Due to the lack of professional equipment, convenient deformation measurement methods are always needed, especially in damage assessments after extreme disasters. Different measurement and judgment methods require different reference data and corresponding processing methods. Considering the flexibility of smartphones, this paper proposes a rapid and simple process of visual measurement from the first-person perspective. By using smartphones fixed on the body, images can be synchronized with the scene seen by operators. With the help of trained convolutional neural networks, the contour line of the target component in the images can be extracted directly by tracking the key points of hands, and man-machine interactive operations are realized by gesture detection without interruption. For further measurement, the error is relieved by Bezier curve fitting, and the contour is transformed into the result of a list of uniformly sampled points from a series of uniform velocity circular orbits through the Fourier series. The feasibility of deformation measurement of structural components is verified by experimental data, and further expansions of this program are discussed.

中文翻译：

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基于智能手机的第一人称视角视觉测量可行性研究

由于缺乏专业设备，总是需要方便的变形测量​​方法，特别是在极端灾害后的损伤评估中。不同的测量和判断方法需要不同的参考数据和相应的处理方法。考虑到智能手机的灵活性，本文提出了一种快速、简单的第一人称视觉测量过程。通过使用固定在身体上的智能手机，图像可以与操作员看到的场景同步。借助经过训练的卷积神经网络，通过对手部关键点的跟踪，可以直接提取图像中目标部件的轮廓线，通过手势检测实现人机交互操作，不间断。为了进一步测量，通过Bezier曲线拟合消除误差，将等高线通过傅里叶级数转化为一系列匀速圆轨道的均匀采样点列表的结果。通过实验数据验证了结构构件变形测量的可行性，并讨论了该方案的进一步扩展。