Skeletal parameter estimation from optical motion capture data

In this paper, we present results of our algorithm for automatically estimating a subject's skeletal structure from optical motion capture data. Our algorithm consists of a series of steps that cluster markers into groups representing body segments, determine their topological connectivity, and...

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Bibliographic Details
Published in2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 2; p. 1185 vol. 2
Main Authors Kirk, A.G., O'Brien, J.F., Forsyth, D.A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Clustering algorithms
Humans
Joints
Kirk field collapse effect
Motion estimation
Optical sensors
Parameter estimation
Skeleton
Testing
Topology
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Summary:In this paper, we present results of our algorithm for automatically estimating a subject's skeletal structure from optical motion capture data. Our algorithm consists of a series of steps that cluster markers into groups representing body segments, determine their topological connectivity, and locate the positions of the connecting joints. Our results show that the system works reliably even when only one or two markers are attached to each segment. We tested an implementation of this algorithm with both passive and active motion capture data and found it to work well. Its computed skeletal estimates closely match measured values, and the algorithm behaves robustly in the presence of noise, marker occlusion, and other errors typical of motion capture data.
ISBN:0769523722
9780769523729
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2005.327