用无线信号精准识别人体姿态，尤其擅长追踪快速移动的关节。
MultiFormer: A Multi-Person Pose Estimation System Based on CSI and Attention Mechanism
- 基于时频双令牌提取器与多头自注意力建模信号特征
- 在公开数据集和自采数据上超越现有方法，腕部肘部精度提升显著
- 适合需要无感监测人体动作的智慧医疗、安防场景
基于信道状态信息（CSI）的人体姿态估计已成为一种非侵入式高精度活动监测的有前景方法，但仍面临多人姿态准确识别和有效特征学习的挑战。本文提出MultiFormer，一种基于无线传感的多人姿态估计系统。该系统采用基于Transformer的时频双令牌特征提取器，结合多头自注意力机制，能够建模子载波间的相关性与时序依赖性。提取的CSI特征与姿态概率热图通过多阶段特征融合网络（MSFN）融合，以施加解剖学约束。在公开的MM-Fi数据集和自建数据集上的大量实验表明，MultiFormer在主流方法之上取得更高精度，尤其在以往难以准确估计的高动态关键点（如手腕、肘部）表现突出。
原文摘要 · Abstract (English)
Human pose estimation based on Channel State Information (CSI) has emerged as a promising approach for non-intrusive and precise human activity monitoring, yet faces challenges including accurate multi-person pose recognition and effective CSI feature learning. This paper presents MultiFormer, a wireless sensing system that accurately estimates human pose through CSI. The proposed system adopts a Transformer based time-frequency dual-token feature extractor with multi-head self-attention. This feature extractor is able to model inter-subcarrier correlations and temporal dependencies of the CSI. The extracted CSI features and the pose probability heatmaps are then fused by Multi-Stage Feature Fusion Network (MSFN) to enforce the anatomical constraints. Extensive experiments conducted on on the public MM-Fi dataset and our self-collected dataset show that the MultiFormer achieves higher accuracy over state-of-the-art approaches, especially for high-mobility keypoints (wrists, elbows) that are particularly difficult for previous methods to accurately estimate.
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