Tracking Deformable Object via Particle Filtering on Manifolds

Abstract

Dynamic deformation of target is a prominent problem in image-based tracking. Most existing particle filtering based tracking algorithms treat deformation parameters of the target as a vector. We have proposed a deformable target tracking algorithm via particle filtering on manifolds, which implements the particle filter with the constraint that the system state lies in a low dimensional manifold: affine Lie group. The sequential Bayesian updating consists in drawing state samples while moving on the manifold geodesics; this provides a smooth prior for the state space change. Then we estimate affine deformation parameters through means on Lie group. Theoretic analysis and experimental evaluations against the tracking algorithm based on particle filtering on vector spaces demonstrate the promise and effectiveness of this algorithm.