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13 June 2014 Kronecker PCA based spatio-temporal modeling of video for dismount classification
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Abstract
We consider the application of KronPCA spatio-temporal modeling techniques1, 2 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 μ 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.1 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.
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Kristjan H. Greenewald and Alfred O. Hero III "Kronecker PCA based spatio-temporal modeling of video for dismount classification", Proc. SPIE 9093, Algorithms for Synthetic Aperture Radar Imagery XXI, 90930V (13 June 2014); https://doi.org/10.1117/12.2050184
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