Neuromorphic Sensing for Yawn Detection in Driver Drowsiness

TL;DR

This paper demonstrates that neuromorphic vision systems can effectively detect yawning behaviors in drivers, providing a promising method for assessing driver drowsiness in autonomous vehicle safety systems.

Contribution

The study extends neuromorphic sensing from facial features to full-face yawning detection, introducing a new dataset and a CNN model with self-attention for this purpose.

Findings

Achieved over 95% precision and recall on the test set.

Demonstrated robustness with simulated public dataset.

Validated feasibility of neuromorphic yawning detection in driver monitoring.

Abstract

Driver monitoring systems (DMS) are a key component of vehicular safety and essential for the transition from semiautonomous to fully autonomous driving. A key task for DMS is to ascertain the cognitive state of a driver and to determine their level of tiredness. Neuromorphic vision systems, based on event camera technology, provide advanced sensing of facial characteristics, in particular the behavior of a driver's eyes. This research explores the potential to extend neuromorphic sensing techniques to analyze the entire facial region, detecting yawning behaviors that give a complimentary indicator of tiredness. A neuromorphic dataset is constructed from 952 video clips (481 yawns, 471 not-yawns) captured with an RGB color camera, with 37 subjects. A total of 95200 neuromorphic image frames are generated from this video data using a video-to-event converter. From these data 21 subjects were selected to provide a training dataset, 8 subjects were used for validation data, and the remaining 8 subjects were reserved for an "unseen" test dataset. An additional 12300 frames were generated from event simulations of a public dataset to test against other methods. A CNN with self-attention and a recurrent head was designed, trained, and tested with these data. Respective precision and recall scores of 95.9 percent and 94.7 percent were achieved on our test set, and 89.9 percent and 91 percent on the simulated public test set, demonstrating the feasibility to add yawn detection as a sensing component of a neuromorphic DMS.