Citation

Roy K. IEEE Trans. Intel. Transp. Syst. 2022; 23(10): 18200-18209.

Copyright

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

DOI

10.1109/TITS.2022.3151752

PMID

unavailable

Abstract

One of the most significant problem in traffic accidents is distracted driving that causes a higher number of deaths and injury every year. In recent times car manufacturing companies are integrating driver distraction detection systems for detecting distracted behavior. In this work we have suggested an mobile usage detection system in driving images taken from inside of a car. Previously proposed distracted driver detection systems that focus only on gaze and head poses. We have suggested another alternative using driver hand images by which distracted driving detection system can be benefited. If the driver looks at the road ahead, previous head pose and gaze based systems are unable to detect distractions caused by cell phone use. To reliably detect these modes of distraction, we trained an unsupervised low rank nonnegative dictionary and used a threshold based criterion over reconstruction error. We have created a new driving dataset based on YouTube videos that includes real-world driving scenarios as well as mobile phone usage while driving. We have also tested our method using a number of publicly available driving datasets, including the State farm driving dataset, the AUC driving dataset, the VIVA dataset, and the CVRR-3D dataset. According to our knowledge of the current literature, this method is the first to use an unsupervised approach to address the problem of cell phone usage while driving. We achieved a competitive test accuracy of 79.17%, 77.19%, 84.0%, 76.0%, 75.45% in the proposed Youtube ego driving, State farm driving, CVRR-3D, VIVA, AUC distracted driver dataset, respectively.

Language: en

Keywords

Accidents; Cameras; Cellular phones; dictionary learning; Driver cell phone usage detection; driver monitoring; Feature extraction; low rank; Mobile handsets; nonnegative representation; sparse representation; unsupervised learning; Vehicles; Wheels