NDLI: Background segmentation of dynamic scenes based on dual model

Content Provider	IET Digital Library
Author	Yin, Baocai Zhang, Jing Wang, Zengfu
Abstract	Detecting moving objects from background in video sequences is the first step of many image applications. The background can be divided into two types according to whether the pixel values of it are variable or not: static one and dynamic one. How to correctly detect moving foreground objects from dynamic scenes is a difficult problem because of the similarity between the moving foreground and the variable background. In this study, a new method for non-parametric background segmentation of dynamic scenes is proposed. Here the background is described by two interrelated models. One of them is called the self-model, which concerns with the recently observed pixel values at the same position, and the other one is called the neighbourhood-model, which is described by the pixel values of the neighbourhood. The author's method can accurately detect the dynamic background. To correctly detect pixels in the foreground as much as possible, the authors also propose an adaptive threshold for foreground decision based on the background characteristics. All of the above detection processes can be done in real time. Experimental results on public dataset demonstrate that the proposed method outperforms the state-of-the-art for background segmentation in dynamic scenes.
Starting Page	545
Ending Page	555
Page Count	11
ISSN	17519632
Volume Number	8
e-ISSN	17519640
Issue Number	Issue 6, Dec (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/8/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2013.0319
Journal	IET Computer Vision
Publisher Date	2014-03-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Background Segmentation Computer Vision And Image Processing Technique Dual Model Dynamic Scenes Foreground Decision Image Application Image Segmentation Image Sequence Moving Foreground Objects Moving Object Detection Neighbourhood-model Object Detection Optical, Image And Video Signal Processing Pixel Value Variable Background Video Sequence Video Signal Processing
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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