Robust Principal Component Analysis for Background Subtraction: Systematic Evaluation and Comparative Analysis

Guyon, Charles; Bouwmans, Thierry; Zahzah, El-hadi

doi:10.5772/38267

Cited by 74 publications

(35 citation statements)

References 20 publications

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“…The motion information is also integrated so as to enhance the accuracy of the background subtraction. A review on different PCA based background subtraction techniques is well studied by Guyon et al [17]. For integrating the texture information Li and Leung [25] have considered direction of gradient as an important clue for object detection also.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

See 1 more Smart Citation

Statistical feature bag based background subtraction for local change detection

Subudhi

Ghosh

Shiu

et al. 2016

Information Sciences

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Section: Accepted Manuscriptmentioning

confidence: 99%

“…The KDE based BGS scheme [10] considers kernelized Gaussian mixture model, which is a non-parametric BGS scheme. The approaches like BRPCA [17] and DT [7] based BGS schemes utilize different feature space for their analysis.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Statistical feature bag based background subtraction for local change detection

Subudhi

Ghosh

Shiu

et al. 2016

Information Sciences

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“…5, increasing the number of control points decrease in general the prediction error as it behaves like least squares solution. In order to compare the results of the suggested technique and existing methods, we chose movies in which the technique described in [19] yield good results, and increase the number of control points such that the difference between the results will be less than 1 % for each frame. In practice, there were no movies in which more than 7 points were necessary in order to yield such results.…”

Section: Comparison To Existing Foreground Detection Techniquesmentioning

confidence: 99%

“…I therefore explain how to construct a redundant scene prediction model to overcome various model mismatch difficulties. In [19] a scheme based on robust principal component analysis (RPCA) is suggested to cope with similar difficulties. The suggested model is developed analytically and tested empirically in various settings successfully.…”

Section: Introductionmentioning

confidence: 99%

Scene appearance model based on spatial prediction

Hagege

2013

Machine Vision and Applications

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The appearance of a static scene as sensed by a camera changes considerably as a result of changes in the illumination that falls upon it. Scene appearance modeling is thus necessary for understanding which changes in the appearance of a scene are the result of illumination changes. For any camera, the appearance of the scene is a function of the illumination sources in the scene, the three-dimensional configuration of the objects in the scene and the reflectance properties of all the surfaces in the scene. A scene appearance model is described here as a function of the behavior of static illumination sources, within or beyond the scene, and arbitrary three-dimensional configurations of patches and their reflectance distributions. Based on the suggested model, a spatial prediction technique was developed to predict the appearance of the scene, given a few measurements within it. The scene appearance model and the prediction technique were developed analytically and tested empirically. Two potential applications are briefly explored.

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“…Comparing with PCA and RPCA, it should be noted that effects of outliers are suppressed in case of linear based optimization when compared with nonlinear based optimization as utilized in PCA. Inspiring from the theory of work performed by Torre and Block, some variants of RPCA [4] have been developed and utilized for subspace based background learning. With a different idea, the Independent Component Analysis has been attempted with a purpose of background modelling [5].…”

Section: Introductionmentioning

confidence: 99%

A new subspace based solution to background modelling and change detection

Işık

Özkan

Gerek

2016

ijisae

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For surveillance system, the background subtraction plays an important role for moving object detection with an algorithm embedded in the camera. Since the existence algorithms cannot satisfy the good accuracy on complex backgrounds including illumination change and dynamic objects, we have put forward the concept of Common Vector Approach (CVA) as a new idea for background modelling. Effectiveness of proposed method is presented through the experiments on popular Wallflower dataset. The obtained visual outputs are compared with well-known methods based on the subjective and objective criteria. From the overall evaluation, we can note the proposed method is not only exhibit successful foreground detection results, but also promises an effective and efficient system for background modelling.

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Robust Principal Component Analysis for Background Subtraction: Systematic Evaluation and Comparative Analysis

Cited by 74 publications

References 20 publications

Statistical feature bag based background subtraction for local change detection

Statistical feature bag based background subtraction for local change detection

Scene appearance model based on spatial prediction

A new subspace based solution to background modelling and change detection

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