运动目标传统检测方法只考虑图像的亮度或纹理等某一种特性，受特异值影响较大，对噪声比较敏感，鲁棒性也不够好，而且背景恢复精度不高。针对以上局限性，提出一种融合结构相似度(structural similar-ity，SSIM)全参考模型和鲁棒主成分分析(robust principal component analysis，RPCA)的运动目标检测方法。此方法综合考虑图像的亮度、对比度和结构三种特性，不采用传统的背景减除法，而是把图像像素点的结构相似度作为度量来实现运动对象与背景的分离。实验结果表明，此方法准确率可达0。95，且F度量较传统运动目标检测算法平均提升0。15，总体上比传统方法更具优势。
Moving target detection based on structural similarity and robust principal component analysis
The traditional detection method of moving target only considers one characteristic of the image,such as brightness or texture,which is greatly affected by outliers,sensitive to noise,poor robustness,and low precision of background recovery.In view of the above limitations,a moving target detection method based on structural similarity(SSIM)full reference model and robust principal component analysis(RPCA)is proposed.In this method,the bright-ness,contrast and structure of the image are considered comprehensively.Instead of using the traditional background subtraction method,the structure similarity of image pixels is taken as the metric to separate the moving objects from the background.The experimental results show that the accuracy of this method can reach 0.95,and the F-measure are 0.15 higher than the traditional moving target detection algorithm on average,which is more advantageous than the traditional method on the whole.
moving target detection,background recoveryrobust principal component analysisstructural simi-larity
NETL
NSTL
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