  We consider the application of KronPCA spatio-temporal modeling techniques
[Greenewald et al 2013, Tsiligkaridis et al 2013] to the extraction of
spatiotemporal features for video dismount classification. KronPCA performs a
low-rank type of dimensionality reduction that is adapted to spatio-temporal
data and is characterized by the T frame multiframe mean and covariance of p
spatial features. For further regularization and improved inverse estimation,
we also use the diagonally corrected KronPCA shrinkage methods we presented in
[Greenewald et al 2013]. We apply this very general method to the modeling of
the multivariate temporal behavior of HOG features extracted from pedestrian
bounding boxes in video, with gender classification in a challenging dataset
chosen as a specific application. The learned covariances for each class are
used to extract spatiotemporal features which are then classified, achieving
competitive classification performance.
