| Item type |
学術雑誌論文 / Journal Article.(1) |
| 公開日 |
2021-03-09 |
| 書誌情報 |
en : IEEE Transactions on Emerging Topics in Computational Intelligence
巻 4,
号 3,
p. 206-215,
発行日 2019
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| タイトル |
|
|
タイトル |
WITM: Intelligent Traffic Monitoring Using Fine-Grained Wireless Signal |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Intelligent traffic monitoring |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
WiFi |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
CSI |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
machine learning |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 著者 |
CHEN, Caijuan
太田, 香
董, 冕雄
YU, Chen
JIN, Hai
|
| 室蘭工業大学研究者データベースへのリンク |
|
|
表示名 |
太田 香（OTA Kaoru） |
|
URL |
http://rdsoran.muroran-it.ac.jp/html/100000140_ja.html |
| 室蘭工業大学研究者データベースへのリンク |
|
|
表示名 |
董 冕雄（DONG Mianxiong） |
|
URL |
http://rdsoran.muroran-it.ac.jp/html/100000145_ja.html |
| 抄録 |
|
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内容記述タイプ |
Abstract |
|
内容記述 |
With the rapid development of the traffic volume, intelligent traffic monitoring technologies have attracted more and more attention, which can support a broad range of applications, including traffic congestion mitigation, traffic violation management, and automated driving assistance. Therefore, it is important to realize convenient, effective, and intelligent traffic monitoring at low cost. In this paper, we develop a comprehensive traffic monitoring system named WiFi-based intelligent traffic monitoring (WITM), which achieves vehicle detection, vehicle type classification, and vehicle speed estimation by measuring the changes of wireless channel state information. The system shows the advantages of convenient deployment, low cost and easy to expand. The proposed detection processes include three key components, a traffic detection method with moving variance, a convolutional neural network-based learning engine to classify the vehicle types, and a combination method of gradient-based and curve fitting to estimate the vehicle speed. By using the fine-grained wireless signal information, WITM achieves vehicle detection with the accuracy of 93.12% and differentiates vehicle types with an accuracy of 87.27%. In addition, the average error of the vehicle speed estimation is less than 5 km/h. |
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言語 |
en |
| 出版者 |
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出版者 |
IEEE |
|
言語 |
en |
| 出版者版へのリンク |
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表示名 |
10.1109/TETCI.2019.2926505 |
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URL |
https://doi.org/10.1109/TETCI.2019.2926505 |
| DOI |
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|
関連タイプ |
isVersionOf |
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|
識別子タイプ |
DOI |
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|
関連識別子 |
10.1109/TETCI.2019.2926505 |
| 日本十進分類法 |
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主題Scheme |
NDC |
|
主題 |
007 |
| 権利 |
|
|
言語 |
en |
|
権利情報 |
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| 著者版フラグ |
|
|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| フォーマット |
|
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内容記述タイプ |
Other |
|
内容記述 |
application/pdf |
TETCI_4_3_206_215 (3.8 MB)
