Sparse composition of body poses and atomic actions for human activity recognition in RGB-D videos

Abstract

This paper presents an approach to recognize human activities using body poses estimated from RGB-D data.

We focus on recognizing complex activities composed of sequential or simultaneous atomic actions characterized by body motions of a single actor. We tackle this problem by introducing a hierarchical compositional model that operates at three levels of abstraction. At the lowest level, geometric and motion descriptors are used to learn a dictionary of body poses. At the intermediate level, sparse compositions of these body poses are used to obtain meaningful representations for atomic human actions. Finally, at the highest level, spatial and temporal compositions of these atomic actions are used to represent complex human activities.

Our results show the benefits of using a hierarchical model that exploits the sharing and composition of body poses into atomic actions, and atomic actions into activities.

A quantitative evaluation using two benchmark datasets illustrates the advantages of our model to perform action and activity recognition.