Subspace Learning for Background Modeling: A Survey

This study deals with determining the foreground region by background subtraction based on a new tensor decomposition method. With this aim, the concept of Common Matrix Approach (CMA) is utilized with a purpose of background modelling. The performance of proposed method is validated by making experiments on real videos provided by Wallflower dataset. The obtained results are compared with well-known methods based on subjective on objective evaluation measures. The obtained good results indicate that using the CMA algorithm for background modelling is a simple and effective technique in terms computational cost and implementation.As an eventual result, we have observed that the superior results are determined on complex backgrounds including dynamic objects and illumination variation in image sets.

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“…For this purpose, the visual results determined in the work of Bouwman [22] are taken as ground on in case of performance comparison.…”

Section: Subjective Resultsmentioning

confidence: 99%

A Note on Background Subtraction by Utilizing a New Tensor Approach

Işık¹,

Özkan

Doğan

2016

ijisae

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“…The survey in [10] suggests that subspace learning models are well suited for background subtraction. Some simple and useful motion detection methods just as [11] combine optical flow with Principal Component Analysis (PCA) and significantly reduce the dimension of data.…”

Section: State-of-the-art In Motion Detectionmentioning

confidence: 99%

A Biologically Inspired Vision-Based Approach for Detecting Multiple Moving Objects in Complex Outdoor Scenes

Zheng

Yang

et al. 2015

Cogn Comput

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This version is available at https://strathprints.strath.ac.uk/53032/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Abstract:In the human brain, independent components of optical flows from the medial superior temporal (MST) area are speculated for motion cognition. Inspired by this hypothesis, a novel approach combining independent component analysis (ICA) with principal component analysis (PCA) is proposed in this paper for multiple moving objects detection in complex scenes-a major real-time challenge as bad weather or dynamic background can seriously influence the results of motion detection. In the proposed approach, by taking advantage of ICA's capability of separating the statistically-independent features from signals, the ICA algorithm is initially employed to analyze the optical flows of consecutive visual image frames. As a result, the optical flows of background and foreground can be approximately separated. Since there are still many disturbances in the foreground optical flows in the complex scene, PCA is then applied to the optical flows of foreground components so that major optical flows corresponding to multiple-moving objects can be enhanced effectively and the motions resulted from the changing background and small disturbances are relatively suppressed at the same time. Comparative experimental results with existing popular motion detection methods for challenging imaging sequences demonstrate that our proposed biologically inspired vision-based approach can extract multiple-moving objects effectively in a complex scene.

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“…As developped in Bouwmans (2009), this model presents several limitations. The first limitation of this model is that the size of the foreground object must be small and don't appear in the same location during a long period in the training sequence.…”

Section: Principal Component Analysismentioning

confidence: 99%

“…Many background subtraction methods have been developed (Bouwmans et al (2010); Bouwmans et al (2008)). A recent survey (Bouwmans (2009)) shows that subspace learning models are well suited for background subtraction. Principal Component Analysis (PCA) has been used to model the background by significantly reducing the data's dimension.…”

Section: Introductionmentioning

confidence: 99%