A Network for structural dense displacement based on 3D deformable mesh
  model and optical flow
Peimian Du Qicheng Guo Yanru Li
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.