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The Journal of Supercomputing

, Volume 75, Issue 1, pp 189–196 | Cite as

Detection of centerline crossing in abnormal driving using CapsNet

  • Minjong Kim
  • Suyoung ChiEmail author
Article
  • 172 Downloads

Abstract

This paper presents the detection of centerline crossing in abnormal driving using a CapsNet. The benefit of the CapsNet is that the capsule contains all the data about the status of objects and recognizes objects as vectors; hence, these can be used to classify driving as normal or abnormal. The datasets use the Creative Commons Licenses from YouTube to obtain traffic accident footages and six time-flow images composed of data with our quantitative basis. A comparison of our proposed architecture with the CNN model showed that our method produces better results.

Keywords

CapsNet Abnormal driving detection Deep convolutional neural network Dynamic routing between capsules 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Korea University of Science and TechnologyDaejeonKorea
  2. 2.Electronics and Telecommunications Research InstituteDaejeonKorea

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