Driver Head Pose Detection from Naturalistic Driving Data

Weiheng Chai, Jiajing Chen, Jiyang Wang, Senem Velipasalar, Archana Venkatachalapathy, Yaw Adu-Gyamfi, Jennifer Merickel, Anuj Sharma

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Driver behavior analysis plays an important role in driver assistance systems. A driver's face and head pose hold the key towards understanding whether the driver's attention and concentration are on the road while driving. Naturalistic driving studies (NDS) allow observing drivers in real-time under naturalistic traffic conditions. Yet, data collected in NDS often comprise low-resolution videos usually with more challenging camera positions compared to controlled studies. For instance, when the camera is not directly facing the driver, classifying head pose becomes more challenging, since the variation between different classes becomes much smaller. In this paper, we propose three different approaches to classify a driver's head pose from naturalistic videos, which were captured by a camera providing a side view, instead of directly facing the driver. These approaches employ a sequence of five key points on the driver's face. We compare these three proposed approaches with each other as well as with three different baselines by using leave-one-driver-out cross-validation on nine different drivers. Results show that our proposed method employing a Bidirectional Gated Recurrent Unit (BiGRU) outperforms the best performing baseline by 11% in terms of overall accuracy.

Original languageEnglish (US)
Pages (from-to)9368-9377
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Volume24
Issue number9
DOIs
StatePublished - Sep 1 2023

Keywords

  • Driver head pose
  • head orientation
  • in-vehicle camera
  • naturalistic data

ASJC Scopus subject areas

  • Mechanical Engineering
  • Automotive Engineering
  • Computer Science Applications

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