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. In, the 20th International Conference on Pattern Recognition, Istanbul, Turkey,
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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.
|Item Type:||Conference or Workshop Item (Paper)|
|Additional Information:||Event Dates: Auguest 2010|
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||13 Apr 2010 09:00|
|Last Modified:||27 Mar 2014 20:15|
|Further Information:||Google Scholar|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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- Gait Learning-Based Regenerative Model: a Level Set Approach. (deposited 13 Apr 2010 09:00) [Currently Displayed]
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