and then we have classified their different improvements in term of strategies. After analyzing the strategies and identifying their limitations, we conclude with several promising directions for future research.
Abstract: Robust Principal Components Analysis (RPCA) shows a nice framework to separate moving objects from the background. The background sequence is then modeled by a low rank subspace that can gradually change over time, while the moving foreground objects constitute the correlated sparse outliers. RPCA problem can be exactly solved via convex optimization that minimizes a combination of the nuclear norm and the l1-norm. To solve this convex program, an Alternating Direction Method (ADM) is commonly used. However, the subproblems in ADM are easily solvable only when the linear mappings in the constraints are identities. This assumption is rarely verified in real application such as foreground detection. In this paper, we propose to use a Linearized Alternating Direction Method (LADM) with adaptive penalty to achieve RPCA for foreground detection. LADM alleviates the constraints of the original ADM with a faster convergence speed. Experimental results on different datasets show the pertinence of the proposed approach.
Abstract: Foreground detection is the first step in video surveillance system to detect moving objects. Principal Components Analysis (PCA) shows a nice framework to separate moving objects from the background but without a mechanism of robust analysis, the moving objects may be absorbed into the background model. This drawback can be solved by recent researches on Robust Principal Component Analysis (RPCA). The background sequence is then modeled by a low rank subspace that can gradually change over time, while the moving foreground objects constitute the correlated sparse outliers. In this paper, we propose to use a RPCA method based on low-rank and block-sparse matrix decomposition to achieve foreground detection. This decomposition enforces the lowrankness of the background and the block-sparsity aspect of the foreground. Experimental results on different datasets show the pertinence of the proposed approach.
Abstract: Subspace learning methods are widely used in background modeling to tackle illumination changes. Their main advantage is that it doesn't need to label data during the training and running phase. Recently, White et al. have shown that a supervised approach can improved significantly the robustness in background modeling. Following
this idea, we propose to model the background via a supervised subspace learning called Incremental Maximum Margin Criterion (IMMC).The proposed scheme enables to initialize robustly the background and to update incrementally the eigenvectors and eigenvalues. Experimental results made on the Wall°ower datasets show the pertinence of the proposed approach.
Abstract: Unsupervised subspace learning methods are widely used in background modeling to be robust to illuminaion changes. Their main advantage is that it doesn’t need to label data during the training and running phase. Recently, White et al. have shown that a supervised approach can improved significantly the robustness in background modeling. Following this idea, we propose to model the background via a supervised
subspace learning called Incremental Maximum Margin Criterion (IMMC). The proposed scheme enables to initialize robustly the background and to update incrementally the eigenvectors and eigenvalues. Experimental results made on the Wallflower datasets show the pertinence of the proposed approach.
Abstract: Mixture of Gaussians (MOG) is the most popular technique for background modeling and presents some limitations when dynamic changes occur in the scene like camera jitter and movement in the background. Furthermore, the MOG is initialized using a training sequence which may benoisy 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. In this context, we present a background modeling algorithm based on Type-2 Fuzzy Mixture of Gaussians which is particularly suitable for infrared videos. The use of the Type-2 Fuzzy Set Theory allows to take into account the uncertainty. The results using the OTCBVS benchmark/test dataset videos show the robustness of the proposed method in presence of dynamic backgrounds
Abstract: Foreground Detection is a key step in background subtraction
problem. This approach consists in the detection
of moving objects from static cameras through a classification
process of pixels as foreground or background.
The presence of some critical situations i.e noise, illumination
changes and structural background changes produces
an uncertainty in the classification of image pixels
which can generate false detections. In this context, we
propose a fuzzy approach using the Choquet integral to
avoid the uncertainty in the classification. The experiments
on different video datasets have been realized by testing
different color space and by fusing color and texture features.
The proposed method is characterized through robustness
against illumination changes, shadows and little
background changes, and it is validated with the experimental
results.
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 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.
Abstract: Detection of moving objects is the first step in
many applications using video sequences like video-surveillance,
optical motion capture and multimedia application. The process
mainly used is the background subtraction which one key step
is the foreground detection. The goal is to classify pixels of
the current image as foreground or background. Some critical
situations as shadows, illumination variations can occur in the
scene and generate a false classification of image pixels. To deal
with the uncertainty in the classification issue, we propose to use
the Choquet integral as aggregation operator. Experiments on
different data sets in video surveillance have shown a robustness
of the proposed method against some critical situations when
fusing color and texture features. Different color spaces have
been tested to improve the insensitivity of the detection to the
illumination changes. Then, the algorithm has been compared
with another fuzzy approach based on the Sugeno integral and
has proved its robustness.
Abstract: We present a foreground detection algorithm based on
a fuzzy integral that is particularly suitable for infrared
videos. The proposed detection of moving objects is based
on fusing intensity and textures using fuzzy integral. The
detection results are then used to update the background in
a fuzzy way. This method allows to robustly detect moving
object in presence of cloudy and rainy conditions. Our
theoretical and experimental results show that the proposed
method gives similar results than the KaewTraKulPong and
Bowden approach based on Mixture Of Gaussians (MOG)
with less memory requirement and time consuming. The
results using the OTCBVS benchmark/test dataset videos
show the robustness of the proposed method
Abstract: Background Subtraction is a widely used approach to detect
moving objects from static cameras. Many different methods
have been proposed over the recent years and can be classified
following different mathematical model: determinist model,
statistical model or filter model. The presence of critical situations
i.e. noise, illumination changes and structural background
changes introduce two main problems: The first one is
the uncertainty in the classification of the pixel in foreground
and background. The second one is the imprecision in the localization
of the moving object. In this context, we propose a
fuzzy approach for background subtraction. For this, we use
the Choquet integral in the foreground detection and propose
fuzzy adaptive background maintenance. Results show the
pertinence of our approach.
