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Driver cellphone usage detection using wavelet scattering and convolutional neural networks | ||
| AUT Journal of Mathematics and Computing | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 10 شهریور 1402 | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajmc.2023.22580.1177 | ||
| نویسندگان | ||
| Ali Nahvi* ؛ Serajeddin Ebrahimian؛ Ali Besharati | ||
| Virtual Reality Laboratory, K.N. Toosi University of Technology, Tehran, Iran | ||
| چکیده | ||
| 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. | ||
| کلیدواژه‌ها | ||
| Mobile use detection؛ Wavelet scattering network؛ CNN؛ Cascade object detector؛ Transfer learning | ||
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آمار تعداد مشاهده مقاله: 224 |
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