MultiFormer: A Multi-Person Pose Estimation System Based on CSI and Attention Mechanism
This addresses the challenge of non-intrusive human activity monitoring for applications like healthcare or surveillance, but it appears incremental as it builds on existing CSI-based methods with specific improvements.
The paper tackles the problem of accurate multi-person pose estimation using Channel State Information (CSI) by proposing MultiFormer, a system that achieves higher accuracy over state-of-the-art approaches, particularly for high-mobility keypoints like wrists and elbows.
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.