Vehicle Detection Using Normalized Color and Edge Map
Vehicle Detection Using Normalized Color and Edge Map
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DOI：
10.1109/tip.2007.891147
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发表时间：
2007-03
影响因子：
10.6
通讯作者：
L. Tsai;J. Hsieh;Kuo-Chin Fan
中科院分区：
文献类型：
--
作者：
L. Tsai;J. Hsieh;Kuo-Chin Fan
本文提出了一种新的车辆检测方法，从静态图像检测车辆的颜色和边缘。与传统的基于运动特征的车辆检测方法不同，该方法引入了一种新的颜色变换模型，通过寻找重要的“车辆颜色”来快速定位候选车辆。由于车辆在不同的天气和光照条件下具有不同的颜色，因此很少有人提出使用颜色来检测车辆。提出的新的颜色变换模型具有很好的能力，从背景中识别车辆像素，即使像素是在不同的照明。在找到可能的车辆候选人，三个重要的功能，包括角点，边缘图，和小波变换系数，用于构建级联多通道分类器。根据该分类器，可以执行有效的扫描以快速验证所有可能的候选者。由于颜色特征预先消除了大部分背景像素，因此扫描过程可以快速实现。实验结果表明，全局颜色特征和局部边缘特征相结合的方法在车辆检测中具有较好的效果。车辆检测平均准确率为94.9%
This paper presents a novel vehicle detection approach for detecting vehicles from static images using color and edges. Different from traditional methods, which use motion features to detect vehicles, this method introduces a new color transform model to find important "vehicle color" for quickly locating possible vehicle candidates. Since vehicles have various colors under different weather and lighting conditions, seldom works were proposed for the detection of vehicles using colors. The proposed new color transform model has excellent capabilities to identify vehicle pixels from background, even though the pixels are lighted under varying illuminations. After finding possible vehicle candidates, three important features, including corners, edge maps, and coefficients of wavelet transforms, are used for constructing a cascade multichannel classifier. According to this classifier, an effective scanning can be performed to verify all possible candidates quickly. The scanning process can be quickly achieved because most background pixels are eliminated in advance by the color feature. Experimental results show that the integration of global color features and local edge features is powerful in the detection of vehicles. The average accuracy rate of vehicle detection is 94.9%