Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2008

Abstract

The key challenge with 3D deformable surface tracking arises from the difficulty in estimating a large number of 3D shape parameters from noisy observations. A recent state-of-the-art approach attacks this problem by formulating it as a Second Order Cone Programming (SOCP) feasibility problem. The main drawback of this solution is the high computational cost. In this paper, we first reformulate the problem into an unconstrained quadratic optimization problem. Instead of handling a large set of complicated SOCP constraints, our new formulation can be solved very efficiently by resolving a set of sparse linear equations. Based on the new framework, a robust iterative method is employed to handle large outliers. We have conducted an extensive set of experiments to evaluate the performance on both synthetic and real-world testbeds, from which the promising results show that the proposed algorithm not only achieves better tracking accuracy, but also executes significantly faster than the previous solution.

Keywords

Algorithms, Performance, Experimentations, Near-duplicate keyframe, image copy detection, nonrigid image matching, semi-supervised learning

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Computer Vision: ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, Proceedings

Volume

5304

First Page

766

Last Page

779

ISBN

9783540886891

Identifier

10.1007/978-3-540-88690-7_57

Publisher

Springer

City or Country

Berlin

Additional URL

https://doi.org/10.1007/978-3-540-88690-7_57

Download

Share

COinS