Extracting Roadway Background Image: Mode-Based Approach

Zheng, Jianyang; Wang, Yinhai; Nihan, N L; Hallenbeck, Mark E

doi:10.3141/1944-11

Cited by 76 publications

(54 citation statements)

References 10 publications

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“…Basic modeling uses a static image or the previous frame as a background. Some authors suggested building a background model by using the arithmetic mean of the continuous images [25], median [26] or based on the result of the histogram analysis over a period [27]. Basic modeling is relatively simple and useful for making background models but it suffers from limitations such as failure of foreground separation with the introduction or removal of objects or when the object suddenly stops.…”

Section: Background Subtractionmentioning

confidence: 99%

A hybrid framework combining background subtraction and deep neural networks for rapid person detection

et al. 2018

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Section: Background Subtractionmentioning

confidence: 99%

A hybrid framework combining background subtraction and deep neural networks for rapid person detection

et al. 2018

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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 [11], [12]. These background modeling methods can be classified in the following categories: Basic Background modeling [13], [14], Statistical Background Modeling [15], Fuzzy Background Modeling [16] and Background Estimation [17]. Other classifications can be found in term of prediction, recursion, adaptation, or modality [18].…”

Section: Introductionmentioning

confidence: 99%

High Definition Video Segmentation Techniques-A Review

Sabeenian¹,

Lavanya²

2013

IJCEE

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Abstract-Real time segmentation of moving regions in image sequences is a fundamental step in many vision systems including automated visual surveillance human-machine interface and very low bandwidth telecommunications. Background identification is a common feature in many video processing systems. One of the most important back ground identification algorithm is the Gaussian Mixture Model algorithm (GMM).On implementation of the Gaussian mixture model on FPGA results in Reduction of the processing capability of the overall system. Trainable Segmentation is adapted to improve the processing capability. After analyzing and evaluating the performance we conclude with several promising directions for future research. Indexterms-Video segmentation, back ground identification.

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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 [2,11,12]. These background modeling methods can be classified in the following categories: Basic Background Modeling [13][14][15], Statistical Background Modeling [1,16,17], Fuzzy Background Modeling [18,19,20] and Background Estimation [3,21,22]. Other classifications can be found in term of prediction [23], recursion [2], adaptation [24], or modality [25].…”

Section: Introductionmentioning

confidence: 99%

Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey

Bouwmans¹,

Baf²,

Vachon³

2010

CSENG

163

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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.

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Extracting Roadway Background Image: Mode-Based Approach

Cited by 76 publications

References 10 publications

A hybrid framework combining background subtraction and deep neural networks for rapid person detection

A hybrid framework combining background subtraction and deep neural networks for rapid person detection

High Definition Video Segmentation Techniques-A Review

Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey

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