Detecting Distracted Driving with Deep Learning

Ofonime Dominic Okon; Li Meng

doi:10.1007/978-3-319-66471-2

Detecting Distracted Driving with Deep Learning

Ofonime Dominic Okon, Li Meng

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

25 Citations (Scopus)

104 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 language	English
Title of host publication	Interactive Collaborative Robotics
Subtitle of host publication	Proceeding of ICR 2017
Editors	Andrey Ronzhin, Gerhard Rigoll, Roman Meshcheryakov
Publisher	Springer Nature
Pages	170-179
ISBN (Electronic)	978-3-319-66471-2
ISBN (Print)	978-3-319-66470-5
DOIs	https://doi.org/10.1007/978-3-319-66471-2
Publication status	E-pub ahead of print - 11 Sept 2017
Event	The 2nd International Conference on Interactive Collaborative Robotics - Hatfield, United Kingdom Duration: 12 Sept 2017 → 16 Sept 2017 http://specom.nw.ru/history/sites/2017/icr2017.html

Publication series

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

Conference

Conference	The 2nd International Conference on Interactive Collaborative Robotics
Country/Territory	United Kingdom
City	Hatfield
Period	12/09/17 → 16/09/17
Internet address	http://specom.nw.ru/history/sites/2017/icr2017.html

Keywords

Distraction Detection
Convolutional Neural Networks
Computer Vision

Access to Document

10.1007/978-3-319-66471-2Licence: Other

1LM-1Accepted author manuscript, 272 KBLicence: Other

Cite this

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title = "Detecting Distracted Driving with Deep Learning",

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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Okon, OD & Meng, L 2017, Detecting Distracted Driving with Deep Learning. in A Ronzhin, G Rigoll & R Meshcheryakov (eds), Interactive Collaborative Robotics: Proceeding of ICR 2017. Lecture Notes in Computer Science book series (LNCS, volume 10459), Springer Nature , pp. 170-179, The 2nd International Conference on Interactive Collaborative Robotics, Hatfield, United Kingdom, 12/09/17. https://doi.org/10.1007/978-3-319-66471-2

Detecting Distracted Driving with Deep Learning. / Okon, Ofonime Dominic; Meng, Li.
Interactive Collaborative Robotics: Proceeding of ICR 2017. ed. / Andrey Ronzhin; Gerhard Rigoll; Roman Meshcheryakov. Springer Nature , 2017. p. 170-179 ( Lecture Notes in Computer Science book series (LNCS, volume 10459)).

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

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AB - 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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Detecting Distracted Driving with Deep Learning

Abstract

Publication series

Conference

Keywords

Access to Document

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