基于超像素和局部颜色恒常性的自适应阴影去除

为快速有效地去除监控视频中运动目标的投射阴影，提出了一种基于超像素和阴影区域的局部颜色恒常性的自适应阴影去除算法。首先采用改进的简单线性迭代聚类算法将视频图像中的运动前景分割为互不重叠的超像素；然后计算了RGB颜色空间中背景与运动前景的亮度比率，并分析了阴影区域的局部颜色恒常性；在此基础上，以超像素为基本处理单元，计算亮度比率的标准差，并利用阴影区域标准差的特征及其分布规律提出基于拐点的自适应阈值算法检测并去除阴影。实验结果表明，该算法可以适用于多种真实场景下的阴影检测，且阴影检测率与目标识别率均超过85%；基于超像素处理可以大幅度降低算法的计算复杂度，该算法每帧平均处理时间为20 ms。该算法可以同时满足阴影去除对准确度、实时性和鲁棒性的要求。

Adaptive shadow removal based on superpixel and local color constancy

In order to remove the moving cast shadow in the surveillance video quickly and efficiently, an adaptive shadow elimination method based on superpixel and local color constancy of shaded area was proposed. First, the improved simple linear iterative clustering algorithm was used to divide the moving area in the video image into non-overlapping superpixels. Then, the luminance ratio of background and the moving foreground in the RGB color space was calculated, and the local color constancy of shaded area was analyzed. Finally, the standard deviation of the luminance ratio was computed by taking superpixel as basic processing unit, and an adaptive threshold algorithm based on turning point according to the characteristic and distribution of the standard deviation of the shadowed region was proposed to detect and remove the shadow. Experimental results show that the proposed method can process shadows in different scenarios, the shadow detection rate and discrimination rate are both more than 85%; meanwhile, the computational cost is greatly reduced by using the superpixel, and the average processing time per frame is 20 ms. The proposed algorithm can satisfy the shadow removal requirements of higher precision, real-time and robustness.