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A moving object extraction algorithm based on the modified codebook model

机译:一种基于修改的码本模型的移动对象提取算法

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The extraction of moving targets in complex background scenes is an important part of computer vision applications. The basic method to extract the interest object is to build time series models of pixels. However, these models are often complex and also consume memories. In order to reduce the computational complexity and to streamline computing time, an improved model base on the conventional codebook model is proposed in this paper. Our improved background model is constructed with codebooks consists of codewords. The codewords are encoded based on a box model corresponding to the change scopes of pixels. At the pixel modeling stage, by using blocks surrounding the pixel area to initialize the codebook, and adjusting the position of access codewords, the modeling time is reduced. Our experiments show that the improved codebook algorithm proposed has the higher robustness and accuracy than the conventional codebook algorithm.
机译:复杂背景场景中的移动目标的提取是计算机视觉应用的重要组成部分。提取利息对象的基本方法是建立时间序列的像素模型。然而,这些模型通常很复杂,也消耗了存储器。为了降低计算复杂性并简化计算时间,本文提出了传统码本模型的改进模型基础。我们改进的后台模型由码本构建,包括码字组成。代码字基于与像素的变化范围对应的盒式型号来编码。在像素建模阶段,通过使用像素区域围绕像素区域来初始化码本的块,并调整访问码字的位置,降低建模时间。我们的实验表明,提出的改进的码本算法具有比传统的码本算法更高的稳健性和准确性。

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