Preliminary Investigation of Estimating Depth Images of Moving Objects from Wi-Fi Channel State Information

• Item type: SIG Technical Reports(1)
• 公開日: 2023-09-18
• タイトル: Preliminary Investigation of Estimating Depth Images of Moving Objects from Wi-Fi Channel State Information
• タイトル / 言語: en
• タイトル: Preliminary Investigation of Estimating Depth Images of Moving Objects from Wi-Fi Channel State Information
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: 測位技術
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Graduate School of Information Science and Technology, Osaka University
• 著者所属: Graduate School of Information Science and Technology, Osaka University
• 著者所属: NTT Communication Science Laboratories
• 著者所属: NTT Communication Science Laboratories
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): NTT Communication Science Laboratories
• 著者所属(英): en
• 著者所属(英): NTT Communication Science Laboratories
• 著者名: Guanyu, Cao; Takuya, Maekawa; Kazuya, Ohara; Yasue, Kishino
• 著者名(英): Guanyu, Cao; Takuya, Maekawa; Kazuya, Ohara; Yasue, Kishino
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: This paper explores a practical approach to estimate depth images of moving humans and objects by Wi-Fi Channel State Information (CSI). This technique enables visual sensing with commercial off-the-shelf Wi-Fi devices that saves expense while being invariant to illumination and occlusion, and friendly to privacy. However, training a cross-modality model usually requires a large amount of paired data. The variety of human postures further exacerbates the data requirement. We leverage Variational Auto-Encoder (VAE) to learn the regularized latent space of depth images in order to estimate unseen images. Besides, we adopt metric learning to learn the physical attributes of depth images. These strategies mitigate the labor of collecting paired data, and enable the use of in-the-wild depth image datasets. Concretely, a teacher-student network is established, where the teacher network is based on Beta-VAE that learns the latent space of depth images with the help of inductive bias containing shape, size, planar coordinates and depth coordinates. The student network is an encoder that learns such latent space via knowledge distillation. The student network and the decoder of the teacher network constitute an end-to-end depth image estimation network from CSI. To the best of our knowledge, our proposed model is the first to estimate the depth images from CSI.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: This paper explores a practical approach to estimate depth images of moving humans and objects by Wi-Fi Channel State Information (CSI). This technique enables visual sensing with commercial off-the-shelf Wi-Fi devices that saves expense while being invariant to illumination and occlusion, and friendly to privacy. However, training a cross-modality model usually requires a large amount of paired data. The variety of human postures further exacerbates the data requirement. We leverage Variational Auto-Encoder (VAE) to learn the regularized latent space of depth images in order to estimate unseen images. Besides, we adopt metric learning to learn the physical attributes of depth images. These strategies mitigate the labor of collecting paired data, and enable the use of in-the-wild depth image datasets. Concretely, a teacher-student network is established, where the teacher network is based on Beta-VAE that learns the latent space of depth images with the help of inductive bias containing shape, size, planar coordinates and depth coordinates. The student network is an encoder that learns such latent space via knowledge distillation. The student network and the decoder of the teacher network constitute an end-to-end depth image estimation network from CSI. To the best of our knowledge, our proposed model is the first to estimate the depth images from CSI.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11838947
• 書誌情報: 研究報告ユビキタスコンピューティングシステム（UBI）; 巻 2023-UBI-79, 号 31, p. 1-8, 発行日 2023-09-18
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8698
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会