An Improved Background Subtraction Method Based on ViBe

He, Botao; Yu, Shuai

doi:10.1007/978-981-10-3002-4_30

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…It is also a non-parametric model and applicable for various scenarios. Owing to the flexibility to structure of method, many researchers developed lots of works to improve this method [39][40][41][42][43][44]. For example, Dou and Li [45] improved ViBe using graph cut optimisation, and Chu et al [46] incorporated region motion classification into ViBe to eliminate ghost regions.…”

Section: Non-parametric Methodsmentioning

confidence: 99%

Background modelling using discriminative motion representation

Zhong

et al. 2017

IET Computer Vision

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Robustness is an important factor for background modelling on various scenarios. Current pixel-based adaptive segmentation method cannot effectively tackle diverse objects simultaneously. To address this problem, in this study, a background modelling method using discriminative motion representation is proposed. Instead of simple usage of intensity to construct the background model, the proposed method extracts a new local descriptor which uses a weighted combination of differential excitations for each pixel to enhance the discriminability of pixels. On the basis of this background model, different categories of objects can be quickly identified by a simple but effective classification rule and accurately be represented in background model by a smart selection of updating strategies. Therefore, the authors' background modelling method can generate complete representation for static objects and decrease false detection caused by dynamic background or illumination variations. Extensive experiments have been conducted to demonstrate that the proposed method obtains more advantages of foreground detection than the state-of-the-art methods. In addition, the proposed method provides a computational efficient algorithm for foreground detection tasks.

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Section: Non-parametric Methodsmentioning

confidence: 99%

Background modelling using discriminative motion representation

Zhong

et al. 2017

IET Computer Vision

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EFF-ViBE: An Efficient and Improved Background Subtraction Approach based on ViBE

Fute¹,

Deffo²,

Tonyé³

2019

IJIGSP

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Background subtraction plays an important role in intelligent video surveillance since it is one of the most used tools in motion detection. If scientific progress has enabled to develop sophisticated equipment for this task, algorithms used should be improved as well. For the past decade a background subtraction technique called ViBE is gaining the field. However, the original algorithm has two main drawbacks. The first one is ghost phenomenon which appears if the initial frame contains a moving object or in the case of a sudden change in the background situations. Secondly it fails to perform well in complicated background. This paper presents an efficient background subtraction approach based on ViBE to solve these two problems. It is based on an adaptive radius to deal with complex background, on cumulative mean and pixel counting mechanism to quickly eliminate the ghost phenomenon and to adapt to sudden change in the background model.

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An Improved Background Subtraction Method Based on ViBe

Cited by 2 publications

References 27 publications

Background modelling using discriminative motion representation

Background modelling using discriminative motion representation

EFF-ViBE: An Efficient and Improved Background Subtraction Approach based on ViBE

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