%0 Journal Article
	%A Yun-Tao Zhang and  Jong-Yeop Bae and  Whoi-Yul Kim
	%D 2016
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 109, 2016
	%T Multi-Layer Multi-Feature Background Subtraction Using Codebook Model Framework
	%U https://publications.waset.org/pdf/10003227
	%V 109
	%X Background modeling and subtraction in video
analysis has been widely used as an effective method for moving
objects detection in many computer vision applications. Recently, a
large number of approaches have been developed to tackle different
types of challenges in this field. However, the dynamic background
and illumination variations are the most frequently occurred problems
in the practical situation. This paper presents a favorable two-layer
model based on codebook algorithm incorporated with local binary
pattern (LBP) texture measure, targeted for handling dynamic
background and illumination variation problems. More specifically,
the first layer is designed by block-based codebook combining with
LBP histogram and mean value of each RGB color channel. Because
of the invariance of the LBP features with respect to monotonic
gray-scale changes, this layer can produce block wise detection results
with considerable tolerance of illumination variations. The pixel-based
codebook is employed to reinforce the precision from the output of the
first layer which is to eliminate false positives further. As a result, the
proposed approach can greatly promote the accuracy under the
circumstances of dynamic background and illumination changes.
Experimental results on several popular background subtraction
datasets demonstrate very competitive performance compared to
previous models.
	%P 23 - 27