Al-Huseiny, Muayed, Mahmoodi, Sasan and Nixon, Mark
Gait Learning-Based Regenerative Model: a Level Set Approach
At the 20th International Conference on Pattern Recognition, Turkey.
This is the latest version of this item.
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.
Conference or Workshop Item
||Event Dates: Auguest 2010
|Venue - Dates:
||the 20th International Conference on Pattern Recognition, Turkey, 2010-08-01
||Southampton Wireless Group
||13 Apr 2010 09:00
||17 Apr 2017 18:28
|Further Information:||Google Scholar|
Available Versions of this Item
Gait Learning-Based Regenerative Model: a Level Set Approach (deposited 13 Apr 2010 09:00)
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