Local polynomial space–time descriptors for action classification

In this paper we propose to tackle human action indexing by introducing a new local motion descriptor based on a model of the optical flow. We propose to apply a coding step to vector field before the modeling. We use two models: a spatial model and a temporal model. The spatial model is computed by projection of optical flow onto bivariate orthogonal polynomials. Then, the time evolution of spatial coefficients is modeled with a one-dimensional polynomial basis. To perform the action classification, we extend recent still image signatures using local descriptors to our proposal and combine them with linear SVM classifiers. The experiments are carried out on the well-known UCF11 dataset and on the more challenging Hollywood2 action classification dataset and show promising results.