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.

由于易于安装和位移测量的成本效益，基于计算机视觉 (CV) 的技术已广泛应用于结构健康监测 (SHM) 领域。本文介绍了基于计算机视觉的方法，通过结合随机样本一致性（RANSAC）在遮挡下进行鲁棒位移测量。所提出的方法使用 Kanade-Lucas-Tomasi (KLT) 跟踪器提取特征点进行跟踪，并通过 RANSAC 过滤这些特征点以去除噪声或遮挡的点。利用过滤后的特征点，所提出的方法结合卡尔曼滤波器来估计 KLT 提取的速度和位移的加速度。为了验证，进行了数值模拟和实验验证。在模拟中，验证了所提出的 RANSAC 滤波的性能，以从包括具有噪声或偏差的虚拟位移的位移组中提取正确的位移。在实验中，通过部分遮挡附加在结构上的跟踪目标来验证 RANSAC 滤波和加速度测量。结果表明，与参考位移传感器和加速度计相比，即使在遮挡条件下，所提出的方法也能成功地测量位移和估计加速度。