A Kalman Filter Based Background Updating Algorithm Robust to Sharp Illumination Changes

Messelodi, Stefano; Modena, Carla Maria; Segata, Nicola; Zanin, Michele

doi:10.1007/11553595_20

Cited by 63 publications

(36 citation statements)

References 12 publications

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“…The BMDM algorithm uses background modelling, and applies quad-tree decomposition to get the corresponding sparse matrix of foreground image, and finally takes use of distance model for the moving object edge detection. First we calculate the characteristic values (the sample mean and sample variance) for each pixel of the sequence of video frames, then we set and adjust the thresholds 1 T , 2 T 3…”

Section: Resultsmentioning

confidence: 99%

An Edge Detection Algorithm of Moving Object Based on Background Modeling and Active Contour Model

Wang¹,

Zhang²,

Shi³

et al. 2013

Proceedings of the 2nd International Conference on Systems Engineering and Modeling

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Abstract.A novel algorithm based on background modelling and active contour model is proposed for moving object edge detection. Firstly, it uses the background modeling to complete moving object detection, then it uses quad-tree decomposition method to contain the corresponding to the foreground image, through the data distribution density of the sparse matrix, calculates the seed points corresponding to the regions which are containing the moving object. Finally, starting from these seed points, it executes the active contour model in parallel to complete the multiple moving objects edge detection. Experimental results show that the proposed algorithm can effectively obtain the object outlines of multi-moving objects and the edge detection results are close to the judgment of the human visual, parallel contour extraction makes our algorithm has good real-time.

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Section: Resultsmentioning

confidence: 99%

An Edge Detection Algorithm of Moving Object Based on Background Modeling and Active Contour Model

Wang¹,

Zhang²,

Shi³

et al. 2013

Proceedings of the 2nd International Conference on Systems Engineering and Modeling

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show abstract

“…Several background modeling approaches have been improved and the newest survey may be found in [3]. Those background modeling approaches might be divided into the following types: Basic Background Modeling [4,5], Statistical Background Modeling [6], Fuzzy Background Modeling [2] and Background Estimation [7] .…”

Section: Issn: 0067-2904mentioning

confidence: 99%

Background modeling in video surveillance by using parallel computing

2017

ijs

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In the last years, the research of extraction the movable object from video sequence in application of computer vision become wide spread and well-known . in this paper the extraction of background model by using parallel computing is done by two steps : the first one using non-linear buffer to extraction frame from video sequence depending on the number of frame whether it is even or odd . the goal of this step is obtaining initial background when over half of training sequence contains foreground object . in the second step , The execution time of the traditional K-mean has been improved to obtain initial background through perform the kmean by using parallel computing where the time has been minimized to 50% of the conventional time of k-mean .Keywords : background subtraction ,video surveillance , k-mean , multithread . ‫ازية‬ ‫المتو‬ ‫الحوسبة‬ ‫استخدام‬ ‫طريق‬ ‫عن‬ ‫بالفيديو‬ ‫اقبة‬ ‫المر‬ ‫في‬ ‫الخلفية‬ ‫نمذجة‬

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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,

Vachon

2008

CSENG

359

127

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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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A Kalman Filter Based Background Updating Algorithm Robust to Sharp Illumination Changes

Cited by 63 publications

References 12 publications

An Edge Detection Algorithm of Moving Object Based on Background Modeling and Active Contour Model

An Edge Detection Algorithm of Moving Object Based on Background Modeling and Active Contour Model

Background modeling in video surveillance by using parallel computing

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

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