Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.19244 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916421223579648 |
|---|---|
| author | Wen, Penghui Hu, Kun Yuan, Dong Ning, Zhiyuan Li, Changyang Wang, Zhiyong |
| author_facet | Wen, Penghui Hu, Kun Yuan, Dong Ning, Zhiyuan Li, Changyang Wang, Zhiyong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19244 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Radio Frequency Signal based Human Silhouette Segmentation: A Sequential Diffusion Approach Wen, Penghui Hu, Kun Yuan, Dong Ning, Zhiyuan Li, Changyang Wang, Zhiyong Computer Vision and Pattern Recognition Multimedia 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. |
| title | Radio Frequency Signal based Human Silhouette Segmentation: A Sequential Diffusion Approach |
| topic | Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2407.19244 |