@article{oai:muroran-it.repo.nii.ac.jp:00010429,
 author = {CHEN, Caijuan and OTA,  Kaoru and 太田, 香 and DONG,  Mianxiong and 董, 冕雄 and YU, Chen and JIN, Hai},
 issue = {3},
 journal = {IEEE Transactions on Emerging Topics in Computational Intelligence},
 month = {},
 note = {application/pdf, 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.},
 pages = {206--215},
 title = {WITM: Intelligent Traffic Monitoring Using Fine-Grained Wireless Signal},
 volume = {4},
 year = {2019},
 yomi = {オオタ, カオル and トウ, メンユウ}
}