Neural-network-based real-time human body posture estimation

Kazuhiko Takahashi*, Tetsuya Uemura, Jun Ohya

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

3 Citations (Scopus)


This paper proposes a real-time human body posture estimation method using ANNs. The network is composed of three ANNs and a decision logic unit. The ANNs' input is the result of a function analysis on a human silhouette's contour extracted from camera images and the ANNs' output indicates the feature points' positions on the contour. The decision logic unit synthesizes each of the ANNs' output vectors and then the 2D coordinates of the human body's feature points are calculated. The proposed method is implemented on a personal computer and runs in real-time (17-20 frames/sec). Experimental results confirm both the feasibility and the effectiveness of the proposed method for estimating human body postures.

Original languageEnglish
Number of pages10
Publication statusPublished - 2000 Dec 1
Externally publishedYes
Event10th IEEE Workshop on Neural Network for Signal Processing (NNSP2000) - Sydney, Australia
Duration: 2000 Dec 112000 Dec 13


Other10th IEEE Workshop on Neural Network for Signal Processing (NNSP2000)
CitySydney, Australia

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering


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