Many individuals lose their lives in traffic accidents. Sleepy driving is one of the leading causes to road accidents and mortality. Frequently, fatigue and micro nap at the wheel are the underlying causes of major accidents. Before a dangerous scenario arises, early symptoms of drowsiness can be identified. The majority of conventional approaches for detecting sleepiness are based on behavioural factors. So, a light weight real time drowsiness detection model employing Deep Learning algorithms are used to detect the drowsiness of the drivers. Based on adaptive threshold technique, the system detects the sleepiness of the driver by identifying facial landmarks. This model can be mounted in Raspberry Pi for the real time monitoring. Based on the threshold value of the output, the buzzer warning will be given to the drivers to detect the drowsy state of the driver.
Driver Drowsiness Detection using Deep Learning
29.03.2022
1327093 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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