Driver distraction detection using capsule network

Deepak Kumar Jain, Rachna Jain*, Xiangyuan LAN, Yash Upadhyay, Anuj Thareja

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

With the onset of the new technological age, the distractions caused due to handheld devices have been a major cause of traffic accidents as they affect the decision-making capabilities of the driver and give them less time to react to difficult situations. Often drivers try to multitask which reduces their reaction time leading to accidents which could have been easily avoided if they had been attentive. As such problems are related to the driver’s negligence toward safety, a possible solution is to monitor driver’s behavior and notify if they are distracted. We propose a CapsNet-based approach for detecting the distracted driver which is a novel approach. The proposed method scores perform well on the real-world environment inputs when compared to other famous methods used for the same. Our proposed methods get high scores for all the most commonly used metrics for classification. On the testing set, the proposed method gets an accuracy of 0.90, 0.92 as precision score, 0.90 as recall score and 0.91 as F-measure.

Original languageEnglish
JournalNeural Computing and Applications
DOIs
Publication statusAccepted/In press - 2020

Scopus Subject Areas

  • Software
  • Artificial Intelligence

User-Defined Keywords

  • CapsNet
  • Driver distraction
  • Dynamic routing
  • Posture classification

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