Structure Based Optical Flow

Classical optical flow objective functions consist of a data term that enforces brightness constancy, and a spatial smoothing term that encourages smooth flow fields. The use of structural information from images has been conventionally used for designing more robust regularizers, to prevent oversmoothing motion discontinuities. In this line of work, we are looking at exploiting image structure in a more detailed manner, as compated to conventionally used gradient filters. We not only propose better regularization terms using this structural information, but also show incorporate it in the data term to improve results.