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Published on: May 10, 2012
A Method of Hyper-sphere Cover in Multidimensional Space for Human Mocap Data Retrieval
Xiaopeng Wei1, Boxiang Xiao, Qiang Zhang
1Key Laboratory of Advanced Design and Intelligent Computing(Dalian University), Ministry of Education, Dalian, China ; School of Mechanical Engineering, Dalian University of Technology, Dalian, China.
Journal of Human Kinetics
|March 15, 2013
Summary
This study introduces a novel hyper-sphere cover method for human motion capture (Mocap) data retrieval. This technique effectively categorizes and retrieves similar human motions from multidimensional data.
Area of Science:
- Computer Science
- Data Science
- Robotics
Background:
- Human motion capture (Mocap) data retrieval is crucial for various applications.
- Existing methods face challenges in efficiently handling high-dimensional Mocap data.
- A robust and scalable retrieval system is needed for complex motion analysis.
Purpose of the Study:
- To propose a novel hyper-sphere cover method for efficient Mocap data retrieval.
- To map Mocap data into a multidimensional space for improved organization.
- To establish a method for identifying and retrieving similar motion patterns.
Main Methods:
- Data normalization and feature extraction were performed on Mocap data.
- A hyper-sphere cover approach was applied in a multidimensional space.
- The retrieval instance and motion data were mapped into this space.
- The CMU free motion database was utilized for implementation and testing.
Main Results:
- The hyper-sphere cover method demonstrated effective organization of motion data.
- Experimental results validated the algorithm's capability in Mocap data retrieval.
- The approach successfully identified distributions of similar motion types.
Conclusions:
- The proposed hyper-sphere cover method offers a promising solution for Mocap data retrieval.
- The technique provides a way to define and retrieve motion categories based on spatial distribution.
- Further research can explore optimizations and applications in diverse Mocap scenarios.
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