@techreport{oai:ipsj.ixsq.nii.ac.jp:00233945,
 author = {Sherief, Hashima and Hamada, Rizk and Sherief, Hashima and Hamada, Rizk},
 issue = {24},
 month = {May},
 note = {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., 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.},
 title = {The Earth Speaks: Advanced Vehicle Classification with Seismic Data},
 year = {2024}
}