Gaussian kernel-based motion measurement

TL;DR

This paper introduces a Gaussian kernel-based vision method for high-precision, sub-pixel motion measurement in structural health monitoring, achieving high accuracy without extensive manual parameter tuning.

Contribution

The novel Gaussian kernel-based approach improves motion measurement accuracy and robustness, eliminating the need for extensive parameter tuning in vision-based structural monitoring.

Findings

Consistently high accuracy in motion measurement across tests

Robustness to different structural conditions

No need for customized parameter setup

Abstract

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.