  Structural displacement is crucial for structural health monitoring, although
it is very challenging to measure in field conditions. Most existing
displacement measurement methods are costly, labor intensive, and
insufficiently accurate for measuring small dynamic displacements. Computer
vision (CV) based methods incorporate optical devices with advanced image
processing algorithms to accurately, cost-effectively, and remotely measure
structural displacement with easy installation. However, non-target based CV
methods are still limited by insufficient feature points, incorrect feature
point detection, occlusion, and drift induced by tracking error accumulation.
This paper presents a reference frame based Deepflow algorithm integrated with
masking and signal filtering for non-target based displacement measurements.
The proposed method allows the user to select points of interest for images
with a low gradient for displacement tracking and directly calculate
displacement without drift accumulated by measurement error. The proposed
method is experimentally validated on a cantilevered beam under ambient and
occluded test conditions. The accuracy of the proposed method is compared with
that of a reference laser displacement sensor for validation. The significant
advantage of the proposed method is its flexibility in extracting structural
displacement in any region on structures that do not have distinct natural
features.
