Detecting Distracted Driving with Deep Learning

Ofonime Dominic Okon, Li Meng

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

7 Citations (Scopus)
45 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationInteractive Collaborative Robotics
Subtitle of host publicationProceeding of ICR 2017
EditorsAndrey Ronzhin, Gerhard Rigoll, Roman Meshcheryakov
PublisherSpringer Nature
Pages170-179
ISBN (Electronic)978-3-319-66471-2
ISBN (Print)978-3-319-66470-5
DOIs
Publication statusE-pub ahead of print - 11 Sept 2017
EventThe 2nd International Conference on Interactive Collaborative Robotics - Hatfield, United Kingdom
Duration: 12 Sept 201716 Sept 2017
http://specom.nw.ru/history/sites/2017/icr2017.html

Publication series

Name Lecture Notes in Computer Science book series (LNCS, volume 10459)
PublisherSpringer

Conference

ConferenceThe 2nd International Conference on Interactive Collaborative Robotics
Country/TerritoryUnited Kingdom
CityHatfield
Period12/09/1716/09/17
Internet address

Keywords

  • Distraction Detection
  • Convolutional Neural Networks
  • Computer Vision

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