Abstract
the sleeping driver is potentially more likely to cause an accident than the person who speeds up since the driver is the victim of sleepiness. Automobile industry researchers, including manufacturers, seek to solve this issue with various technical solutions that can avoid such a situation. This paper proposes an implementation of a lightweight method to detect driver's sleepiness using facial landmarks and head pose estimation based on neural network methodologies on a mobile device. We try to improve the accurateness by using face images that the camera detects and passes to CNN to identify sleepiness. Firstly, applied a behavioral landmark's sleepiness detection process. Then, an integrated Head Pose Estimation technique will strengthen the system's reliability. The preliminary findings of the tests demonstrate that with real-time capability, more than 86% identification accuracy can be reached in several real-world scenarios for all classes, including with glasses, without glasses, and light-dark background. This work aims to classify drowsiness, warn, and inform drivers, helping them to stop falling asleep at the wheel. The integrated CNN-based method is used to create a high accuracy and simple-to-use real-time driver drowsiness monitoring framework for embedded devices and Android phones
Highlights
We proposed the behavioral method of sleepiness detection using a mobile phone frontal camera by utilizing facial landmarks passed to a convolutional neural networks (CNN) for drowsiness classification
This input runs into the neural network, and the algorithm determines whether the driver is sleepy
This paper proposed an enhanced sleepiness approach that relies upon CNN-based Computer Vision
Summary
The key factors that endanger road safety are drowsiness or fatigue, which causes serious accidents, fatalities, and economic losses. One more study report that an estimated $60.4 billion a year is spent by the US government and companies on sleepy driving injuries It financially harms the customer by about $ 16.4 billion like in property loss, medical claims, time loss, and daily life productivity because of traffic road accidents [2]. German Council of Traffic Safety (DVR) argues that one in every four road traffic deaths was caused by drivers' momentary drowsiness [3]. Such as in the year 2010, 54 percent of young drivers drove vehicles when feeling sleepy, and 28 percent fell asleep. The accomplishment of this work is the ability to provide heavier classification models with a lightweight alternative
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