This study proposes a Network to recognize displacement of a RC frame
structure from a video by a monocular camera. The proposed Network consists of
two modules which is FlowNet2 and POFRN-Net. FlowNet2 is used to generate dense
optical flow as well as POFRN-Net is to extract pose parameter H. FlowNet2
convert two video frames into dense optical flow. POFRN-Net is inputted dense
optical flow from FlowNet2 to output the pose parameter H. The displacement of
any points of structure can be calculated from parameter H. The Fast Fourier
Transform (FFT) is applied to obtain frequency domain signal from corresponding
displacement signal. Furthermore, the comparison of the truth displacement on
the First floor of the First video is shown in this study. Finally, the
predicted displacements on four floors of RC frame structure of given three
videos are exhibited in the last of this study.

本研究提出了一个网络，通过单目摄像头识别 RC 框架结构的位移。该网络由 FlowNet2 和 POFRN-Net 两个模块组成，FlowNet2 用于生成稠密光流，POFRN-Net 用于提取姿态参数 H。该研究展示了对第一视频的真实位移进行的对比，并展示了给定三个视频中 RC 框架结构的四个楼层的预测位移。