Cross-modal Learning of Graph Representations using Radar Point Cloud for Long-Range Gesture Recognition

TL;DR

This paper introduces a novel cross-modal learning architecture that combines camera and radar point clouds to enable accurate long-range gesture recognition in challenging conditions, achieving high accuracy and robustness.

Contribution

It presents a new long-range gesture recognition method using radar and camera data with a cross-learning approach and a Dynamic Graph CNN variant for improved representation.

Findings

Achieved 98.4% accuracy on five gestures

Demonstrated effective noise suppression and generalization

Enabled long-range recognition up to 2 meters

Abstract

Gesture recognition is one of the most intuitive ways of interaction and has gathered particular attention for human computer interaction. Radar sensors possess multiple intrinsic properties, such as their ability to work in low illumination, harsh weather conditions, and being low-cost and compact, making them highly preferable for a gesture recognition solution. However, most literature work focuses on solutions with a limited range that is lower than a meter. We propose a novel architecture for a long-range (1m - 2m) gesture recognition solution that leverages a point cloud-based cross-learning approach from camera point cloud to 60-GHz FMCW radar point cloud, which allows learning better representations while suppressing noise. We use a variant of Dynamic Graph CNN (DGCNN) for the cross-learning, enabling us to model relationships between the points at a local and global level and to model the temporal dynamics a Bi-LSTM network is employed. In the experimental results section, we demonstrate our model's overall accuracy of 98.4% for five gestures and its generalization capability.