Citation

Wagner B, Taffner F, Karaca S, Karge L. IEEE Trans. Intel. Transp. Syst. 2022; 23(5): 4257-4266.

Copyright

(Copyright © 2022, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2020.3043145

PMID

unavailable

Abstract

Distracted driving is a problem which yearly causes a large amount of road traffic crashes with high rates of fatalities and injured persons. Recently, car manufacturers started to integrate driver monitoring systems to detect visual distraction. This paper proposes a method to extend such systems by driver posture classification to detect driver cell phone usage and food consumption. Such an extension can be beneficial since systems that focus on the detection of visual distraction mainly rely on head pose and gaze information. Thus, distraction caused by cell phone usage or food consumption can not be detected by these systems when the driver is looking to the road ahead. To robustly detect those types of manual and cognitive distraction, different deep learning models were trained and evaluated based on a new image dataset which was captured by two infrared cameras to ensure that a large range of head angles can be covered by the system. Separate Convolutional Neural Networks (CNNs) were trained and evaluated for the dataset of the left and the right camera to optimize the classification accuracy. The trained CNNs revealed a competitive test accuracy of 92.88% and 90.36% for the left and the right camera, respectively. In inference mode, the models achieve a frame rate of 44Hz and 28Hz for the left and the right camera, respectively. The combination of the classification output of both networks revealed a test accuracy of 92.54%.

Language: en

Keywords

Cameras; Monitoring; Roads; Vehicles; Head; deep learning; Visualization; Cellular phones; Driver monitoring; driver posture classification