Video stabilization using SIFT-ME features and fuzzy clustering

We propose a digital video stabilization process using information that the scale-invariant feature transform (SIFT) provides for each frame. We use a fuzzy clustering scheme to separate the SIFT features representing global motion from those representing local motion. We then calculate the global orientation change and translation between the current frame and the previous frame. Each frame's translation and orientation is added to an accumulated total, and a Kalman filter is applied to estimate the desired motion. We provide experimental results from five video sequences using peak signal-to-noise ratio (PSNR) and qualitative analysis.