摘要： 目前多数阴影检测方法局限于半影阴影,不能很好地应对本影阴影。针对该问题,引入卡尔曼滤波,提出一种自上向下的动态阴影检测与跟踪方法。利用梯度信息获得目标的轮廓信息以改进前景分割过程,分析每个潜在阴影的纹理相似性和亮度失真的空间相似性,在数据关联框架中结合卡尔曼滤波,利用目标和阴影之间的时间一致性提高阴影检测率。在多个数据集上的实验结果表明,该方法稳定高效,与几何信息法、颜色空间差异法和多级方法相比,其平均阴影辨别率较高。

关键词: 阴影检测与跟踪, 卡尔曼滤波, 时间一致性, 前景分割, 梯度, 阴影检测

Abstract: As most shadow detection methods are limited by penumbral shadows and cannot process umbra shadow well,a top-down dynamic shadow detection and tracking method based on Kalman Filtering(KF) is proposed.Firstly,contour information of the target is obtained by gradient information,and foreground segmentation is also improved.Then,the similarity of texture and spatial similarity of brightness distortion for each potential shadow are analyzed.Finally,in the frame of data association,time consistency between target and shadow is used to increase shadow detection rate,combining with KF.Experimental results on several data sets show that the proposed method is stable and efficient.Compared with geometry information method,color space difference method and multi-level method,it has higher average shadow identification rate.

Key words: shadow detection and tracking, Kalman Filtering(KF), time consistency, foreground segmentation, gradient, shadow detection

中图分类号: 

• TP391