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 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: 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.
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: 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.
