Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Automatic motion capture data denoising via filtered subspace clustering and low rank matrix approximation

Language

English

Abstract

In this paper, we present an automatic Motion Capture (MoCap) data denoising approach via filtered subspace clustering and low rank matrix approximation. Within the proposed approach, we formulate the MoCap data denoising problem as a concatenation of piecewise motion matrix recovery problem. To this end, we first present a filtered subspace clustering approach to separate the noisy MoCap sequence into a group of disjoint piecewise motions, in which the moving trajectories of each piecewise motion always share the similar low dimensional subspace representation. Then, we employ the accelerated proximal gradient (APG) algorithm to find a complete low-rank matrix approximation to each noisy piecewise motion and further apply a moving average filter to smooth the moving trajectories between the connected motions. Finally, the whole noisy MoCap data can be automatically restored by a concatenation of all the recovered piecewise motions sequentially. The proposed approach does not need any physical information about the underling structure of MoCap data or require auxiliary data sets for training priors. The experimental results have shown an improved performance in comparison with the state-of-the-art competing approaches. © 2014 Elsevier B.V.

Keywords

Accelerated proximal gradient, Filtered subspace clustering, Low-rank matrix approximation, MoCap data denoising, Moving average filter

Publication Date

2014

Source Publication Title

Signal Processing

Volume

105

Start Page

350

End Page

362

Publisher

Elsevier

DOI

10.1016/j.sigpro.2014.06.009

ISSN (print)

01651684

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