Gait Learning-Based Regenerative Model: a Level Set Approach
Muayed Al-Huseiny, Sasan Mahmoodi and Mark Nixon
In: International Conference on Pattern Recognition, 23- 26 August, Istanbul, Turkey.
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.