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Proceedings Paper

Pose estimation from video sequences based on Sylvester's equation
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Paper Abstract

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.

Paper Details

Date Published: 29 January 2007
PDF: 12 pages
Proc. SPIE 6508, Visual Communications and Image Processing 2007, 65081S (29 January 2007); doi: 10.1117/12.702601
Show Author Affiliations
Chong Chen, Univ. of Illinois at Chicago (United States)
Dan Schonfeld, Univ. of Illinois at Chicago (United States)
Junlan Yang, Univ. of Illinois at Chicago (United States)
Magdi Mohamed, Motorola Labs. (United States)

Published in SPIE Proceedings Vol. 6508:
Visual Communications and Image Processing 2007
Chang Wen Chen; Dan Schonfeld; Jiebo Luo, Editor(s)

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