Bayesian dense motion field estimation with landmark constraint

Abstract

In this paper, a dense motion field estimation technique based on the Bayesian framework is proposed to estimate the true dense motion fields of video sequences. Previous stochastic techniques of dense motion field estimation adopts piecewise smooth motion model and use MAP estimation to find the motion field with joint minimization of motion compensation errors and maximization of motion smoothness. However, such random process does not guarantee to converge to the true motion field. In this paper, the motion of landmark points in the video sequence is introduced into the MAP estimation process to regularize the estimated motion field. Experimental results show that the proposed algorithm produces estimated motion fields which preserve piecewise smooth nature and are visually close to the true motion of the video sequences.