Robust human posture analysis using incremental learning and recall based on degree of confidence of feature points

Atsushi Shimada, Madoka Kanouchi, Daisaku Arita, Rin Ichiro Taniguchi

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Purpose - The purpose of this paper is to present an approach to improve the accuracy of estimating feature points of human body on a vision-based motion capture system (MCS) by using the variable-density self-organizing map (VDSOM). Design/methodology/approach - The VDSOM is a kind of self-organizing map (SOM) and has an ability to learn training samples incrementally. The authors let VDSOM learn 3D feature points of human body when the MCS succeeded in estimating them correctly. On the other hand, one or more 3D feature point could not be estimated correctly, the VDSOM is used for the other purpose. The SOM including VDSOM has an ability to recall a part of weight vector which have learned in the learning process. This ability is used to recall correct patterns and complement such incorrect feature points by replacing such incorrect feature points with them. Findings - Experimental results show that the approach is effective for estimation of human posture robustly compared with the other methods. Originality/value - The proposed approach is interesting for the collaboration between an MCS and an incremental learning.

Original languageEnglish
Pages (from-to)304-326
Number of pages23
JournalInternational Journal of Intelligent Computing and Cybernetics
Volume2
Issue number2
DOIs
Publication statusPublished - Jun 5 2009

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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