Abstract
Based on a three-dimensional (3D) linear model and the Bayesian rule, a method is explored to identify human walkers from two-dimensional (2D) motion sequences taken from different viewpoints. Principal component analysis constructs the 3D linear model from a set of Fourier represented examples. The sets of coefficients derived from projecting 2D motion sequences onto the 3D model by means of a maximum a posterior estimate is used as a signature of a walker. Simulating an identification experiment on a set of walking data we show that these signatures show invariance across viewpoints and can be used for viewpoint-independent person identification. © 2005 Elsevier B.V. All rights reserved.
| Original language | English |
|---|---|
| Pages (from-to) | 250-256 |
| Number of pages | 7 |
| Journal | Neurocomputing |
| Volume | 69 |
| Issue number | 1-3 |
| DOIs | |
| Publication status | Published - Dec 2005 |
Keywords
- Linear model
- Principal component analysis (PCA)
- View-independent gait identification
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