3D Human Action Recognition Using Spatio-temporal Motion Templates
Our goal is automatic recognition of basic human actions, such as stand, sit and wave hands, to aid in natural communication between a human and a computer. Human actions are inferred from human body joint motions, but such data has high dimensionality and large spatial and temporal variations may o...
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Published in | Computer Vision in Human-Computer Interaction pp. 120 - 130 |
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Main Authors | , , |
Format | Book Chapter Conference Proceeding |
Language | English |
Published |
Berlin, Heidelberg
Springer Berlin Heidelberg
2005
Springer |
Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
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Summary: | Our goal is automatic recognition of basic human actions, such as stand, sit and wave hands, to aid in natural communication between a human and a computer. Human actions are inferred from human body joint motions, but such data has high dimensionality and large spatial and temporal variations may occur in executing the same action. We present a learning-based approach for the representation and recognition of 3D human action. Each action is represented by a template consisting of a set of channels with weights. Each channel corresponds to the evolution of one 3D joint coordinate and its weight is learned according to the Neyman-Pearson criterion. We use the learned templates to recognize actions based on χ2 error measurement. Results of recognizing 22 actions on a large set of motion capture sequences as well as several annotated and automatically tracked sequences show the effectiveness of the proposed algorithm. |
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Bibliography: | This research was supported, in part, by the Advanced Research and Development Activity of the U.S. Government under contract No. MDA904-03-C1786 |
ISBN: | 9783540296201 3540296204 |
ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/11573425_12 |