The vibration data are quite important for structural health monitoring (SHM). This paper proposed a novel method, to adaptively estimate video motions of the structure in subpixel accuracy, without attaching any targets. The proposed method includes three steps. In the first step, to remove outliers and simultaneously preserve feature points, the Gaussian range kernel is used along with the Gaussian spatial kernel, and calculated by the polynomial fitting and recursive integrals computing. In the second step, to calculate video pixel motions varied with spatial coordinates in the region of interest (ROI) for testing, the ROI is divided into multiple grid cells. Motions in each grid cell are modeled as local spatially-variant homography matrices, and their spatial consistency are enhanced by a shape-preserving constraint. The third step is to enhance both spatial and temporal correlations of the calculated homography matrices, achieved by the data term and the smoothness term in both space and time domains. The superiority of the proposed method over traditional methods was validated in several case studies for analyzing structural motions. Among the comparisons, the proposed method can produce image denoising, camera motions, structural motions, and structural modal information in subpixel accuracy, and with the best accuracy.

振动数据对于结构健康监测 (SHM) 非常重要。本文提出了一种新方法，以亚像素精度自适应地估计结构的视频运动，而无需附加任何目标。所提出的方法包括三个步骤。在第一步中，为了去除异常值并同时保留特征点，将高斯范围核与高斯空间核一起使用，并通过多项式拟合和递归积分计算来计算。在第二步中，为了计算感兴趣区域（ROI）中随空间坐标变化的视频像素运动以进行测试，将ROI划分为多个网格单元。每个网格单元中的运动都被建模为局部空间变化的单应矩阵，并且它们的空间一致性通过保形约束得到增强。第三步是增强计算的单应性矩阵的空间和时间相关性，通过空间和时间域中的数据项和平滑度项实现。在几个分析结构运动的案例研究中验证了所提出的方法优于传统方法的优越性。在比较中，所提出的方法可以产生亚像素精度的图像去噪、相机运动、结构运动和结构模态信息，并且具有最好的精度。