Driver Drowsiness Detection System Using Conventional Machine Learning

Madireddy, Radheswarreddy; Anudeep, Dulla Sai Krishna; Poorna, S. S.; Anuraj, K; Krishna, M. Gokul; Balaji, A. Jayanth; Venkat, Dammuru Jaideep

doi:10.1007/978-981-15-9647-6_31

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Bamidele et al [ 24 ] designed a non-intrusive and low-cost driver drowsiness detection solution based on face and eye tracking. Madireddy et al [ 25 ] built a non-intrusive drowsiness detection system based on Raspberry Pi and OpenCV. In their system, an SVM was employed to extract visual features, such as eye and mouth aspect ratios, blink rate, and yawning rate.…”

Section: Related Workmentioning

confidence: 99%

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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%

“…Eye and mouth features are the most frequently used features for remote camera-based drowsiness detections [ 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. In this type of system, face landmarks are extracted as the first step to identify eye and mouth features.…”

Section: Related Workmentioning

confidence: 99%