Probabilistic Confidence Measures for Block Matching Motion Estimation

This paper addresses the problem of deriving measures that express the degree of the reliability of motion vectors estimated by a block-matching motion estimation method. We express the block matching motion estimation scheme in the probabilistic framework as a maximum-likelihood estimation scheme. Subsequently, we derive the confidence measures in terms of the a posteriori probabilities and the likelihoods of the estimated vectors. The assumptions about the type of the likelihood, that is, about the underlying conditional probability distribution of the motion compensated intensity differences (e.g., Laplacian) are derived from the objective criterion of the block-matching estimator. All parameters are estimated from data that are derived as by-product of the motion estimation scheme and our method, practically, introduces no additional computational cost. The derivation of the confidence measures is incorporated in a multiscale scheme. Experimental results are presented for image sequences with known ground-truth motion.