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ICPR
2010
IEEE

Gait Learning-Based Regenerative Model: A Level Set Approach

13 years 10 months ago
Gait Learning-Based Regenerative Model: A Level Set Approach
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.
Muayed Sattar Al-Huseiny, Sasan Mahmoodi, Mark Nix
Added 13 May 2010
Updated 13 May 2010
Type Conference
Year 2010
Where ICPR
Authors Muayed Sattar Al-Huseiny, Sasan Mahmoodi, Mark Nixon
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