Pose estimation from video sequences based on Sylvester's equation
Chong Chen, Dan Schonfeld, Junlan Yang, Magdi A. Mohamed · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
In this paper, we introduce a method to jointly track the object motion and estimate pose within the framework of particle filtering. We focus on direct estimation of the 3D pose from a 2D image sequence. Scale-Invariant Feature Transform (SIFT) is used to extract feature points in the images. We show that pose estimation from the corresponding feature points can be formed as a solution to Sylvester's equation. We rely on a solution to Sylvester's equation based on the Kronecker product method to solve the equation and determine the pose state. We demonstrate that the classical Singular Value Decomposition (SVD) approach to pose estimation provides a solution to Sylvester's equation in 3D-3D pose estimation. The proposed approach to the solution of Sylvester's equation is therefore equivalent to the classical SVD method for 3D-3D pose estimation, yet it can also be used for pose estimation from 2D image sequences. Finally, we rely on computer simulation experiments to demonstrate the performance of our algorithm on video sequences.