A Human Action Descriptor Based on Motion Coordination

TL;DR

This paper introduces CODE, a human action descriptor based on joint coordination principles, which improves action recognition by identifying key joints and their velocity correlations, tested on public datasets.

Contribution

The paper proposes a novel coordination-based descriptor (CODE) that captures joint relationships for human action recognition, incorporating velocity and correlation features.

Findings

CODE outperforms existing methods on HDM05 and Berkeley MHAD datasets.

The descriptor effectively captures joint coordination patterns for action recognition.

The correlation-based similarity measure enhances descriptor comparison accuracy.

Abstract

In this paper, we present a descriptor for human whole-body actions based on motion coordination. We exploit the principle, well known in neuromechanics, that humans move their joints in a coordinated fashion. Our coordination-based descriptor (CODE) is computed by two main steps. The first step is to identify the most informative joints which characterize the motion. The second step enriches the descriptor considering minimum and maximum joint velocities and the correlations between the most informative joints. In order to compute the distances between action descriptors, we propose a novel correlation-based similarity measure. The performance of CODE is tested on two public datasets, namely HDM05 and Berkeley MHAD, and compared with state-of-the-art approaches, showing recognition results.