Spatial displacement tracking of vibrating structure using multiple feature points assisted binocular visual reconstruction

Abstract

Current vision-based displacement measurement methods have limitations of requiring manual target and parameter adjustment, along with significant user involvement to achieve the desired results. This paper proposes an advanced and comprehensive vision system based on ZED cameras for full-field three-dimensional (3D) vibration displacement measurement of civil structures. Compared to existing binocular systems consisting of multiple monoculars, ZED binoculars offer greater flexibility on instrument setup and data acquisition. A novel keypoint detection and matching algorithm based on deep learning is used to achieve target-free measurement, which is capable of capturing feature points with high precision. The performance of the proposed vision-based approach is evaluated through experimental studies on a six-story scaled frame structure. Statistical analysis of the comparative results in sinusoidal cases demonstrates that the displacement responses obtained from the proposed vision-based approach show consistent results in compliance with displacement sensor measurements. System identification of the target structure is achieved through a seismic case and the error analysis is also concluded in detail.