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Application of a registration method based on SVD in detecting moving object of dynamic background

机译:基于SVD的登记方法在动态背景中的移动对象中的应用

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This paper proposes a method used to detect big moving object in the complicated dynamic background, which integrates the phase correlation technique including singular value decomposition and the method in which multi-frames difference images is multiplied. The phase correlation algorithm based on singular value decomposition is insensitive to noise and change of gray and contrast. Comparing with many complex phase correlation algorithm and registration algorithm in spatial domain, our method not only can effectively restrain noise, but also enhancing the registration precision, whose speed is nearly two times as quickly as original phase correlation algorithm. The fact is found by the result of experiment that the phase correlation matrix is rank one for a noise-free rigid translation model. A new phase correlation matrix is recast based on the property which can effectively restrain noise and change of gray. By estimating global moving vector of two images using phase correlation based on singular value decomposition, background is accurately matched. The matched images are processed to calculate the image differences between the first and fourth, the second and fifth, the third and sixth. After these difference images are multiplied, clear edge of moving object is obtained. Thus the accurate location of object is realized by calculating barycentre of image. At last, simulation results prove that this proposed method can overcome effectiveness well in the lighting variations and noise. It is also efficient and applicable for accurate moving object orientation in the complicated dynamic background.
机译:本文提出了一种用于检测复杂动态背景中的大移动物体的方法,其集成了包括奇异值分解的相位相关技术和多帧差异图像乘以的方法。基于奇异值分解的相位相关算法对灰色和对比度的噪声和变化不敏感。与空间域中的许多复杂相位相关算法和登记算法进行比较,我们的方法不仅可以有效地抑制噪声,而且还可以增强登记精度,其速度几乎与原始相位相关算法一起快速。通过实验结果发现了相位相关矩阵为无辐射刚性翻译模型的等级。基于可以有效抑制噪声和灰色变化的性质,新相位相关矩阵是重量的。通过基于奇异值分解的相位相关估计两个图像的全局移动矢量,背景是精确匹配的。处理匹配的图像以计算第一和第四,第二和第五,第三和第六之间的图像差异。在这些差异图像乘以之后,获得移动物体的清晰边缘。因此,通过计算图像的重心来实现对象的精确位置。最后,仿真结果证明,这种提出的方​​法可以在照明变化和噪声中克服有效性。它还具有高效且适用于复杂动态背景中的准确移动对象方向。

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