基于超像素和支持向量机的车辆阴影检测算法
Vehicle shadow detection algorithm based on superpixel and SVM

为解决车辆阴影检测中易将车辆阴影相似的车辆区域误检测为车辆阴影的问题,提出了一种基于超像素和支持向量机的车辆阴影检测算法.首先,利用简单线性迭代聚类法将图像分割为若干超像素;然后,以超像素为基本检测单位,根据HSV空间中的一组判别条件对车辆阴影进行初步检测;在此基础上,利用支持向量机识别并去除被误检测为车辆阴影的车辆区域,进而得到最终的车辆阴影.实验结果表明,所提算法能够较好地区分车辆阴影及与车辆阴影相似的车辆区域,提高车辆阴影的检测率和分类率.
To solve the problem that the vehicle region similar to the shadow is apt to be wrongly detected as the shadow during vehicle shadow detection, a vehicle shadow detection algorithm based on superpixel and SVM(support vector machine)is proposed. First, the current image is segmented to several superpixels by the simple linear iterative clustering method. Then, according to a group of criterion conditions in HSV(hue, saturation, value)space, the vehicle shadow is detected preliminary by taking superpixels as basic testing units. Finally, the vehicle region which is detected wrongly as the vehicle shadow is recognized by SVM and removed from the preliminary detection results, thus the final vehicle shadow is obtained. The experimental results show that the proposed method can well distinguish the shadow from the vehicle region similar to the shadow, and can improve the detection rate and the discrimination rate of the vehicle shadow