A Dual-Branch Structural Network for Human Pose Estimation Based on Millimeter Wave Radar

Radar-based Human Pose Estimation (R-HPE) aims to locate the body joints of each individual in a given radar image. This is relevant for various applications such as action recognition, person re-identification, and human-object interaction. Unlike traditional RGB-based human pose estimation, radar-based human pose estimation can effectively preserve human privacy and remain stable under low-light conditions and darkness. However, research on radar-based human pose estimation is limited, and existing methods fail to adequately model radar features, resulting in lower accuracy of corresponding pose estimation algorithms. Therefore, this paper proposes a dual-branch structured network, which can extract dimension-independent features and dimension-dependent features separately and then combine them for more precise decision-making. This allows the network to learn richer and more diverse feature representations, thereby improving the quality of feature extraction. Meanwhile, a Multi-Dimensional Feature Fusion Network extracts more detailed feature representations. Furthermore, it is combined with a Transformer module to further enhance the model's ability to extract local features and global modeling capabilities, thereby improving the accuracy of human pose estimation. Extensive experiments conducted on the HuPR[10] dataset demonstrate that our model outperforms existing state-of-the-art models in terms of human pose estimation performance.