Dense optical flow field estimation using recursive LMS filtering

Abstract

In this paper, we present a novel recursive method for pixel-based motion estimation. Assuming small displacements, we use an adaptive LMS filtering scheme to match the intensity values and calculate the displacement between two adjacent video frames. Using a sliding window from the template image, the proposed algorithm employs a simple 2-D LMS filter to adapt the corresponding set of coefficients in order to match the pixel value in the reference frame. The peak value in the resulted coefficient distribution points to the displacement between the frames at each pixel position. The experiments demonstrate good results because the filtering takes advantage of the localized correlation of image data in adjacent frames, and produces refined estimates of the displacements at sub-pixel accuracy. One particular advantage is that the proposed method is flexible and well suited for the estimation of small displacements within video frames. The proposed method can be applied in several applications such as super-resolution, video stabilization and denoising of video sequences.