[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

Feature Extraction and HMM-Based Classification of Gait Video Sequences 239
The HMM applied in experiments is ergodic, i.e. it allows for transitions from
any state to every other state. Probabilistic allocation of the observations to the
states is described by the continuous multivariate Gaussian distribution.
3 Experimental Results
Adapted versions of the procedures from the Matlab Toolbox for Dimensionality
Reduction (http://homepage.tudelft.nl/19j49/Matlab_Toolbox_for_Dimensionality_
Reduction.html) were used in the phase of data preparation, whereas the process of
the HMM-based data classification was programmed in C# using the Statistics library
of Accord .Net framework (http://code.google.com/p/accord/).
Accuracy of data classification was measured by means of the Correct
Classification Rate (CCR).
The main goal of the numerical experiments was to determine the dependency
between CCR and number N of system states varying from 2 to 8 for a different
values of resultant dimensionality d. However, because of computational
expensiveness of the Dijkstra’s algorithm constructing the shortest path for the
Isomap, maximum value of d used for this method was equal to 5. Results of
classification received in case of d = 5 for all three reduction methods (PCA,
Isomap, LLE) are presented in Fig. 3 (the number k of nearest neighbors was set
to 20).
Fig. 3. Dependency between CCR and number N of states for all three methods using d = 5
The whole set of experiments was performed for PCA and LLE using a set of
resultant dimensionalities d including successive powers of 2 from 1 to 512. Such
a dependency obtained for the PCA is depicted in Fig. 4, but it is necessary to
mention that sets of results related to d = 1, 2 as well as d = 512 are not presented