Dual‐reference approach for vision‐based structural displacement measurement using time‐varying Kalman filter

Accurate displacement measurement is essential for structural health monitoring (SHM) to ensure infrastructure safety. Most previous vision-based displacement measurement methods either rely on static reference frames or lack dynamic error feedback, leading to performance degradation under real-world conditions. To address these challenges, this study proposes the dual-reference Kanade–Lucas–Tomasi (DR-KLT) method, which improves vision-based displacement measurements by dynamically integrating both the initial reference frame and the previous reference frame in the KLT tracker. The proposed method estimates the reliability of tracking by analyzing performance indicators such as corner tendency, bi-directional error, number of feature points, and optical flow magnitude. These estimates are incorporated into a time-varying Kalman filter for accurate displacement estimation. Validation through simulations, lab-scale, and on-site experiments demonstrate the method's robustness and superior accuracy compared to single-reference approaches. The results confirm that the DR-KLT approach effectively mitigates the limitations of conventional KLT-based tracking under unstable conditions such as occlusion or lighting variation, making it a reliable tool for real-world SHM applications.