An efficient way to classify human gaits

Chili Chang Yu, Hsu Yung Cheng, Chien Hung Cheng, Kuo Chin Fan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The computation efficiency in human identification problem is a very important issue when the number of database templates is large. In this paper, we propose a histogram based approach to improve the computation efficiency for human gait classification. We convert the human gait classification problem to a histogram matching problem. In order to speed up the recognition process, we adopt a multiresolution structure on the Motion Energy Histogram (MEH). To utilize the multiresolution structure more efficiently, we propose an automated uneven partitioning method which is achieved by utilizing the quadtree decomposition results of MEH. In that case, the computation time is only relevant to the number of partitioned histogram bins. Experiments demonstrate the feasibility and validity of the proposed approach.

Original languageEnglish
Title of host publication2nd International Conference on Computer Research and Development, ICCRD 2010
Pages156-160
Number of pages5
DOIs
StatePublished - 2010
Event2nd International Conference on Computer Research and Development, ICCRD 2010 - Kuala Lumpur, Malaysia
Duration: 7 May 201010 May 2010

Publication series

Name2nd International Conference on Computer Research and Development, ICCRD 2010

Conference

Conference2nd International Conference on Computer Research and Development, ICCRD 2010
Country/TerritoryMalaysia
CityKuala Lumpur
Period7/05/1010/05/10

Keywords

  • Gait classification
  • Multiresolution histogram

Fingerprint

Dive into the research topics of 'An efficient way to classify human gaits'. Together they form a unique fingerprint.

Cite this