This paper provides an automated system based on machine learning and computer vision to detect cellphone usage during driving. We used Wavelet Scattering Networks, which is a simple and efficient type of architecture. The presented model is straightforward and compact and requires little hyper-parameter tuning. The speed of this model is similar to the Convolutional Neural Networks. We monitored the driver from two viewpoints: a frontal view of the driver’s face and a side view of the driver’s whole body. We created a new dataset for the first viewpoint, and used a publicly available dataset for the second viewpoint. Our model achieved the test accuracy of 91% for our new dataset and 99% for the publicly available one.
Nahvi, A., Ebrahimian, S., & Besharati, A. (2023). Driver cellphone usage detection using wavelet scattering and convolutional neural networks. AUT Journal of Mathematics and Computing, (), -. doi: 10.22060/ajmc.2023.22580.1177
MLA
Ali Nahvi; Serajeddin Ebrahimian; Ali Besharati. "Driver cellphone usage detection using wavelet scattering and convolutional neural networks". AUT Journal of Mathematics and Computing, , , 2023, -. doi: 10.22060/ajmc.2023.22580.1177
HARVARD
Nahvi, A., Ebrahimian, S., Besharati, A. (2023). 'Driver cellphone usage detection using wavelet scattering and convolutional neural networks', AUT Journal of Mathematics and Computing, (), pp. -. doi: 10.22060/ajmc.2023.22580.1177
VANCOUVER
Nahvi, A., Ebrahimian, S., Besharati, A. Driver cellphone usage detection using wavelet scattering and convolutional neural networks. AUT Journal of Mathematics and Computing, 2023; (): -. doi: 10.22060/ajmc.2023.22580.1177
)