Name
Abstract | ||
|---|---|---|
We propose a learning method for gait synthesis from a sequence of shapes(frames) with the ability to extrapolate to novel data. It involves the application of PCA, first to reduce the data dimensionality to certain features, and second to model corresponding features derived from the training gait cycles as a Gaussian distribution. This approach transforms a non Gaussian shape deformation problem into a Gaussian one by considering features of entire gait cycles as vectors in a Gaussian space. We show that these features which we formulate as continuous functions can be modeled by PCA. We also use this model to in-between (generate intermediate unknown) shapes in the training cycle. Furthermore, this paper demonstrates that the derived features can be used in the identification of pedestrians. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICPR.2010.648 | Pattern Recognition |
Keywords | Field | DocType |
Gaussian distribution,gait analysis,image motion analysis,learning (artificial intelligence),principal component analysis,shape recognition,Gaussian distribution,Gaussian shape deformation problem,Gaussian space,PCA,gait learning,gait synthesis,level set approach,pedestrian identification,regenerative model,Computer Vistion,Gait Analysis,Level Sets,PCA,Statistical Models | Continuous function,Computer vision,Data modeling,Pattern recognition,Computer science,Level set,Curse of dimensionality,Gait analysis,Gaussian,Artificial intelligence,Statistical model,Principal component analysis | Conference |
ISSN | ISBN | Citations |
1051-4651 | 978-1-4244-7542-1 | 9 |
PageRank | References | Authors |
0.53 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muayed S. Al-Huseiny | 1 | 18 | 2.17 |
| Sasan Mahmoodi | 2 | 94 | 17.37 |
| Mark S. Nixon | 3 | 3080 | 304.45 |