Real-time dynamic background segmentation based on a statistical approach

Peng, Jian-Wen; Horng, Wen-Bing

doi:10.1109/icnsc.2009.4919310

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…Stauffer and Grimson (1999) supposed that a specific pixel may have values following a Gaussian distribution. In the Gaussian distribution model, the background is modeled as a set of pixels with a Gaussian distribution of 2 and 2.5 standard deviations for each pixel (Peng and Horng, 2009). Subsequently, a Mixture of Gaussians (MoG) was introduced.…”

Section: Methodsmentioning

confidence: 99%

Using Multi-Scale Filtering to Initialize a Background Extraction Model

Davarpanah

Khalid

Nan

et al. 2012

Journal of Computer Science

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Problem statement: Probability-based methods which usually work based on the saved history of each pixel are utilized severally in extracting a background image for moving detection systems. Probability-based methods suffer from a lack of information when the system first begins to work. The model should be initialized using an alternative accurate method. Approach: The use of a nonparametric filtering to calculate the most probable value for each pixel in the initialization phase can be useful. In this study a complete system to extract an adaptable gray scale background image is presented. It is a probability-based system and especially suitable for outdoor applications. The proposed method is initialized using a multi-scale filtering method. Results: The results of the experiments certify that not only the quality of the final extracted background is about 10% more accurate in comparison to four recent re-implemented methods, but also the time consumption of the extraction are acceptable. Conclusion: Using multi-scale filtering to initialize the background model and to extract the background using a probability-based method proposes an accurate and adaptable background extraction method which is able to handle sudden and large illumination changes.

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Section: Methodsmentioning

confidence: 99%

Using Multi-Scale Filtering to Initialize a Background Extraction Model

Davarpanah

Khalid

Nan

et al. 2012

Journal of Computer Science

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“…Objects can be represented in several ways, sometimes objects are represented integrating the color and shape texture feature [1] and tracked, objects can also be represented in forms of points, that is known as scale invariant features [2], some of the tracking has been performed by estimating the background, and subtracting it from the foreground [3]. In kernel based object tracking the objects are tracked based on target representation and localization technique [4], adaptive feature selection procedure was also done in kernel based tracking [6], then, spatiogram instead of histogram was considered recently in kernel based approach [7].…”

Section: Introductionmentioning

confidence: 99%

Real time object tracking based on segmentation and distance minimization

Chakraborty

Patra

2010

2010 Annual IEEE India Conference (INDICON)

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In this paper we propose a novel algorithm for object tracking from Video images based on segmentation and Kernel based procedure. Many Kernel based object tracking algorithms have been developed during last few years. The computational complexity becomes very high in those kernel based techniques. In our proposed method the target localization problem is minimized using segmentation technique, instead of using mean shift tracking algorithm. Following segmentation technique the localization problem of target candidate gets minimized, and then comparing the target candidate with the target model by using Bhattacharya coefficient the object can easily be detected. So, the object can be tracked with less computational burden and more efficiently. The proposed algorithm is validated with an existing video sequence and another with a real time video sequence.

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“…Stauffer and Grimson (1999) supposed that a specific pixel may have values following a Gaussian distribution. In the Gaussian distribution model, the background of a scene is assumed as a set of pixels with a Gaussian distribution of between 2 and 2.5 standard deviations for each pixel (Peng and Horng, 2009). Subsequently, a Mixture of Gaussians (MoG) was introduced.…”

Section: Introductionmentioning

confidence: 99%

An Effective History-based Background Extraction System

Davarpanah¹,

Khalid²,

Golchin³

2012

Journal of Computer Science

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Problem statement: In many visions-based surveillance systems, the first step is accomplished by detecting moving objects resulted from subtraction of the current captured frame from the extracted background. So, the results of these systems mainly depend on the accuracy of the background image. Approach: In this study, a proposed background extraction system is presented to model the background using a simple method, to initialize the model, to extract the moving objects and to construct the final background. Our model saves the history of each pixel separately. It uses the saved information to extract the background using a probability-based method. It updates the history of the pixel consequently and according to the value of that pixel in the current captured image. Results: Results of the experiments certify that not only the quality of the final extracted background is the best between four recently re-implemented methods, but also the time consumption of the extraction is acceptable. Conclusion: Since History-based methods use temporal information extracted from the several previous frames, they are less sensitive to noise and sudden changes for extracting the background image.

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Real-time dynamic background segmentation based on a statistical approach

Cited by 6 publications

References 22 publications

Using Multi-Scale Filtering to Initialize a Background Extraction Model

Using Multi-Scale Filtering to Initialize a Background Extraction Model

Real time object tracking based on segmentation and distance minimization

An Effective History-based Background Extraction System

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