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3d human motion retrieval based on human hierarchical index structure.

Q Zhang1, X Guo1

  • 1Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Ministry of Education, Dalian, China.

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Summary
This summary is machine-generated.

This study introduces an efficient method for retrieving large human motion datasets using a hierarchical index and fast similarity computation. The approach ensures effective organization and retrieval of motion data, improving database management.

Keywords:
KMP algorithmhuman hierarchical structuremotion capturemotion patternmotion retrieval

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Data Science

Background:

  • The increasing size of motion capture data necessitates efficient retrieval methods.
  • Existing motion retrieval systems require effective indexing and similarity measures for large datasets.

Purpose of the Study:

  • To develop an efficient and effective method for retrieving human motion sequences from large databases.
  • To address the challenges posed by the growing volume of motion capture data.

Main Methods:

  • A hierarchical index structure for human motion data was developed.
  • A nonlinear method was employed for segmenting motion sequences.
  • Motion patterns were extracted and a fast similarity measure algorithm was used for computation.

Main Results:

  • The proposed method demonstrated effectiveness in organizing and retrieving motion data.
  • Experimental results confirmed the efficiency of the hierarchical indexing and similarity computation approach.

Conclusions:

  • The developed hierarchical index structure and fast similarity measure provide an effective solution for large-scale motion database retrieval.
  • This approach enhances the efficiency and usability of motion capture data management systems.