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Predicting missing markers in human motion capture using l 1‐sparse representation
Author(s) -
Xiao Jun,
Feng Yinfu,
Hu Wenyuan
Publication year - 2011
Publication title -
computer animation and virtual worlds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 49
eISSN - 1546-427X
pISSN - 1546-4261
DOI - 10.1002/cav.413
Subject(s) - sparse approximation , missing data , computer science , representation (politics) , perspective (graphical) , artificial intelligence , motion capture , set (abstract data type) , data set , motion (physics) , pattern recognition (psychology) , machine learning , law , politics , political science , programming language
Missing marker problem is very common in human motion capture. In contrast to most current methods which handle this problem based on trying to learn a reliable predictor from the observations, we consider it from the perspective of sparse representation and propose a novel method which is named l 1‐sparse representation of missing markers prediction (L1‐SRMMP). We assume that the incomplete pose can be represented by a linear combination of a few poses from the training set and the representation is sparse. Therefore, we cast the predicting missing markers as finding a sparse representation of the observable data of the incomplete pose, and then we use it to predict the missing data. In order to get a sparse representation, we employ l 1‐norm in our objective function. Moreover, we propose presentation coefficient weighted update (PCWU) algorithm to mitigate the limited capacity problem of the training set. Experimental results demonstrate the effectiveness and efficiency of our method to predict the missing markers in human motion capture. Copyright © 2011 John Wiley & Sons, Ltd.