Estimation of motion from a sequence of images using spherical projective geometry

Motion is an important cue for many applications. Here we propose a solution for estimating motion from a sequence of images using three algorithms, namely, batch, recursive and bootstrap methods. The motion derived using spherical projection relates the image motion to the object motion. This equation is reformulated into a dynamical space state model, for which a Kalman filter can be easily applied to yield the estimate of depth. We also propose a new approach for establishing correspondences using local planar invariants and hierarchical groupings. The proposed algorithm provides a simple yet robust method having lower time complexity and less ambiguity in matching than its predecessors.