Event cameras contain emerging, neuromorphic vision sensors that capture local-light intensity changes at each pixel, generating a stream of asynchronous events. This way of acquiring visual information constitutes a departure from traditional frame-based cameras and offers several significant advantages - low energy consumption, high temporal resolution, high dynamic range and low latency. Driver monitoring systems (DMS) are in-cabin safety systems designed to sense and understand a drivers physical and cognitive state. Event cameras are particularly suited to DMS due to their inherent advantages. This paper proposes a novel method to simultaneously detect and track faces and eyes for driver monitoring. A unique, fully convolutional recurrent neural network architecture is presented. To train this network, a synthetic event-based dataset is simulated with accurate bounding box annotations, called Neuromorphic-HELEN. Additionally, a method to detect and analyse drivers' eye blinks is proposed, exploiting the high temporal resolution of event cameras. Behaviour of blinking provides greater insights into a driver level of fatigue or drowsiness. We show that blinks have a unique temporal signature that can be better captured by event cameras.
Keywords: Convolutional neural network; Driver monitoring system; Event cameras.
Copyright © 2021. Published by Elsevier Ltd.