Structural vibration measurement is a crucial and necessary step for structural health monitoring. Recently, computer vision-based techniques have been proposed by researchers to measure structural motion remotely. However, the direct application of vision-based measurement to practical applications still faces some challenges, mainly because intrinsic camera vibration can introduce significant errors to the measurement results. In this study, a three-stage approach using an embedded inertial measurement unit is proposed to compensate for the camera movement. First, camera rotations are estimated by employing a complementary filter with an adaptive gain to fuse gyroscope measurement and accelerometer data. Next, binary robust invariant scalable key-point features are detected from the region of interest and tracked between video frames using a Kanade–Lucas–Tomasi tracker. Finally, structural acceleration is obtained by combining the information for the obtained structural features and the estimated nonstationary camera motion. The performance of the proposed approach is investigated using both a moving handheld camera and a camera mounted on the unmanned aerial vehicle in the laboratory. These results demonstrate that the proposed method can be effectively applied to measure structural vibration, without requiring stationary background features to be available in the video.

中文翻译：

---

视觉惯性结构加速度测量

结构振动测量是结构健康监测的关键和必要步骤。最近，研究人员提出了基于计算机视觉的技术来远程测量结构运动。然而，将基于视觉的测量直接应用到实际应用中仍然面临一些挑战，主要是因为相机固有的振动会给测量结果带来很大的误差。在这项研究中，提出了一种使用嵌入式惯性测量单元的三阶段方法来补偿相机移动。首先，通过使用具有自适应增益的互补滤波器来估计相机旋转，以融合陀螺仪测量和加速度计数据。下一个，从感兴趣区域检测二进制鲁棒不变的可扩展关键点特征，并使用 Kanade-Lucas-Tomasi 跟踪器在视频帧之间进行跟踪。最后，通过结合获得的结构特征信息和估计的非平稳摄像机运动来获得结构加速度。在实验室中使用移动手持相机和安装在无人机上的相机来研究所提出方法的性能。这些结果表明，所提出的方法可以有效地应用于测量结构振动，而不需要在视频中提供固定的背景特征。在实验室中使用移动手持相机和安装在无人机上的相机来研究所提出方法的性能。这些结果表明，所提出的方法可以有效地应用于测量结构振动，而不需要在视频中提供固定的背景特征。在实验室中使用移动手持相机和安装在无人机上的相机来研究所提出方法的性能。这些结果表明，所提出的方法可以有效地应用于测量结构振动，而不需要在视频中提供固定的背景特征。