A Global Spatio-Temporal Representation for Action Recognition
In this paper we introduce an effective method to construct a global spatio-temporal representation for action recognition. This representation is inspired by the fact that human actions can be treated as 3D shapes induced by the silhouettes in the space-time volume. We estimate the silhouettes which contain detailed shape information of the action, and present an efficient sampling method to extract interest points along the silhouettes. The local interest point is represented by a spatio-temporal descriptor based on 2D DAISY. Our global space-time representation is the integration of these local descriptors in an order along the silhouette. In this manner, we not only utilize the static shape information, but also the spatial-temporal cue. We have obtained impressive results on publicly available action datasets.