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Gait Learning-Based Regenerative Model: a Level Set Approach

Al-Huseiny, Muayed, Mahmoodi, Sasan and Nixon, Mark (2010) Gait Learning-Based Regenerative Model: a Level Set Approach At the 20th International Conference on Pattern Recognition, Turkey.

Record type: Conference or Workshop Item (Paper)


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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Published date: August 2010
Additional Information: Event Dates: Auguest 2010
Venue - Dates: the 20th International Conference on Pattern Recognition, Turkey, 2010-08-01
Organisations: Southampton Wireless Group


Local EPrints ID: 270823
PURE UUID: 0ab0f59d-64ed-4699-a40c-e1bd49b5cdb8

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Date deposited: 13 Apr 2010 09:00
Last modified: 18 Jul 2017 06:50

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Author: Muayed Al-Huseiny
Author: Sasan Mahmoodi
Author: Mark Nixon

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