Kronecker PCA Based Spatio-Temporal Modeling of Video for Dismount Classification
This work addresses video-based gender classification for surveillance or security applications, but it is incremental as it builds on existing Kronecker PCA methods with minor modifications.
The paper tackled the problem of video dismount classification by applying Kronecker PCA spatio-temporal modeling to extract features from pedestrian bounding boxes, achieving competitive classification performance in gender classification on a challenging dataset.
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.