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Knowledge discovery from 3D human motion streams through semantic dimensional reduction

Knowledge discovery from 3D human motion streams through semantic dimensional reduction Knowledge Discovery from 3D Human Motion Streams through Semantic Dimensional Reduction YOHAN JIN, MySpace Inc. BALAKRISHNAN PRABHAKARAN, University of Texas 3D human motion capture is a form of multimedia data that is widely used in entertainment as well as medical elds (such as orthopedics, physical medicine, and rehabilitation where gait analysis is needed). These applications typically create large repositories of motion capture data and need ef cient and accurate content-based retrieval techniques. 3D motion capture data is in the form of multidimensional time-series data. To reduce the dimensions of human motion data while maintaining semantically important features, we quantize human motion data by extracting spatio-temporal features through SVD and translate them onto a symbolic sequential representation through our proposed sGMMEM (semantic Gaussian Mixture Modeling with EM). In order to handle variations in motion capture data due to human body characteristics and speed of motion, we transform the semantically quantized values into a histogram representation. This representation is used as a signature for classi cation and similarity-based retrieval. We achieved good classi cation accuracies for œcoarse  human motion categories (such as walking 92.85%, run 91.42%, and jump 94.11%) and even for subtle categories (such as dance 89.47%, laugh http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) Association for Computing Machinery

Knowledge discovery from 3D human motion streams through semantic dimensional reduction

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2011 by ACM Inc.
ISSN
1551-6857
DOI
10.1145/1925101.1925104
Publisher site
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Abstract

Knowledge Discovery from 3D Human Motion Streams through Semantic Dimensional Reduction YOHAN JIN, MySpace Inc. BALAKRISHNAN PRABHAKARAN, University of Texas 3D human motion capture is a form of multimedia data that is widely used in entertainment as well as medical elds (such as orthopedics, physical medicine, and rehabilitation where gait analysis is needed). These applications typically create large repositories of motion capture data and need ef cient and accurate content-based retrieval techniques. 3D motion capture data is in the form of multidimensional time-series data. To reduce the dimensions of human motion data while maintaining semantically important features, we quantize human motion data by extracting spatio-temporal features through SVD and translate them onto a symbolic sequential representation through our proposed sGMMEM (semantic Gaussian Mixture Modeling with EM). In order to handle variations in motion capture data due to human body characteristics and speed of motion, we transform the semantically quantized values into a histogram representation. This representation is used as a signature for classi cation and similarity-based retrieval. We achieved good classi cation accuracies for œcoarse  human motion categories (such as walking 92.85%, run 91.42%, and jump 94.11%) and even for subtle categories (such as dance 89.47%, laugh

Journal

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) – Association for Computing Machinery

Published: Feb 1, 2011

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