HDNet: Hierarchical Dynamic Network for Gait Recognition using Millimeter-Wave Radar

TL;DR

This paper introduces HDNet, a hierarchical dynamic network utilizing millimeter-wave radar for gait recognition, featuring a novel point flow descriptor and dynamic frame sampling, achieving superior performance over existing methods.

Contribution

The paper proposes a novel hierarchical dynamic network with point flow descriptor and dynamic frame sampling for improved radar-based gait recognition.

Findings

HDNet outperforms existing state-of-the-art methods on public datasets.

Point flow descriptor effectively captures dynamic gait features.

Dynamic frame sampling enhances computational efficiency without performance loss.

Abstract

Gait recognition is widely used in diversified practical applications. Currently, the most prevalent approach is to recognize human gait from RGB images, owing to the progress of computer vision technologies. Nevertheless, the perception capability of RGB cameras deteriorates in rough circumstances, and visual surveillance may cause privacy invasion. Due to the robustness and non-invasive feature of millimeter wave (mmWave) radar, radar-based gait recognition has attracted increasing attention in recent years. In this research, we propose a Hierarchical Dynamic Network (HDNet) for gait recognition using mmWave radar. In order to explore more dynamic information, we propose point flow as a novel point clouds descriptor. We also devise a dynamic frame sampling module to promote the efficiency of computation without deteriorating performance noticeably. To prove the superiority of our methods, we perform extensive experiments on two public mmWave radar-based gait recognition datasets, and the results demonstrate that our model is superior to existing state-of-the-art methods.