RANSAC algorithm for matching inlier correspondences in video stabilisation

Abstract

In order to stabilise jittery video, we need to find a transform which reduces the distortion between frames. To find this transformation, feature points must be identified in consecutive frames. In the existing methods, correspondences between feature points are found by considering sum of squared differences as matching cost between respective points but by this method, many of the point correspondences are obtained having limited accuracy. To rectify this problem, we proposed here, M-estimator SAmple Consensus (MSAC) algorithm which is variant of random sample consensus (RANSAC) algorithm. In our proposed method, inlier and outlier feature points are found by conventional RANSAC algorithm. Then to match these inlier feature points MSAC algorithm is used which give the robust estimate of transformation between consecutive video frames. The MSAC algorithm is repeated multiple times and at each run the cost of the end result is calculated via Sum of Absolute Differences between both image frames. Sum of absolute difference (SAD) measures the distortion between two frames by evaluating the similarity between image blocks. On the basis of SAD values, affine transform is derived. This transform gives details about the camera motion and is capable to improve the image plane. It is clear from simulation results, inliers correspondences get exactly coincident which gives more favourable results thus stabilising jittery videos.