[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

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paths classification: statistical, histogram, Fourier transform and timeline. The
features can be calculated by using dimensionality reduction techniques [24]. The
classical approaches of dimensionality reduction of motion data do the work at
the level of the pose descriptors. In [19] on the basis of distances between the
selected body parts, the feature vectors of binary silhouettes are extracted and the
first two principal components are chosen. In [10] linear PCA and in [15] nonlinear
manifold learning is applied prior to the classification with HMM. In [21]
and [19] DTW follows by the reduction of pose descriptors by PCA of feature
vectors calculated for video and motion capture data, respectively. In [18] a modified
ICA is used for skeletons of binary silhouettes. Other examples can be found
in [12], [8] and [11].
Recently the multilinear reduction methods for tensor objects have gained more
attention. They allow to reduce multi-dimensional objects indexed by multiple
indices. The motion sequences are addressed by the frame number and spatial
coordinates, which means that the entire sequences can be reduced, not only the
pose descriptors. In [16] the MPCA method is used for the detected cycles of binary
silhouettes and the classification is performed by selected distance functions.
The MPCA reduction is also extended by LDA method. The application of MPCA
to the classification of motion capture data by supervised learning can be found in
[22]. In [14] mutlilinear ICA and in [17] uncorrelated MPCA are applied to face
recognitions.
It is very challenging task to propose small number of features without loss of
individual data. Thus, feature vectors are usually defined in high dimensional
spaces, which makes the classification more difficult and less reliable. However, it
is still possible to discover a feature subset sufficient for precise motion data
classification by a proper search method. That is main contribution of presented
research. We apply supervised feature selection for extracted features of video
sequences. The effectiveness of selection is evaluated by obtained compression
rate and accuracy of a classification.
On the basis of our previous work [6] we decided to extract features of video
recordings data by MPCA technique and classify them by supervised machine
learning. To reduce the dimensionality of feature space and discover most informative
ones the selection is carried out, prior to classification phase. Similar as in
[6] CASIA gait database is used to examine proposed identification method.
2 Gait Data Representation
Portions of the research in this paper use the CASIA Gait Database collected by
the Institute of Automation, Chinese Academy of Sciences. The database contains
raw video recordings as well as extracted binary silhouettes by background subtraction
(see Fig. 1). The data could be used as a basis for markerless motion capture
[23].