| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 9653374 | Neurocomputing | 2005 | 7 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Zonghua Zhang, Nikolaus F Troje,