University of Hertfordshire

By the same authors

Detecting Distracted Driving with Deep Learning

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

Documents

  • 1LM-1

    Accepted author manuscript, 272 KB, PDF document

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Original languageEnglish
Title of host publicationInteractive Collaborative Robotics
Subtitle of host publicationProceeding of ICR 2017
EditorsAndrey Ronzhin, Gerhard Rigoll, Roman Meshcheryakov
PublisherSpringer International Publishing
Pages170-179
ISBN (Electronic)978-3-319-66471-2
ISBN (Print)978-3-319-66470-5
DOIs
Publication statusE-pub ahead of print - 11 Sep 2017
EventThe 2nd International Conference on Interactive Collaborative Robotics - Hatfield, United Kingdom
Duration: 12 Sep 201716 Sep 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
CountryUnited Kingdom
CityHatfield
Period12/09/1716/09/17
Internet address

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.

Notes

© Springer International Publishing AG 2017

ID: 12491556