Estimating Motion of Constant Acceleration from Image Sequences

This paper presents a model-based algorithm for estimating motion from monocular image sequences. We first present a new two-view motion algorithm and then extend it to multiple views. The two-view algorithm requires generally 6 pairs of point correspondences to give unique solution of the motion parameters. However, when the uredpoints lie on a Maybank Quadric, the algorithm requires 7 pairs of point correspondences to give double solutions. Object-centered motion representations and a motion model of constant acceleration are used to estimate motion parameters from long image sequences. The algorithm guarantees globally optimal solution. Since the algorithm does not involve structure parameters, it contains the least number of unknowns and is hence more efficient and robust than the existing ones. Experimental results with real image data are presented. The same method can be applied to solve for motions described by second or higher orders of polynomials.