Vision-based methods have shown great potential in vibration-based structural health monitoring (SHM). However, these methods are not standard practices yet, since their accuracy and robustness may be influenced by extreme environmental conditions. To this end, this paper proposed a method, named dynamic regularized total variation correlation filter (DTVCF). In DTVCF, an effective optimization problem, which contains the space and structural shape information, is defined for dynamically updating the regularization weight map. Then the regularization weight map is smoothed by a total variation (TV) optimization. These are to better track the dramatically changing or almost invisible structural shape, caused by extreme environmental conditions. Moreover, efficient subpixel image registration (ESR) is used in each tracked region of interest (ROI), over time, to achieve subpixel accuracy. The superiority of DTVCF was validated in extreme environmental conditions. DTVCF could achieve subpixel level structural displacement estimation with high accuracy. Furthermore, DTVCF could process approximately 9.00 frame/s, and 3.50 frame/s in two shaking table tests, indicating its high efficiency for SHM applications.

中文翻译：

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极端环境条件下的结构运动估计——基于动态相关滤波器的计算机视觉方法

基于视觉的方法在基于振动的结构健康监测（SHM）方面显示出了巨大的潜力。然而，这些方法还不是标准做法，因为它们的准确性和鲁棒性可能会受到极端环境条件的影响。为此，本文提出了一种动态正则化全变差相关滤波器（DTVCF）方法。在DTVCF中，定义了包含空间和结构形状信息的有效优化问题来动态更新正则化权重图。然后通过全变分（TV）优化来平滑正则化权重图。这些是为了更好地跟踪由极端环境条件引起的急剧变化或几乎看不见的结构形状。此外，随着时间的推移，在每个跟踪的感兴趣区域 (ROI) 中使用高效的子像素图像配准 (ESR)，以实现子像素精度。 DTVCF的优越性在极端环境条件下得到了验证。 DTVCF可以实现高精度的亚像素级结构位移估计。此外，DTVCF 在两次振动台测试中可以处理大约 9.00 帧/s 和 3.50 帧/s，这表明它对于 SHM 应用的效率很高。