It has been a challenging task to estimate optical flow for videos in which either foreground or background exhibits remarkable motion information (i.e., large displacement), or those with insufficient resolution due to artifacts like motion blur or noise. We present a novel optical flow algorithm, which approaches the above problem as solving the task of energy minimization, which exploits image data and smoothness terms at the superpixel level. Our proposed method can be considered as an extended mean-shift algorithm, which advances color and gradient information of superpixels across consecutive frames with smoothness guarantees. Since we do not require assumptions of linearlization during optimization (as standard optical flow approaches do), we are able to alleviate local minimum problems and thus produce improved estimation results. Empirical results on the MPI-Sintel video dataset verify the effectiveness of our proposed method.
