Traffic monitoring system based on deep learning and seismometer data

Ahmad Bahaa Ahmad, Takeshi Tsuji

研究成果: ジャーナルへの寄稿学術誌査読

4 被引用数 (Scopus)


Currently, vehicle classification in roadway-based techniques depends mainly on pho-tos/videos collected by an over-roadway camera or on the magnetic characteristics of vehicles. However, camera-based techniques are criticized for potentially violating the privacy of vehicle occupants and exposing their identity, and vehicles can evade detection when they are obscured by larger vehicles. Here, we evaluate methods of identifying and classifying vehicles on the basis of seismic data. Vehicle identification from seismic signals is considered a difficult task because of interference by various noise. By analogy with techniques used in speech recognition, we used different artificial intelligence techniques to extract features of three, different-sized vehicles (buses, cars, motorcycles) and seismic noise. We investigated the application of a deep neural network (DNN), a convolutional neural network (CNN), and a recurrent neural network (RNN) to classify vehicles on the basis of vertical-component seismic data recorded by geophones. The neural networks were trained on 5580 unprocessed seismic records and achieved excellent training accuracy (99%). They were also tested on large datasets representing periods as long as 1 month to check their stability. We found that CNN was the most satisfactory approach, reaching 96% accuracy and detecting multiple vehicle classes at the same time at a low computational cost. Our findings show that seismic methods can be used for traffic monitoring and security purposes without violating the privacy of vehicle occupants, offering greater efficiency and lower costs than current methods. A similar approach may be useful for other types of transportation, such as vessels and airplanes.

ジャーナルApplied Sciences (Switzerland)
出版ステータス出版済み - 5月 2 2021

!!!All Science Journal Classification (ASJC) codes

  • 材料科学(全般)
  • 器械工学
  • 工学(全般)
  • プロセス化学およびプロセス工学
  • コンピュータ サイエンスの応用
  • 流体および伝熱


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