Real-time face & eye tracking and blink detection using event cameras
| Autor: | Etienne Perot, Aisling Cahill, Joseph Lemley, Cian Ryan, Christoph Posch, Amr Elrasad, Paul Kielty, Brian O'Sullivan |
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| Rok vydání: | 2021 |
| Předmět: |
Male
0209 industrial biotechnology Computer science Cognitive Neuroscience ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Convolutional neural network 020901 industrial engineering & automation Artificial Intelligence Minimum bounding box Photography 0202 electrical engineering electronic engineering information engineering Humans Computer vision Eye-Tracking Technology Blinking Pixel business.industry Event (computing) Frame (networking) Recurrent neural network Neuromorphic engineering Eye tracking 020201 artificial intelligence & image processing Neural Networks Computer Artificial intelligence business |
| Zdroj: | Neural Networks. 141:87-97 |
| ISSN: | 0893-6080 |
| DOI: | 10.1016/j.neunet.2021.03.019 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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