Active Vision-based Attention Monitoring System for Non-Distracted Driving

Lamia Alam, Mohammed Moshiul Hoque, M. Ali Akber Dewan, Nazmul Siddique, Inaki Rano*, Iqbal H. Sarker

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Inattentive driving is a key reason of road mishaps causing more deaths than speeding or drunk driving. Research efforts have been made to monitor drivers’ attentional states and provide support to drivers. Both invasive and non-invasive methods have been applied to track driver’s attentional states, but most of these methods either use exclusive equipment which are costly or use sensors that cause discomfort. In this paper, a vision-based scheme is proposed for monitoring the attentional states of the drivers. The system comprises four major modules such as cue extraction and parameter estimation, monitoring and decision making, level of attention estimation, and alert system. The system estimates the attentional level and classifies the attentional states based on the percentage of eyelid closure over time (PERCLOS), the frequency of yawning and gaze direction. Various experiments were conducted with human participants to assess the performance of the suggested scheme, which demonstrates the system’s effectiveness with 92% accuracy.

Original languageEnglish
JournalIEEE Access
Pages (from-to)28540-28557
Publication statusPublished - 2021


  • attention monitoring
  • attentional states
  • Biomedical monitoring
  • Computer vision
  • driving assistance
  • Fatigue
  • gaze direction
  • human-computer interaction
  • Monitoring
  • Roads
  • Sensors
  • Vehicles
  • Visualization


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