A compositional approach for 3D arm-hand action recognition

Abstract

In this paper we propose a fast and reliable vision-based framework for 3D arm-hand action modelling, learning and recognition in human-robot interaction scenarios. The architecture consists of a compositional model that divides the arm-hand action recognition problem into three levels. The bottom level is based on a simple but sufficiently accurate algorithm for the computation of the scene flow. The middle level serves to classify action primitives through descriptors obtained from 3D Histogram of Flow (3D-HOF); we further apply a sparse coding (SC) algorithm to deal with noise. Action Primitives are then modelled and classified by linear Support Vector Machines (SVMs), and we propose an on-line algorithm to cope with the real-time recognition of primitive sequences. The top level system synthesises combinations of primitives by means of a syntactic approach. In summary the main contribution of the paper is an incremental method for 3D arm-hand behaviour modelling and recognition, fully implemented and tested on the iCub robot, allowing it to learn new actions after a single demonstration.