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Study on driver's distraction research trend and deep learning based behavior recognition model

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2021, 26(11), pp.173-182
  • DOI : 10.9708/jksci.2021.26.11.173
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : July 29, 2021
  • Accepted : November 18, 2021
  • Published : November 30, 2021

Sangkon Han 1 Jung-in Choi 2

1부산대학교
2아주대학교

Accredited

ABSTRACT

In this paper, we analyzed driver's and passenger's motions that cause driver's distraction, and recognized 10 driver's behaviors related to mobile phones. First, distraction-inducing behaviors were classified into environments and factors, and related recent papers were analyzed. Based on the analyzed papers, 10 driver's behaviors related to cell phones, which are the main causes of distraction, were recognized. The experiment was conducted based on about 100,000 image data. Features were extracted through SURF and tested with three models (CNN, ResNet-101, and improved ResNet-101). The improved ResNet-101 model reduced training and validation errors by 8.2 times and 44.6 times compared to CNN, and the average precision and f1-score were maintained at a high level of 0.98. In addition, using CAM (class activation maps), it was reviewed whether the deep learning model used the cell phone object and location as the decisive cause when judging the driver's distraction behavior.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.