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

TL;DR

This paper applies Kronecker PCA to model spatiotemporal features in video for dismount classification, demonstrating effective feature extraction and classification performance on challenging datasets.

Contribution

It introduces the use of KronPCA with diagonally corrected shrinkage for spatiotemporal modeling of video features in dismount classification.

Findings

Achieved competitive classification accuracy on challenging datasets.

Demonstrated effective spatiotemporal feature extraction using KronPCA.

Validated the approach with HOG features from pedestrian videos.

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.