HDNet: Hierarchical Dynamic Network for Gait Recognition using Millimeter-Wave Radar
This work addresses privacy and robustness issues in gait recognition for surveillance applications, though it is incremental as it builds on existing radar-based methods.
The authors tackled gait recognition in challenging environments by proposing HDNet, a hierarchical dynamic network using millimeter-wave radar, which outperformed existing state-of-the-art methods on two public datasets.
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.