Human motion classification and management based on mocap data analysis

Authors:
Harshad Kadu;Maychen Kuo;C.-C. Jay Kuo
Affiliations:
University of Southern California, Los Angeles, USA;University of Southern California, Los Angeles, USA;University of Southern California, Los Angeles, USA
Venue:
J-HGBU '11 Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding
Year:
2011

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Abstract

Human motion understanding based on motion capture (mocap) data is investigated. Recent rapid developments and applications of mocap systems have resulted in a large corpus of mocap sequences, and an automated annotation technique that can classify basic motion types into multiple categories is needed. A novel technique for automated mocap data classification is developed in this work. Specifically, we adopt the tree-structured vector quantization (TSVQ) method to approximate human poses by codewords and approximate the dynamics of mocap sequences by a codeword sequence. To classify mocap data into different categories, we consider three approaches: 1) the spatial domain approach based on the histogram of codewords, 2) the spatial-time domain approach via codeword sequence matching, and 3) a decision fusion approach. We test the proposed algorithm on the CMU mocap database using the n-fold cross validation procedure and obtain a correct classification rate of 97%.