Document Detail


Example-based human motion denoising.
MedLine Citation:
PMID:  20616400     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
With the proliferation of motion capture data, interest in removing noise and outliers from motion capture data has increased. In this paper, we introduce an efficient human motion denoising technique for the simultaneous removal of noise and outliers from input human motion data. The key idea of our approach is to learn a series of filter bases from precaptured motion data and use them along with robust statistics techniques to filter noisy motion data. Mathematically, we formulate the motion denoising process in a nonlinear optimization framework. The objective function measures the distance between the noisy input and the filtered motion in addition to how well the filtered motion preserves spatial-temporal patterns embedded in captured human motion data. Optimizing the objective function produces an optimal filtered motion that keeps spatial-temporal patterns in captured motion data. We also extend the algorithm to fill in the missing values in input motion data. We demonstrate the effectiveness of our system by experimenting with both real and simulated motion data. We also show the superior performance of our algorithm by comparing it with three baseline algorithms and to those in state-of-art motion capture data processing software such as Vicon Blade.
Authors:
Hui Lou; Jinxiang Chai
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  IEEE transactions on visualization and computer graphics     Volume:  16     ISSN:  1077-2626     ISO Abbreviation:  IEEE Trans Vis Comput Graph     Publication Date:    2010 Sep-Oct
Date Detail:
Created Date:  2010-07-09     Completed Date:  2010-09-28     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9891704     Medline TA:  IEEE Trans Vis Comput Graph     Country:  United States    
Other Details:
Languages:  eng     Pagination:  870-9     Citation Subset:  IM    
Affiliation:
Texas A&M University, College Station, TX, USA. wslh@cse.tamu.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Computer Graphics*
Data Interpretation, Statistical
Humans
Imaging, Three-Dimensional
Models, Biological*
Movement / physiology*
Nonlinear Dynamics

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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