LoFi: Vision-Aided Label Generator for Wi-Fi Localization and Tracking
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909782566240256 |
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| author | Zhao, Zijian Chen, Tingwei Meng, Fanyi Cai, Zhijie Li, Hang Li, Xiaoyang Zhu, Guangxu |
| author_facet | Zhao, Zijian Chen, Tingwei Meng, Fanyi Cai, Zhijie Li, Hang Li, Xiaoyang Zhu, Guangxu |
| contents | Data-driven Wi-Fi localization and tracking have shown great promise due to their lower reliance on specialized hardware compared to model-based methods. However, most existing data collection techniques provide only coarse-grained ground truth or a limited number of labeled points, significantly hindering the advancement of data-driven approaches. While systems like lidar can deliver precise ground truth, their high costs make them inaccessible to many users. To address these challenges, we propose LoFi, a vision-aided label generator for Wi-Fi localization and tracking. LoFi can generate ground truth position coordinates solely from 2D images, offering high precision, low cost, and ease of use. Utilizing our method, we have compiled a Wi-Fi tracking and localization dataset using the ESP32-S3 and a webcam. The code and dataset of this paper are available at https://github.com/RS2002/LoFi. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05074 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LoFi: Vision-Aided Label Generator for Wi-Fi Localization and Tracking Zhao, Zijian Chen, Tingwei Meng, Fanyi Cai, Zhijie Li, Hang Li, Xiaoyang Zhu, Guangxu Computer Vision and Pattern Recognition Signal Processing Data-driven Wi-Fi localization and tracking have shown great promise due to their lower reliance on specialized hardware compared to model-based methods. However, most existing data collection techniques provide only coarse-grained ground truth or a limited number of labeled points, significantly hindering the advancement of data-driven approaches. While systems like lidar can deliver precise ground truth, their high costs make them inaccessible to many users. To address these challenges, we propose LoFi, a vision-aided label generator for Wi-Fi localization and tracking. LoFi can generate ground truth position coordinates solely from 2D images, offering high precision, low cost, and ease of use. Utilizing our method, we have compiled a Wi-Fi tracking and localization dataset using the ESP32-S3 and a webcam. The code and dataset of this paper are available at https://github.com/RS2002/LoFi. |
| title | LoFi: Vision-Aided Label Generator for Wi-Fi Localization and Tracking |
| topic | Computer Vision and Pattern Recognition Signal Processing |
| url | https://arxiv.org/abs/2412.05074 |