Robust estimation of human posture using incremental learnable self-organizing map

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

We propose 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). The VDSOM is a kind of SelfOrganizing Map (SOM) and has an ability to learn training samples incrementally. We let VDSOM learn 3-D feature points of human body when the MCS succeeded in estimating them correctly. On the other hand, one or more 3-D feature point could not be estimated correctly, we use the VDSOM 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. We use this ability to recall correct patterns and complement such incorrect feature points by replacing such incorrect feature points with them.

Original languageEnglish
Title of host publication2008 International Joint Conference on Neural Networks, IJCNN 2008
Pages939-946
Number of pages8
DOIs
Publication statusPublished - Nov 24 2008
Event2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China
Duration: Jun 1 2008Jun 8 2008

Other

Other2008 International Joint Conference on Neural Networks, IJCNN 2008
CountryChina
CityHong Kong
Period6/1/086/8/08

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All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Cite this

Shimada, A., Kanouchi, M., Arita, D., & Taniguchi, R-I. (2008). Robust estimation of human posture using incremental learnable self-organizing map. In 2008 International Joint Conference on Neural Networks, IJCNN 2008 (pp. 939-946). [4633912] https://doi.org/10.1109/IJCNN.2008.4633912