Local polynomial space–time descriptors for action classification
Résumé
In this paper we propose to tackle human actions indexing by introducing a new local motion de-scriptor based on a model of the optical flow. We pro-pose to apply a coding step to vector field before the modeling. We use two modeling, a spatial model and a temporal model. The spatial model is computed by pro-jection of optical flow onto bivariate orthogonal poly-nomials. 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 ex-periments are carried out on the well known UCF11 dataset and on the more challenging Hollywood2 ac-tion classification dataset and show promising results.
Origine | Fichiers produits par l'(les) auteur(s) |
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