Real-Time Face & Eye Tracking and Blink Detection using Event Cameras
This work addresses driver safety by enabling more efficient and accurate monitoring of fatigue or drowsiness, though it is incremental as it applies existing event camera advantages to a specific domain.
The paper tackled real-time face and eye tracking with blink detection for driver monitoring using event cameras, achieving results through a novel fully convolutional recurrent neural network trained on a synthetic dataset, Neuromorphic HELEN, and exploiting the high temporal resolution to capture unique blink signatures.
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.