3D Human Action Recognition Using Spatio-temporal Motion Templates
Abstract
Our goal is automatic recognition of basic human actions,
such as stand, sit and wave hands, to aid in natural communication between
a human and a computer. Human actions are inferred from human
body joint motions, but such data has high dimensionality and large spatial
and temporal variations may occur in executing the same action. We
present a learning-based approach for the representation and recognition
of 3D human action. Each action is represented by a template consisting
of a set of channels with weights. Each channel corresponds to the
evolution of one 3D joint coordinate and its weight is learned according
to the Neyman-Pearson criterion. We use the learned templates to
recognize actions based on ¥ö2 error measurement. Results of recognizing
22 actions on a large set of motion capture sequences as well as several
annotated and automatically tracked sequences show the effectiveness of
the proposed algorithm.