Despite progress made in the accuracy and robustness of optical flow in past years, the problem of over-segmentation and the blurring of image edge and motion boundary caused by the illumination change, complex texture, large displacement, and motion occlusion still remain. Recently, we developed a guided filtering scheme for flow field estimation, which is implemented as an add-on optimal operation during the coarse-to-fine optical flow computation. In this paper, we first review the research progress in optical flow computation and discuss limitations of the currently popular median filtering heuristic for a flow field optimization. We then introduce a general formulation of the guided filtering and provide the detailed illustration. Furthermore, we explore the potential of the guided filtering optimization for the flow field estimation under the coarse-to-fine computing scheme. Finally, we modify some typical and state-of-the-art optical flow methods by applying the proposed guided filtering operation to the baseline models, and test the performances of the basic and developed models through the Middlebury, MPI-Sintel, and KITTI data. The experimental results demonstrate that the guided filtering scheme is able to preserve the image edges and motion boundaries, and to improve the accuracy and robustness of optical flow estimation.
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