一种基于主成分分析算法的车辆阴影消除算法

摘要

针对车辆检测中的阴影问题,提出了一种基于主成分分析算法(principal component analysis,PCA)的交通视频车辆阴影消除算法.该算法通过引入PCA算法确定阴影区域的方位,将算法的计算复杂度降低了3/4,提高了检测效率.基于亮度与纹理的分布特征,提出了一种IT(inverse transformation)模型将阴影区域转化为与其相应背景相似的分布以弱化阴影,从而使阴影像素在背景差法中被检测为背景像素与运动车辆分离.实验表明,本文算法阴影检测与分辨率高,阴影消除效果好,显著提高了检测的准确性.%To the shadowing problem in vehicle detection,a shadow elimination algorithm for vehicles in traffic video is proposed using the principal component analysis(PCA).The orientation of the shadow area is determined by PCA for the algorithm to greatly reduce the complexity of the shadow detection algorithm and improve the detec-tion efficiency.Based on the distribution characteristics of brightness and texture, a IT(inverse transformation) model is proposed to transform the shaded area into a distribution similar to it's background to weaken the shadow, so that the shadow pixels are detected as background pixels separated from the moving vehicle in the background difference method.Experiments show that the proposed algorithm has high shadow detection and resolution, good shadow elimination and improves significantly the accuracy of detection.Experiment results show that the algorithm have highly shadow detection rate and resolution rate,it can effectively eliminate the shadow,and the accuracy of detection is improved significantly.