Neuromorphic Driver Monitoring Systems: A Proof-of-Concept for Yawn Detection and Seatbelt State Detection Using an Event Camera

Abstract

Driver monitoring systems (DMS) are a key component of vehicular safety and essential for the transition from semi-autonomous to fully autonomous driving. Neuromorphic vision systems, based on event camera technology, provide advanced sensing in motion analysis tasks. In particular, the behaviours of drivers’ eyes have been studied for the detection of drowsiness and distraction. This research explores the potential to extend neuromorphic sensing techniques to analyse the entire facial region, detecting yawning behaviours that give a complimentary indicator of drowsiness. A second proof of concept for the use of event cameras to detect the fastening or unfastening of a seatbelt is also developed. Synthetic training datasets are derived from RGB and Near-Infrared (NIR) video from both private and public datasets using a video-to-event converter and used to train, validate, and test a convolutional neural network (CNN) with a self-attention module and a recurrent head for both yawning and seatbelt tasks. For yawn detection, respective F1-scores of 95.3% and 90.4% were achieved on synthetic events from our test set and the “YawDD” dataset. For seatbelt fastness detection, 100% accuracy was achieved on unseen test sets of both synthetic and real events. These results demonstrate the feasibility to add yawn detection and seatbelt fastness detection components to neuromorphic DMS.

Keywords

• Driver monitoring
• drowsiness detection
• event camera
• computer vision
• CNN
• LSTM