# Detection of Driver Drowsiness by Calculating the Speed of Eye Blinking

arXiv: https://arxiv.org/abs/2110.11223 · HTML: https://mlsanity.com/paper/2110.11223/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 21 Oct 2021.

## Builds on (most cited first, 2 of 2)

- [SVIRO: Synthetic Vehicle Interior Rear Seat Occupancy Dataset and Benchmark](https://mlsanity.com/paper/2001.03483/) (2020)
- [DFKI Cabin Simulator: A Test Platform for Visual In-Cabin Monitoring Functions](https://mlsanity.com/paper/2002.03749/) (2020)

## Built on by (most cited first, 1 of 1)

- [Applying Spatiotemporal Attention to Identify Distracted and Drowsy Driving with Vision Transformers](https://mlsanity.com/paper/2207.12148/) (2022)

## Abstract (arXiv)

Many road accidents are caused by drowsiness of the driver. While there are methods to detect closed eyes, it is a non-trivial task to detect the gradual process of a driver becoming drowsy. We consider a simple real-time detection system for drowsiness merely based on the eye blinking rate derived from the eye aspect ratio. For the eye detection we use HOG and a linear SVM. If the speed of the eye blinking drops below some empirically determined threshold, the system triggers an alarm, hence preventing the driver from falling into microsleep. In this paper, we extensively evaluate the minimal requirements for the proposed system. We find that this system works well if the face is directed to the camera, but it becomes less reliable once the head is tilted significantly. The results of our evaluations provide the foundation for further developments of our drowsiness detection system.