Motion estimation via hierarchical block matching and graph cut

Block matching based motion estimation algorithms are adopted in numerous practical video processing applications due to their low complexity. However, conventional block matching based methods process each block independently to minimize the energy function, which results in a local minimum. It fails to preserve the motion details. In this paper, we formulate the motion estimation as a labeling problem. The candidate labels are initialized by adopting a hierarchical block matching method. Then, we employ a graph cut algorithm to efficiently solve the global labeling problem with candidate labels. Experimental results show that the proposed approach can well preserve the motion details and outperforms all other block based motion estimation methods in terms of endpoint error and angle error on the Middleburry optical flow benchmark.