Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey

“…It aims at separating the moving objects in the scene (that constitute the foreground) from a robust model of the static environment (the background), often applied in surveillance, object detection, tracking and other applications. The traditional background subtraction [1,2,3] algorithms mainly based on RGB color data. Facing the issues of shadows, camouflage, illumination changes, traditional method can't achieve good results [4].…”

Section: Introductionmentioning

confidence: 99%

An improved mixture of Gaussians model based on color and depth data

Wu¹,

Liu²,

Liang³

2017

Proceedings of the 2017 2nd International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 20

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Abstract. In the traditional background subtraction algorithms based on color, in the special critical situation, such as face the challenges of shadows, color camouflage, illumination changes, they can't achieve good results. But depth data can solve such problems effectively, because depth sensor operating normally in the scene. We proposed an improved mixture of Gaussian model algorithm based on color and depth data in this paper, and use a new decision-making method to fuse results. In order to ensure the stability of the model, we have special treatment of the ADO area. We test our method in three datasets, the results show that proposed approach obtain good results.

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“…In the latter category, the background scene may exhibit vigorous motions. Bouwmans [4] presented a survey on statistical background modeling. The methods were classified in term of category, strategies used, and critical situation to be tackled.…”

Section: Introductionmentioning

confidence: 99%

Detection of foreground in dynamic scene via two-step background subtraction

Chan

2015

Machine Vision and Applications

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Various computer vision applications such as video surveillance and gait analysis have to perform human detection. This is usually done via background modeling and subtraction. It is a challenging problem when the image sequence captures the human activities in a dynamic scene. This paper presents a method for foreground detection via a two-step background subtraction. Background frame is first generated from the initial image frames of the image sequence and continuously updated based on the background subtraction results. The background is modeled as nonoverlapping blocks of background frame pixel colors. In the first step of background subtraction, the current image frame is compared with the background model via a similarity measure. The potential foregrounds are separated from the static background and most of the dynamic background pixels. In the second step, if a potential foreground is sufficiently large, the enclosing region is compared with the background model again to obtain a refined shape of the foreground. We compare our method with various existing background subtraction methods using image sequences containing dynamic background elements such as trees and water. We show through the quantitative measures the superiority of our method.

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Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey

Cited by 147 publications

References 273 publications

Fast convergence of regularised Region-based Mixture of Gaussians for dynamic background modelling

Fast convergence of regularised Region-based Mixture of Gaussians for dynamic background modelling

An improved mixture of Gaussians model based on color and depth data

Detection of foreground in dynamic scene via two-step background subtraction

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