一种用于静态图像车辆检测的颜色变换模型

充分利用车辆的颜色特征,提出了一种新的基于Karhunen-Loeve（KL）变换的颜色变换模型。利用该模型可将所有像素颜色由RGB三维颜色空间变换到一个二维颜色空间上。在这个二维颜色空间上,车辆像素和非车辆像素分别集中在具有较好分离程度的两个区域。在此基础上,利用分类器可实现静态图像车辆的识别。实验结果表明,该颜色变换模型具有较好的车辆分割能力,准确性较高。

Color Transform Model for Vehicle Detection in Static Images

To make full use of the color feature of a vehicle, a color transform model based on Karhunen-Loeve(KL) transform is proposed. Utilizing the model, all pixels can be transformed from RGB color space to a 2-D color space. On this 2-D color space, vehicle pixels and non-vehicle pixels will respectively concentrate on detachable areas. On this basis, vehicles can be detected from static images by using a classifier. Testing results demonstrate that when the model is applied the vehicle detection can be used with high accuracy.