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首页> 外文会议>Conference on Image Processing: Algorithms and Systems III; 20040119-20040121; San Jose,CA; US >Median Model for Background Subtraction in Intelligent Transportation System
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Median Model for Background Subtraction in Intelligent Transportation System

机译：智能交通系统中背景扣除的中值模型

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摘要

This research is generally divided into two phases: the first phase deals with background image generation and vehicle detection, the second phase deals with vehicle tracking and video handoff. In the first phase we view the image as a mixture of three data distributions: vehicle, background and shadow. Thus the problem is modeled as a mixture of a Gaussian problem, and our goal is to separate the background data from other data distributions. We propose a median model and an improved median model to separate the background data from mixture data and to generate background reference images. In the median model, we keep track of deviation between the median and its neighbors in a reordered pixel sequence. When sample size is big enough, the reordered pixel sequence is in what we call a balanced-median model. This model is indicated by a very small deviation value. In this case the median of the pixel sequence falls in the background set and can be used for background estimation. When sample size is not big enough, the reordered pixel sequence is in what we call a shifted-median model. This model is indicated by a much bigger deviation value. In this case the median falls out of the background set and is excluded for background estimation.
机译：这项研究通常分为两个阶段：第一个阶段涉及背景图像生成和车辆检测，第二个阶段涉及车辆跟踪和视频切换。在第一阶段，我们将图像视为三种数据分布的混合：车辆，背景和阴影。因此，将问题建模为高斯问题的混合体，我们的目标是将背景数据与其他数据分布分开。我们提出了一种中位数模型和一种改进的中位数模型，以将背景数据与混合数据分开并生成背景参考图像。在中位数模型中，我们在重新排序的像素序列中跟踪中位数及其邻居之间的偏差。当样本量足够大时，重新排序的像素序列就是所谓的平衡中值模型。该模型由非常小的偏差值表示。在这种情况下，像素序列的中值落入背景集中，可用于背景估计。当样本量不够大时，重新排序的像素序列就是所谓的中位数偏移模型。该模型由更大的偏差值表示。在这种情况下，中位数不在背景范围之内，并被排除在背景估计之外。

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