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arXiv · 1405.4574

Kronecker PCA Based Spatio-Temporal Modeling of Video for Dismount Classification

Kristjan H. Greenewald·Alfred O. Hero III
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Abstract

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.

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Kristjan H. Greenewald, Alfred O. Hero III. 2014-05-19. Kronecker PCA Based Spatio-Temporal Modeling of Video for Dismount Classification. https://doi.org/10.1117/12.2050184

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