Vehicle detection using normalized color and edge map
Vehicle detection using normalized color and edge map
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DOI：
10.1109/icip.2005.1530126
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发表时间：
2005-11
期刊：
影响因子：
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通讯作者：
L. Tsai;J. Hsieh;Kuo-Chin Fan
中科院分区：
文献类型：
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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，从图像中寻找重要的“车辆颜色”，从而快速定位出可能的候选车辆。由于车辆在不同的照明条件下具有不同的颜色，因此很少提出使用颜色来检测车辆的工作。本文证明了新的颜色变换模型具有极端的能力，从背景中识别车辆像素，即使他们在各种光照条件下被照亮。每个检测到的像素对应于可能的车辆候选者。然后，两个重要的功能，包括边缘图和小波变换系数用于构建一个多通道分类器来验证这个候选人。根据这个分类器，我们可以执行一个有效的扫描，从静态图像中检测出所有想要的车辆。由于颜色特征首先用于过滤掉大多数背景像素，因此这种扫描可以非常快速地实现。实验结果表明，该方法能有效地从静态图像中检测出车辆。车辆检测的平均准确率为94.5%。
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" from images for quickly locating possible vehicle candidates. Since vehicles have different colors under different lighting conditions, there were seldom works proposed for detecting vehicles using colors. This paper proves that the new color transform model has extreme abilities to identify vehicle pixels from backgrounds even though they are lighted under various illumination conditions. Each detected pixel corresponds to a possible vehicle candidate. Then, two important features including edge maps and coefficients of wavelet transform are used for constructing a multi-channel classifier to verify this candidate. According to this classifier, we can perform an effective scan to detect all desired vehicles from static images. Since the color feature is first used to filter out most background pixels, this scan can be extremely quickly achieved. Experimental results show that the integrated scheme is very powerful in detecting vehicles from static images. The average accuracy of vehicle detection is 94.5%.