Vision-based automatic structural motion estimation in presence of 3D camera motion effects

Vision-based structural motion estimation methods show great potential in structural health monitoring (SHM). However, available methods cannot automatically estimate and eliminate three-dimensional (3D) camera motion effects caused by environmental factors from the video containing structural motions. This limits further applications of vision-based methods in SHM. To this end, this paper proposed a target-free video measurement method, which is formulated as a feature point congealing problem, and its solution includes two steps. First, the links of feature points from stationary backgrounds are assumed to have consistent and smooth motions, and are detected by a maximum a posteriori (MAP) estimation of the Bayesian model. Second, based on the detected links, the joint homography matrix containing only 3D camera motion effects can be updated iteratively, by minimizing transformation differences between the current frame and other frames. The superiority of the proposed method over traditional methods was validated in case studies of Humen bridge motion estimations. It was shown the proposed method has the best performance in video stabilization and camera motion effects estimation. Moreover, combined with the phase-based method, subpixel small structural motions can be well estimated.