Detecting Distracted Driving with Deep Learning
Okon, Ofonime Dominic and Meng, Li
(2017)
Detecting Distracted Driving with Deep Learning.
In:
Interactive Collaborative Robotics : Proceeding of ICR 2017.
Lecture Notes in Computer Science book series (LNCS, volume 10459)
.
Springer Nature, GBR, pp. 170-179.
ISBN 978-3-319-66470-5
Driver distraction is the leading factor in most car crashes and near-crashes. This paper discusses the types, causes and impacts of distracted driving. A deep learning approach is then presented for the detection of such driving behaviors using images of the driver, where an enhancement has been made to a standard convolutional neural network (CNN). Experimental results on Kaggle challenge dataset have confirmed the capability of a convolutional neural network (CNN) in this complicated computer vision task and illustrated the contribution of the CNN enhancement to a better pattern recognition accuracy.
Item Type | Book Section |
---|---|
Additional information | © Springer International Publishing AG 2017 |
Keywords | distraction detection, convolutional neural networks, computer vision |
Date Deposited | 15 May 2025 16:40 |
Last Modified | 04 Jun 2025 17:06 |
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