Robust vision-based displacement measurement and acceleration estimation using RANSAC and Kalman filter

Computer vision (CV)-based techniques have been widely used in the field of structural health monitoring (SHM) owing to ease of installation and cost-effectiveness for displacement measurement. This paper introduces computer vision based method for robust displacement measurement under occlusion by incorporating random sample consensus (RANSAC). The proposed method uses the Kanade-Lucas-Tomasi (KLT) tracker to extract feature points for tracking, and these feature points are filtered through RANSAC to remove points that are noisy or occluded. With the filtered feature points, the proposed method incorporates Kalman filter to estimate acceleration from velocity and displacement extracted by the KLT. For validation, numerical simulation and experimental validation are conducted. In the simulation, performance of the proposed RANSAC filtering was validated to extract correct displacement out of group of displacements that includes dummy displacement with noise or bias. In the experiment, both RANSAC filtering and acceleration measurement were validated by partially occluding the target for tracking attached on the structure. The results demonstrated that the proposed method successfully measures displacement and estimates acceleration as compared to a reference displacement sensor and accelerometer, even under occluded conditions.