Paper Info
Title
Restoring corrupted motion capture data via jointly low-rank matrix completion
Abstract
Motion capture (mocap) technology is widely used in various applications. The acquired mocap data usually has missing data due to occlusions or ambiguities. Therefore, restoring the missing entries of the mocap data is a fundamental issue in mocap data analysis. Based on jointly low-rank matrix completion, this paper presents a practical and highly efficient algorithm for restoring the missing mocap data. Taking advantage of the unique properties of mocap data (i.e, strong correlation among the data), we represent the corrupted data as two types of matrices, where both the local and global characteristics are taken into consideration. Then we formulate the problem as a convex optimization problem, where the missing data is recovered by solving the two matrices using the alternating direction method of multipliers algorithm. Experimental results demonstrate that the proposed scheme significantly outperforms the state-of-the-art algorithms in terms of both the quality and computational cost.
Year
DOI
Venue
2014
10.1109/ICME.2014.6890222
ICME
Keywords
Field
DocType
matrix multiplication,missing mocap data,mocap data analysis,image capture,jointly low-rank matrix completion,alternating direction method,convex optimization problem,mocap technology,convex programming,restoring corrupted motion capture data,multipliers algorithm,matrix completion,motion capture,low-rank,convex optimization,image motion analysis,optimization,trajectory,image restoration,accuracy,convex functions
Motion capture,Matrix completion,Pattern recognition,Computer science,Matrix (mathematics),Low-rank approximation,Convex function,Artificial intelligence,Missing data,Image restoration,Convex optimization
Conference
ISSN
Citations 
PageRank 
1945-7871
4
0.40
References 
Authors
7
5
Name
Order
Citations
PageRank
Junhui Hou139549.84
Zhen-Peng Bian2584.41
Lap-pui Chau3121898.45
Nadia Magnenat-Thalmann45119659.15
Ying He51264105.35