Towards developing an interface for human–robot interaction, this paper proposes a
two-level approach to recognise gestures which are composed of trajectories followed
by different body parts. In a first level, individual trajectories are described by a
set of key-points. These points are chosen as the corners of the curvature function
associated to the trajectory, which will be estimated using and adaptive, non-iterative
scheme. This adaptive representation allows removing noise while preserving detail in
curvature at different scales. In a second level, gestures are characterised through global
properties of the trajectories that compose them. Gesture recognition is performed
using a confidence value that integrates both levels. Experimental results show that
the performance of the proposed method is high in terms of computational cost and
memory consumption, and gesture recognition ability.
