Driver Head Pose Detection From Naturalistic Driving Data

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.

Automated summary

Driver behavior analysis is crucial in driver assistance systems, especially in understanding the driver's attention and concentration. However, data collected in naturalistic driving studies (NDS) often have lower quality videos and more challenging camera positions. In this study, we propose three approaches using facial key points to classify a driver's head pose from side-view videos. We compare these approaches with baselines using leave-one-driver-out cross-validation and find that the proposed method using BiGRU achieves an 11% improvement in overall accuracy compared to the best performing baseline.