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Implementation of a Multiple Switch Time Approach to Style-Based Motion Segmentation

Sheng, Yu
Format
Thesis/Dissertation; Online
Author
Sheng, Yu
Advisor
LaViers Minnick, Amy
Abstract
This thesis presents progress on segmenting human movement based on a notion of movement quality. This research is an extension of a style-based motion classification where, here, this classification is used to segment long motion phrases into smaller, discrete motion snippets. In particular, this thesis presents a given trajectory that is segmented into three shorter trajectories that each has their own length and quality. The objective of the thesis is to refine this segmentation, extend it to an arbitrary number of segmentation points, apply it to motion capture data and explore other extensions. A key novel contribution of this thesis is the analytical derivation of first order necessary conditions for optimality. The research may be used to build a library of motion primitives and aid the study of motion recognition in automation.
Language
English
Published
University of Virginia, Department of Systems Engineering, MS (Master of Science), 2014
Published Date
2014-07-24
Degree
MS (Master of Science)
Collection
Libra ETD Repository
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