Local polynomial space-time descriptors for actions classification

In this paper we propose to tackle human actions indexing by introducing a new local motion descriptor. Our proposed descriptor is based on two modeling, 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 dimension 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 KTH dataset and on the more challenging Hollywood2 action classification dataset and show promising results.