Removal shadow with background subtraction model ViBe algorithm

Chen, Feiling; Zhu, Bin; Jing, Wenlin; Yuan, Lin

doi:10.1109/imsna.2013.6743265

Cited by 4 publications

(1 citation statement)

References 18 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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