A VARIATIONAL FRAMEWORK FOR SIMULTANEOUS MOTION AND DISPARITY ESTIMATION IN A SEQUENCE OF STEREO IMAGES

In this paper, we present a variational framework for joint disparity and motion estimation in a sequence of stereo images. The prob-lem involves the estimation of four dense fields: two motion fields and two disparity fields. In order to reduce computational complex-ity and improve estimation accuracy, the two motion fields, for the left and right sequences, and the disparity field of the current stereo pair are jointly estimated, using the stereo-motion consistency con-straint. In the proposed variational framework, the joint estimation problem is formulated as a convex programming problem in which a convex objective function is minimized under specific convex con-straints. This minimization is achieved using an efficient parallel block-iterative algorithm. Experimental results involving real stereo sequences indicate the feasibility and robustness of our approach