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

Xin Liu*, Yiu Ming CHEUNG, Shu Juan Peng, Zhen Cui, Bineng Zhong, Ji Xiang Du

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

26 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)350-362
Number of pages13
JournalSignal Processing
Publication statusPublished - Dec 2014

Scopus Subject Areas

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

User-Defined Keywords

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


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