Abstract
Road traffic accidents caused by fatigue driving are common causes of human casualties. In this paper, we present a driver fatigue detection algorithm using two-stream network models with multi-facial features. The algorithm consists of four parts: (1) Positioning mouth and eye with multi-task cascaded convolutional neural networks (MTCNNs). (2) Extracting the static features from a partial facial image. (3) Extracting the dynamic features from a partial facial optical flow. (4) Combining both static and dynamic features using a two-stream neural network to make the classification. The main contribution of this paper is the combination of a two-stream network and multi-facial features for driver fatigue detection. Two-stream networks can combine static and dynamic image information, while partial facial images as network inputs can focus on fatigue-related information, which brings better performance. Moreover, we applied gamma correction to enhance image contrast, which can help our method achieve better results, noted by an increased accuracy of 2% in night environments. Finally, an accuracy of 97.06% was achieved on the National Tsing Hua University Driver Drowsiness Detection (NTHU-DDD) dataset.
Highlights
According to the National Highway Traffic Safety Administration report, 22% to 24% of traffic accidents are caused by driver fatigue
In driver fatigue detection based on computer vision, some researchers focus on the driver’s mouth movement [7,8], while others study the relation between fatigue and eye movement [9,10,11,12]
We proposed a method using multi-task cascaded convolutional neural networks (MTCNNs) to extract the mouth and the left eye area, and use gamma correction to enhance the image contrast
Summary
According to the National Highway Traffic Safety Administration report, 22% to 24% of traffic accidents are caused by driver fatigue. There are various techniques to measure driver fatigue These techniques can be generally classified into three categories: vehicle-focused, driver-focused, and computer vision-based methods. Driver-focused methods focus on psychophysiological parameters such as using electroencephalogram (EEG) data [2,3,4], which would be an intrusive mechanism for detecting driver status. Charlotte [6] combined vehicle-focused and driver-focused methods, measuring physiological and behavioral indicators to analyze and prevent accidents. In driver fatigue detection based on computer vision, some researchers focus on the driver’s mouth movement [7,8], while others study the relation between fatigue and eye movement [9,10,11,12]. Ji et al [15] combined multiple visual cues to get a more robust and accurate model, which included eyelid movement, gaze movement, head movement, and facial expression
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