基于多点对参考模型的车辆阴影消除

Vehicle Shadow Elimination Based on Multi-Point Pair Reference Model

为了解决车辆检测过程中的阴影干扰问题,以点对属性为基础,利用HSL色彩空间中前景、背景和运动阴影之间的色度、亮度属性,在给定的背景图像集中,采用离线训练的方法,于图像的全局域中建立稳定的不随环境变化的多点对参考模型.该模型充分考虑了图像全局域的颜色信息,减小了背景像素的误差,能在线消除运动阴影对车辆分割的影响,且对于复杂环境下的运动背景和光照变化具有较强的抑制作用.为减小算法的计算量,引入背景模板,减少了参与运算的像素点,提高了算法的分割效率.仿真实验表明,运用多点对参考模型消除车辆运动阴影,比其它阴影消除方法具有更强的准确性和鲁棒性.

In order to eliminate the shadow interference in a vehicle detection process,a stable multi-point pair refe-rence model based on point pair properties,which is insensitive to the environment,is established in the global domain of the image.This model takes advantage of the hue and the luminance among the foreground,the background and the moving shadow in HSL color space and adopts the offline training method in a given background image set.By fully considering the color information of the whole image,the model reduces the error of background pixels,online eliminates the impact of moving shadow on vehicle segmentation and strongly inhibits the moving background and illumination change in complex environments.Moreover,to simplify the computation of the proposed algorithm,background templates are introduced,which greatly reduces the number of operation pixels and improves the segmentation efficiency.Simulated results indicate that,as compared with the other vehicle shadow elimination me-thods,the proposed algorithm based on the multi-point pair reference model is more accurate and robust.