Hybrid Background Subtraction in video using Bi-level CodeBook model

Varma, Soumya; Sreeraj, M

doi:10.1109/icadiwt.2014.6814674

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…Pratically, FTT processes blocks much faster in comparison with DCT. But, DCT outperforms slightly FFT in terms of precision, similarity and F-measure [513].…”

Section: Features In a Transform Domainmentioning

confidence: 90%

On the Role and the Importance of Features for Background Modeling and Foreground Detection

Bouwmans¹,

Silva²,

Marghes³

et al. 2016

Preprint

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Background modeling has emerged as a popular foreground detection technique for various applications in video surveillance. Background modeling methods have become increasing efficient in robustly modeling the background and hence detecting moving objects in any visual scene. Although several background subtraction and foreground detection have been proposed recently, no traditional algorithm today still seem to be able to simultaneously address all the key challenges of illumination variation, dynamic camera motion, cluttered background and occlusion. This limitation can be attributed to the lack of systematic investigation concerning the role and importance of features within background modeling and foreground detection. With the availability of a rather large set of invariant features, the challenge is in determining the best combination of features that would improve accuracy and robustness in T. Bouwmans Lab. MIA,

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“…Pratically, FTT processes blocks much faster in comparison with DCT. But, DCT outperforms slightly FFT in terms of precision, similarity and F-measure [513].…”

Section: Features In a Transform Domainmentioning

confidence: 90%

On the Role and the Importance of Features for Background Modeling and Foreground Detection

Bouwmans¹,

Silva²,

Marghes³

et al. 2016

Preprint

View full text Add to dashboard Cite

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“…Firstly, an online expectation maximisation algorithm was developed to update the Gaussian mixture and then, combined a spherical K-mean method to get accurate direction to update the model. Other authors, Varma and Sreeraj (2014) also proposed a hybrid method using code book to extract the foreground region of interest (ROI) from background scene. In this work, two code books, block based and pixel based are used for efficient object detection.…”

Section: Cluster Background Modelmentioning

confidence: 99%

Performance analysis of moving object detection using BGS techniques in visual surveillance

Sharma¹,

Lohan²

2019

IJSTDS

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Over the last decennium, the object detection is the pivotal step in any machine vision and image processing application. It is the initial step applied to extract most informative pixel from the video stream. Many algorithms are available in literature for extraction of visual information or foreground object from video sequence. This paper also provides a detailed overview of both conventional and traditional approaches used for detection of object. This paper explores various related methods, major challenges, applications, resources such as datasets, web-sources, etc. This paper presents a detailed overview of a moving object detection using background subtraction techniques in the video surveillance system that provide safety in cities, towns or home when video sequence is captured using IP cameras. The experimental work of this paper is performed over change detection, I2R, and wallflower datasets. The experimental work also depicts a comparative analysis of some of the peer methods. This work demonstrates several performance metrics to check robustness of the compared state-of-the-art methods.

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“…A motion based particle filter is used in [14] to ensure that people will never be absorbed into the background model deduced from original CB method. [15] combines the CB method and the MOG method to overcome extracting the false foreground pixels.…”

Section: Introductionmentioning

confidence: 99%

Fast robust foreground-background segmentation based on variable rate codebook method in Bayesian framework for detecting objects of interest

Zhao

Liu

2014

2014 7th International Congress on Image and Signal Processing

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In this paper, a reliable pixel-based foregroundbackground segmentation technique for detecting object(s) of interest (OOI) from video sequence captured by a fixed camera is proposed. OOIs, used for further tracking or positioning applications, should be detected accurately from those moving (or still) objects even under variable illumination and the corresponding background model need to update quickly. To cope with fast change of the scene, we present an adaptive variable rate codebook update algorithm based on the cache mechanism, which adjusts the time thresholds according to the number of current effective samples in codebook. Then Bayes rule is employed to make the final decision based on prebuilt OOIs' color model and the background model deduced from the codebook. The experiment results have proven the given method's effectiveness.

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Hybrid Background Subtraction in video using Bi-level CodeBook model

Cited by 5 publications

References 13 publications

On the Role and the Importance of Features for Background Modeling and Foreground Detection

On the Role and the Importance of Features for Background Modeling and Foreground Detection

Performance analysis of moving object detection using BGS techniques in visual surveillance

Fast robust foreground-background segmentation based on variable rate codebook method in Bayesian framework for detecting objects of interest

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