A survey on visual and non-visual features in Driver’s drowsiness detection

Pandey, Nageshwar Nath; Muppalaneni, Naresh Babu

doi:10.1007/s11042-022-13150-1

Cited by 7 publications

(1 citation statement)

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“…Compared with EOG-based eye feature measurement, camera-based devices can be installed inside the cabin to provide a remote sensing solution that requires no physical contact with the driver (see [ 21 ] for a recent review). In the past two decades, various algorithms have been developed to retrieve visual features that are sensitive to driver drowsiness.…”

Section: Related Workmentioning

confidence: 99%

A CNN-Based Wearable System for Driver Drowsiness Detection

Zhang

Zhu

et al. 2023

Sensors

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Drowsiness poses a serious challenge to road safety and various in-cabin sensing technologies have been experimented with to monitor driver alertness. Cameras offer a convenient means for contactless sensing, but they may violate user privacy and require complex algorithms to accommodate user (e.g., sunglasses) and environmental (e.g., lighting conditions) constraints. This paper presents a lightweight convolution neural network that measures eye closure based on eye images captured by a wearable glass prototype, which features a hot mirror-based design that allows the camera to be installed on the glass temples. The experimental results showed that the wearable glass prototype, with the neural network in its core, was highly effective in detecting eye blinks. The blink rate derived from the glass output was highly consistent with an industrial gold standard EyeLink eye-tracker. As eye blink characteristics are sensitive measures of driver drowsiness, the glass prototype and the lightweight neural network presented in this paper would provide a computationally efficient yet viable solution for real-world applications.

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Section: Related Workmentioning

confidence: 99%