Radio Frequency Signal based Human Silhouette Segmentation: A Sequential Diffusion Approach

TL;DR

This paper introduces a two-stage Sequential Diffusion Model for human silhouette segmentation using RF signals, effectively capturing motion dynamics and multi-view information to improve segmentation accuracy in complex environments.

Contribution

The proposed SDM leverages a sequential diffusion approach with cross-view and spatio-temporal blocks, advancing RF-based human silhouette segmentation beyond one-shot methods.

Findings

Achieved state-of-the-art IoU of 0.732 on HIBER benchmark.

Effectively models motion dynamics and multi-view information.

Outperforms existing RF-based segmentation methods.

Abstract

Radio frequency (RF) signals have been proved to be flexible for human silhouette segmentation (HSS) under complex environments. Existing studies are mainly based on a one-shot approach, which lacks a coherent projection ability from the RF domain. Additionally, the spatio-temporal patterns have not been fully explored for human motion dynamics in HSS. Therefore, we propose a two-stage Sequential Diffusion Model (SDM) to progressively synthesize high-quality segmentation jointly with the considerations on motion dynamics. Cross-view transformation blocks are devised to guide the diffusion model in a multi-scale manner for comprehensively characterizing human related patterns in an individual frame such as directional projection from signal planes. Moreover, spatio-temporal blocks are devised to fine-tune the frame-level model to incorporate spatio-temporal contexts and motion dynamics, enhancing the consistency of the segmentation maps. Comprehensive experiments on a public benchmark -- HIBER demonstrate the state-of-the-art performance of our method with an IoU 0.732. Our code is available at https://github.com/ph-w2000/SDM.