Abstract
The adequate automatic detection of driver fatigue is a very valuable approach for the prevention of traffic accidents. Devices that can determine drowsiness conditions accurately must inherently be portable, adaptable to different vehicles and drivers, and robust to conditions such as illumination changes or visual occlusion. With the advent of a new generation of computationally powerful embedded systems such as the Raspberry Pi, a new category of real-time and low-cost portable drowsiness detection systems could become standard tools. Usually, the proposed solutions using this platform are limited to the definition of thresholds for some defined drowsiness indicator or the application of computationally expensive classification models that limits their use in real-time. In this research, we propose the development of a new portable, low-cost, accurate, and robust drowsiness recognition device. The proposed device combines complementary drowsiness measures derived from a temporal window of eyes (PERCLOS, ECD) and mouth (AOT) states through a fuzzy inference system deployed in a Raspberry Pi with the capability of real-time response. The system provides three degrees of drowsiness (Low-Normal State, Medium-Drowsy State, and High-Severe Drowsiness State), and was assessed in terms of its computational performance and efficiency, resulting in a significant accuracy of 95.5% in state recognition that demonstrates the feasibility of the approach.
Highlights
Driver fatigue and drowsiness constitute one of the leading causes of traffic accidents, being involved in 9.5% of crashes in the US [1] and 6% of fatal accidents in Brazil [2]
The proposed device combines complementary drowsiness measures derived from a temporal window of eyes (PERCLOS, eye closing duration (ECD)) and mouth (AOT) states through a fuzzy inference system deployed in a Raspberry Pi with the capability of real-time response
We investigate two approaches: a cascade classifier that employs Haar-like filter features as proposed in [34], and a linear support vector machine (SVM) with histogram of oriented gradients (HOG) features, as proposed in [35]
Summary
Driver fatigue and drowsiness constitute one of the leading causes of traffic accidents, being involved in 9.5% of crashes in the US [1] and 6% of fatal accidents in Brazil [2]. To alleviate these figures, authorities, research groups, and automobile manufacturers have concentrated their efforts on developing awareness campaigns, promoting the implementation and use of rest stops, and developing automatic devices that assist drivers by detecting fatigue or drowsiness [3]. There are three categories of drowsiness detection systems, based on the measures of these sensors [4]: Vehicle-Based, physiological, and behavioral. Physiological measures include electroencephalography (EEG), electrooculography (EoG), electrocardiography (ECG), and electromyography (EMG) signals, and systems detect their deviation from the characteristics of the subject’s standard signals, and
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