Robust optical flow estimation via edge preserving filtering

Highlights：

• Problem: It is known that optical flow estimation techniques suffer from the issues of ill-defined edges and boundaries of the moving objects. Traditional variational methods for optical flow estimation are not robust to handle these issues since the local filters in these methods do not hold the robustness near the edges.

• Our Method: In this paper, we propose a non-local total variation NLTV-L1 optical flow estimation method based on robust weighted guided filtering. Additionally, we modify some state-of-the-art variational optical flow estimation methods by the robust weighted guided filtering objective function to verify the performance on Middlebury, MPI-Sintel, and Foggy Zurich sequences.

• Results: Experimental results show that the proposed method can preserve edges and improve the accuracy of optical flow estimation compared with several state of-the-art methods.