Drive-net: Convolutional network for driver distraction detection

Mohammed S. Majdi, Sundaresh Ram, Jonathan T. Gill, Jeffrey J. Rodriguez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

To help prevent motor vehicle accidents, there has been significant interest in finding an automated method to recognize signs of driver distraction, such as talking to passengers, fixing hair and makeup, eating and drinking, and using a mobile phone. In this paper, we present an automated supervised learning method called Drive-Net for driver distraction detection. Drive-Net uses a combination of a convolutional neural network (CNN) and a random decision forest for classifying images of a driver. We compare the performance of our proposed Drive-Net to two other popular machine-learning approaches: a recurrent neural network (RNN), and a multi-layer perceptron (MLP). We test the methods on a publicly available database of images acquired under a controlled environment containing about 22425 images manually annotated by an expert. Results show that Drive-Net achieves a detection accuracy of 95%, which is 2% more than the best results obtained on the same database using other methods.

Original languageEnglish (US)
Title of host publication2018 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages69-72
Number of pages4
ISBN (Electronic)9781538665688
DOIs
StatePublished - Sep 21 2018
Event2018 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2018 - Las Vegas, United States
Duration: Apr 8 2018Apr 10 2018

Publication series

NameProceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation
Volume2018-April

Other

Other2018 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2018
CountryUnited States
CityLas Vegas
Period4/8/184/10/18

Keywords

  • Image classification
  • convolutional neural networks
  • driver distraction
  • random forest

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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  • Cite this

    Majdi, M. S., Ram, S., Gill, J. T., & Rodriguez, J. J. (2018). Drive-net: Convolutional network for driver distraction detection. In 2018 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2018 - Proceedings (pp. 69-72). [8470309] (Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation; Vol. 2018-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSIAI.2018.8470309