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ABSTRACT
On average 1200 road accidents are recorded daily in India out of which 400 lead to direct death and the rest get affected badly. The major reason for these accidents is drowsiness caused by both sleep and alcohol. Due to driving for a long time or intoxication, drivers might feel sleepy which is the biggest distraction for
them while driving. This distraction might cause the death of the driver and other passengers in the vehicle and at the same time it also causes the death of people in the other vehicles and pedestrians. This mistake of one person on the road would take their own life and also take the lives of others and put respective families in sorrow and tough situations. To prevent such accidents we, team 5A propose a system which alerts the
driver if he/she feels drowsy. To accomplish this, we implement the solution using a computer-vision-based machine learning model. The driver’s face is detected by a face recognition algorithm continuously using a camera and the face of the driver is captured. The face of the driver is given as input to a classification algorithm which is trained with a data set of images of drowsy and non-drowsy faces. The algorithm uses landmark detection to classify the face as drowsy or not drowsy. If the driver’s face is drowsy, a voice alert is generated by the system. This alert can make the driver aware that he/she is feeling drowsy and the necessary actions can then be taken by the driver. This system can be used in any vehicle on the road to ensure the safety of the people who are travelling and prevent accidents which are caused due to the drowsiness of the driver.
Index-Terms: Computer Vision, Machine Learning, Convolutional Neural Networks
INTRODUCTION
A car accident is the major cause of death around 1.3 million people die every year. The majority of these accidents are caused because of distraction or the drowsiness of the driver. The countless number of people drives for long distance every day and night on the highway. Drowsiness appears in situations of stress and fatigue in an unexpected and inopportune way, and it may be produced by sleep disorders,
certain types of medications, and even, boredom situations, for example, driving for a long time. In this way, drowsiness produces dangerous situations and increases the probability that an accident occurs.
In this context, it is important to use new technologies to design and build systems that will monitor drivers, and measure their level of attention throughout the whole driving process. To prevent such accidents, our team has come up with a solution for this. In this system, a camera is used to record the user’s visual characteristics. We use face detection and CNN techniques and try to detect the drowsiness of the driver, if he/she is drowsy then the alarm will be generated. So that the driver will be cautious and take
preventive measures. Driver drowsiness detection contributes to the decrease in the number of deaths occurring in traffic accidents.
Back Propagation:
Backpropagation is an algorithm commonly used to train neural networks. When the neural network is initialized, weights are set for its elements, called neurons. Inputs are loaded, they are passed through the network of neurons, and the network provides an output for each one, given the initial weights. Backpropagation helps to adjust the weights of the neurons so that the result comes closer and closer to the known true result. Backpropagation, short for “backward propagation of errors,” is an algorithm for supervised learning of artificial neural networks using gradient descent. Given an artificial
neural network and an error function, the method calculates the gradient of the error function for the neural network’s weights. It is a generalization of the delta rule for perceptrons to multilayer feedforward neural networks. The “backwards” part of the name stems from the fact that the calculation of the gradient proceeds backwards through the network, with the gradient of the final layer of weights being calculated first and the gradient of the first layer of weights being calculated last. Partial computations of the gradient from one layer are reused in the computation of the gradient for the previous layer. This backwards flow of the error information allows for efficient computation of the gradient at each layer versus the naive approach of calculating the gradient of each layer separately.
Backpropagation’s popularity has experienced a recent resurgence given the widespread adoption of deep neural networks for image recognition and speech recognition. It is considered an efficient algorithm, and modern implementations take advantage of specialized GPUs to further improve performance.
