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Detecting Distracted Driving with Deep Learning

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Interactive Collaborative Robotics (ICR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10459))

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Abstract

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.

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Authors and Affiliations

  1. School of Engineering and Technology, University of Hertfordshire, Hatfield, UK

    Ofonime Dominic Okon & Li Meng

Authors
  1. Ofonime Dominic Okon
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  2. Li Meng
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Corresponding author

Correspondence to Li Meng .

Editor information

Editors and Affiliations

  1. SPIIRAS , St. Petersburg, Russia

    Andrey Ronzhin

  2. Technical University of Munich, Munich, Germany

    Gerhard Rigoll

  3. Tomsk State University , Tomsk, Russia

    Roman Meshcheryakov

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Okon, O.D., Meng, L. (2017). Detecting Distracted Driving with Deep Learning. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds) Interactive Collaborative Robotics. ICR 2017. Lecture Notes in Computer Science(), vol 10459. Springer, Cham. https://doi.org/10.1007/978-3-319-66471-2_19

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  • DOI: https://doi.org/10.1007/978-3-319-66471-2_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66470-5

  • Online ISBN: 978-3-319-66471-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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