Automobile fatigue driving prediction method

Abstract

The invention discloses an automobile fatigue driving prediction method. The method comprises the following steps of successively constructing and arranging first to fourth grades of convolutional neural networks, inputting an image, using a first grade of the convolutional neural network to acquire a candidate face window and a corresponding bounding box regression vector, and through the first grade and a second grade of the convolutional neural networks, merging candidate windows which are highly overlapped; for the residual candidate windows, through a third grade of the convolutional neural network, using face characteristic point mark information to predict and identify a human eye area; according to an eye characteristic point, segmenting an eye area, inputting a fourth grade of the convolutional neural network, through a depth learning algorithm, training a depth visual characteristic model of an eye image; making a video collected by a camera successfully pass through a CNN1, a CNN2, a CNN3 and a CNN4, and distinguishing a closing state of eyes; and calculating a driver fatigue visual assessment parameter PERCLOS, and when a PERCLOS value is greater than 40%, determining that a driver begins to feel fatigue or is in a fatigue state, and outputting an early warning signal. By using the method of the invention, the fatigue state of the driver under various conditions of illumination, an attitude and an expression can be detected, detection result robustness is high, and influences of factors of the illumination, the attitude, the expression and the like on driver fatigue detection are effectively overcome.

Description

technical field [0001] The invention relates to the fields of computer vision and image processing, in particular to a method for predicting automobile fatigue driving. Background technique [0002] With the development of the economy, the ownership of cars has increased dramatically. According to the prediction results of the program of the research group of the Development Research Center of the State Council, the number of automobiles in China will reach 56.69 million in 2010, 194 million in 2016, and will exceed 250 million in 2020. The mileage of highways is increasing day by day, and the average speed of highway traffic has been greatly improved, which has undoubtedly played a role in promoting economic development. But at the same time, traffic accidents have also increased accordingly, which has brought great threats to people's lives and property safety. According to statistics provided by the traffic control department of the Ministry of Public Security of China,...

AI Technical Summary

Problems solved by technology

[0007] In view of the fact that the image classification based on deep learning is effective, and the rapid development of big data and computer hardware promotes the effect of deep learning classification, some researchers use big data for face recognition, but because the target of the eyes is small, it involves occlusion. , expression changes, light changes and other complex situations, so currently this kind of method cannot be applied to eye fatigue detection, and there is no fatigue detection method based on multi-task learning cascaded deep convolutional neural network

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