Journal•ISSN: 1874-4796
Recent Patents on Computer Science
Bentham Science
About: Recent Patents on Computer Science is an academic journal. The journal publishes majorly in the area(s): Cloud computing & Software. It has an ISSN identifier of 1874-4796. Over the lifetime, 247 publications have been published receiving 2452 citations.
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TL;DR: The purpose of this paper is to provide a survey and an original classification of improvements of the original MOG, and to discuss relevant issues to reduce the computation time.
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.
495 citations
TL;DR: This paper surveys many existing schemes in the literature of background removal, surveying the common pre-processing algorithms used in different situations, presenting different background models, and the most commonly used ways to update such models and how they can be initialized.
Abstract: Identifying moving objects is a critical task for many computer vision applications; it provides a classification of the pixels into either foreground or background. A common approach used to achieve such classification is background removal. Even though there exist numerous of background removal algorithms in the literature, most of them follow a simple flow diagram, passing through four major steps, which are pre-processing, background modelling, foreground de- tection and data validation. In this paper, we survey many existing schemes in the literature of background removal, sur- veying the common pre-processing algorithms used in different situations, presenting different background models, and the most commonly used ways to update such models and how they can be initialized. We also survey how to measure the performance of any moving object detection algorithm, whether the ground truth data is available or not, presenting per- formance metrics commonly used in both cases.
424 citations
TL;DR: An extended and updated survey of the recent researches and patents which concern statistical background modeling to achieve a comparative evaluation and to conclude with several promising directions for future research.
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 terms of category. Then, the original methods are reminded and discussed following the challenges met in video sequences. We classified their respective improvements in terms of strategies used. Furthermore, we discussed them in terms of the critical situations they claim to handle. Finally, we conclude with several promising directions for future research. The survey also discussed relevant patents.
339 citations
TL;DR: The improvements of the PCA in terms of strategies and the variants in term of the used subspace learning algorithms are classified and a comparative evaluation of the variants is presented and they are evaluated with the state-of-art algorithms by using the Wallflower dataset.
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. and a representative patent using PCA concerns the detection of cars and persons in video surveillance. 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.
106 citations
86 citations