PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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.

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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7126
Deposited By:Sasan Mahmoodi
Deposited On:04 March 2011