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The Earth Speaks: Advanced Vehicle Classification with Seismic Data
https://ipsj.ixsq.nii.ac.jp/records/233891
https://ipsj.ixsq.nii.ac.jp/records/23389181d1d9a5-ea0f-4347-8830-a0ed6c54c9b8
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
IPSJ-DPS24199024.pdf (480.0 kB)
2026年5月8日からダウンロード可能です。
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Copyright (c) 2024 by the Information Processing Society of Japan
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| 非会員：¥660, IPSJ:学会員：¥330, DPS:会員：¥0, DLIB:会員：¥0 | ||
| Item type | SIG Technical Reports(1) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2024-05-08 | |||||||||
| タイトル | ||||||||||
| タイトル | The Earth Speaks: Advanced Vehicle Classification with Seismic Data | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | The Earth Speaks: Advanced Vehicle Classification with Seismic Data | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | [MBL/ITS]自動運転と測位 | |||||||||
| 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 | ||||||||||
| Computational Learning Theory Team, RIKEN-AIP | ||||||||||
| 著者所属 | ||||||||||
| Graduate School of Information Science and Technology, Osaka University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Computational Learning Theory Team, RIKEN-AIP | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Graduate School of Information Science and Technology, Osaka University | ||||||||||
| 著者名 |
Sherief, Hashima
× Sherief, Hashima
× Hamada, Rizk
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| 著者名(英) |
Sherief, Hashima
× Sherief, Hashima
× Hamada, Rizk
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Recently, privacy-preserving vehicle classification (VC) methods have gained great focus, especially with the growing intelligent sensor technologies. Amongst these, seismic-aided VC is a promising intelligent solution but a challenging issue due to the interference of signals sourced from various noises. Seismic-aided VC overcomes the challenges of image/video classification as it can easily/low cost detect the traffic volume without private user information and in different weather conditions. Therefore, this paper proposes an efficient deep learning-based vehicle classification approach within the fractional wavelet domain to identify vehicle categories. The system achieves a high classification accuracy of 98% with real-time processing of 0.05 seconds. | |||||||||
| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Recently, privacy-preserving vehicle classification (VC) methods have gained great focus, especially with the growing intelligent sensor technologies. Amongst these, seismic-aided VC is a promising intelligent solution but a challenging issue due to the interference of signals sourced from various noises. Seismic-aided VC overcomes the challenges of image/video classification as it can easily/low cost detect the traffic volume without private user information and in different weather conditions. Therefore, this paper proposes an efficient deep learning-based vehicle classification approach within the fractional wavelet domain to identify vehicle categories. The system achieves a high classification accuracy of 98% with real-time processing of 0.05 seconds. | |||||||||
| 書誌レコードID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AN10116224 | |||||||||
| 書誌情報 |
研究報告マルチメディア通信と分散処理（DPS） 巻 2024-DPS-199, 号 24, p. 1-7, 発行日 2024-05-08 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 2188-8906 | |||||||||
| 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 | |||||||||
