We hear a lot about accidents in our day-to-day life. In India, a person dies in every 4 min due to road accidents, which is the highest in our world. Though there are several reasons for accidents to occur, one of the main reasons can be the drowsiness of the driver. Drowsiness and weariness are among the significant causes of the road accidents. This paper gives an insight about how we can detect the drowsiness of a driver. An alert will be sent to the driver as well as passengers on board if we detect that the driver is drowsy. This can reduce the probability of an accident to occur and increase transport safety. First we calculate the eye lid closure, and we take a few frames to consider and detect the drowsiness which is EAR, and then while he yawns, the values of the mouth will be calculated in the same procedure as eyes which are MAR. The message alert will be sent if the values are greater than the threshold value considered alerting everyone in the vehicle.
Real-Time Driver Drowsiness Detection Using Visual Behaviour and MTCNN Algorithm
Advs in Intelligent Syst., Computing
Proceedings of Third International Conference on Intelligent Computing, Information and Control Systems ; Kapitel : 67 ; 913-921
2022-03-15
9 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
OpenCV , Eye aspect ratio (EAR) , Mouth aspect ratio (MAR) , Histogram of oriented gradients (HOG) , Support vector machine (SVM) , Bayesian classifier , Multi-task cascade convolution neural network (MTCNN) Engineering , Computational Intelligence , Control, Robotics, Mechatronics , Artificial Intelligence , Science and Technology Studies
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