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Feature- and classifier-based vehicle headlight/shadow removal in video

机译:视频中基于特征和分类器的车辆前照灯/阴影去除

摘要

A method for removing false foreground image content in a foreground detection process performed on a video sequence includes, for each current frame, comparing a feature value of each current pixel against a feature value of a corresponding pixel in a background model. The each current pixel is classified as belonging to one of a candidate foreground image and a background based on the comparing. A first classification image representing the candidate foreground image is generated using the current pixels classified as belonging to the candidate foreground image. The each pixel in the first classification image is classified as belonging to one of a foreground image and a false foreground image using a previously trained classifier. A modified classification image is generated for representing the foreground image using the pixels classified as belonging to the foreground image while the pixels classified as belonging to the false foreground image are removed.
机译:一种在视频序列上执行的前景检测处理中去除假前景图像内容的方法,包括:对于每个当前帧,将每个当前像素的特征值与背景模型中对应像素的特征值进行比较。基于比较,将每个当前像素分类为属于候选前景图像和背景之一。使用被分类为属于候选前景图像的当前像素来生成表示候选前景图像的第一分类图像。使用先前训练的分类器,将第一分类图像中的每个像素分类为属于前景图像和伪前景图像之一。使用被分类为属于前景图像的像素而去除被分类为属于假前景图像的像素,生成用于表示前景图像的修改的分类图像。

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