Kronecker PCA based spatio-temporal modeling of video for dismount classification

Kristjan H. Greenewald; Alfred O. Hero III

doi:10.1117/12.2050184

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13 June 2014 Kronecker PCA based spatio-temporal modeling of video for dismount classification

Kristjan H. Greenewald, Alfred O. Hero III

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Proceedings Volume 9093, Algorithms for Synthetic Aperture Radar Imagery XXI; 90930V (2014) https://doi.org/10.1117/12.2050184
Event: SPIE Defense + Security, 2014, Baltimore, Maryland, United States

Abstract

We consider the application of KronPCA spatio-temporal modeling techniques^{1, 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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