Gaussian kernel-based motion measurement
This addresses the need for accurate, low-cost vision-based motion measurement in structural health monitoring, though it appears incremental as it builds on existing kernel-based approaches.
The paper tackles the problem of sub-pixel-level motion measurement for structural health monitoring by developing a Gaussian kernel-based method that tracks Gaussian kernel locations between frames, incorporating motion consistency and super-resolution constraints. Numerical and experimental validations show it consistently achieves high accuracy without requiring customized parameter tuning for different samples.
The growing demand for structural health monitoring has driven increasing interest in high-precision motion measurement, as structural information derived from extracted motions can effectively reflect the current condition of the structure. Among various motion measurement techniques, vision-based methods stand out due to their low cost, easy installation, and large-scale measurement. However, when it comes to sub-pixel-level motion measurement, current vision-based methods either lack sufficient accuracy or require extensive manual parameter tuning (e.g., pyramid layers, target pixels, and filter parameters) to reach good precision. To address this issue, we developed a novel Gaussian kernel-based motion measurement method, which can extract the motion between different frames via tracking the location of Gaussian kernels. The motion consistency, which fits practical structural conditions, and a super-resolution constraint, are introduced to increase accuracy and robustness of our method. Numerical and experimental validations show that it can consistently reach high accuracy without customized parameter setup for different test samples.