Because the optical flow field contains both the motion information of the object and the three-dimensional structure information of the scene

optical flow calculation technology is one of the important tasks in the field of computer vision and machine vision.But

for the existing optical flow methods

there is over-smoothing problem in image boundary preserving.This paper proposes a global TV-L1(Total Variational with L1 norm

TV-L1) variational optical flow computation method based on the mutual-structure guided filtering.By extracting the mutual-structural regions of the image with higher confidence

we construct the global mutual-structure guided filtering objective function

and optimize the algorithm via combining the pyramid layering strategy with the alternating iteration scheme.This method can better preserve the image boundary information.Finally

we compare the proposed method with the existing representative variational methods LDOF(Large Displacement Optical Flow

LDOF)

CLG-TV(Combined Local-Global Total Variation

CLG-TV)

Classic++

NNF(Nearest Neighbor Fields

NNF) and deep learning method FlowNet2.0 by using standard test image datasets.The experimental results demonstrate that the presented method has more accuracy and better robustness than the other evaluated methods

especially has the significant effect of boundary preserving in the areas

and it has application prospects in moving target detection

robot obstacle avoidance

and so on.