Radio Frequency Signal based Human Silhouette Segmentation: A Sequential Diffusion Approach
<|reference_start|>Radio Frequency Signal based Human Silhouette Segmentation: A Sequential Diffusion Approach: 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.<|reference_end|>
@article{wen2024radio,
    title={Radio Frequency Signal based Human Silhouette Segmentation: A Sequential
  Diffusion Approach},
    author={Penghui Wen, Kun Hu, Dong Yuan, Zhiyuan Ning, Changyang Li, Zhiyong
  Wang},
    journal={arXiv preprint arXiv:2407.19244},
    year={2024},
    doi={10.1109/ICME57554.2024.10688347},
    archivePrefix={arXiv},
    eprint={2407.19244},
    primaryClass={cs.CV cs.MM}
}