基于最大熵的线性车道识别与跟踪边缘提取方法研究

Wang Rong-ben, Yu Tian-hong, Jin Li-sheng, Chu Jiang-wei, Gu Bai-yuan
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引用次数: 4

摘要

为了更好地提取车道标记边缘并进行识别,提出了一种基于最大熵的车道标记边缘提取方法。该方法结合了一维和二维熵信息。同时,将图像窗口变分技术应用于车道标记边缘提取,基于双归一化可调模板获取车道标记参数。最后利用梯形AOI方法实现车道标记的实时跟踪。
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Edge extraction method study based on maximum entropy for linear lane identifying and tracking
In order to better abstract lane mark edge and identify it, this paper proposes a new edge extraction method based on maximum entropy. This method combines both one-dimension and two-dimension entropy information. Meanwhile, image window variation technology is also applied for lane mark edge extraction and lane mark parameters can be acquired based on the bi-normalized adjustable template. Finally lane mark real-time tracking is realized by applying trapezia AOI method.
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