Occlusion detecting window matching scheme for optical flow estimation with discrete optimization

Occlusion detection plays an important role in optical flow estimation and vice versa. We propose a single framework to simultaneously estimate flow and detect occlusion using novel support-weight based window matching. The proposed support-weight provides an effective clue to detect occlusion based on the assumption that the occlusion occupies relatively small portion in the window. By applying a coarse-to-fine approach we successfully address non-small occlusion problems as well. The proposed method also presents reasonable estimation for the flow for the occluded pixels. The energy model with the matching cost and flow regularization cost is optimized by an efficient discrete optimization method. Experiments demonstrate our method improves estimated flow accuracy compared to the method without occlusion detection, particularly on motion boundaries. It also yields highly competitive occlusion detection results, outperforming the previous state-of-the-art methods.