基于卡尔曼滤波自上向下的动态阴影检测与跟踪  

Top-down Dynamic Shadow Detection and Tracking Based on Kalman Filtering

作　　者：张正本 蔡鹏飞 孙挺[2] 

机构地区：[1]河南工学院计算机科学与技术系,河南新乡453000 [2]西北大学可视化研究所,西安710069

出　　处：《计算机工程》2017年第2期257-263,共7页Computer Engineering

基　　金：河南省教育厅科学技术研究重点项目(13A520221;14A520045);河南省教育科学"十二五"规划课题([2012]-JKGHAC-0116*)

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。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.

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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]