Robust discriminative correlation-based full-field motion estimation of large-scale structures using a single video camera

Full-field motion, which reflects the health state of large-scale structures, is difficult to capture using traditional structural health monitoring (SHM) systems due to limited measurement points. Moreover, numerous structures lack SHM systems capable of accurately monitoring motions. Readily available videos, with their numerous pixels acting as an array of sensors, are promising in estimating full-field motion with a high resolution. However, existing vision-based motion estimation methods fail to achieve good accuracy and robustness. Accordingly, a novel motion estimation method is proposed to measure the full-field motion of large-scale structures with a single video camera. This approach adopts robust discriminative correlation to detect targets of various shapes by adaptively filtering the textures of the target and the background. The accuracy of the estimated motion reaches subpixel levels by introducing continuous convolution, which transforms discrete pixels into a continuous function. A factorized convolution operator and a Gaussian mixture model are used to compact the number of model parameters and training samples. This approach estimates the accurate displacement and high-resolution mode shape in an experimental study. Moreover, the displacement of the antenna on the high-rise Saige Building is estimated with a portable camera, yielding an error of 1.75 % compared with the laser Doppler vibrometer result. The high-resolution mode shape of the antenna is further visualized with a modal assurance criterion (MAC) value of over 0.98 compared with the simulated result. The full-field vortex-excited resonance of the long-span Humen Bridge is estimated using a surveillance video, yielding accurate mode shapes with an MAC value over 0.97 compared with the accelerometer result.