Driver Distraction Detection Method Based on Continuous Head Pose Estimation

Zuopeng Zhao,Yi Xu,Lan Zhang,Hualin Yan,Xinzheng Xu,Zhongxin Zhang,Sili Xia,Fabio Solari

doi:10.1155/2020/9606908

Abstract

In view of the fact that the detection of driver’s distraction is a burning issue, this study chooses the driver’s head pose as the evaluation parameter for driving distraction and proposes a driver distraction method based on the head pose. The effects of single regression and classification combined with regression are compared in terms of accuracy, and four kinds of classical networks are improved and trained using 300W-LP and AFLW datasets. The HPE_Resnet50 with the best accuracy is selected as the head pose estimator and applied to the ten-category distracted driving dataset SF3D to obtain 20,000 sets of head pose data. The differences between classes are discussed qualitatively and quantitatively. The analysis of variance shows that there is a statistically significant difference in head posture between safe driving and all kinds of distracted driving at 95% and 90% confidence levels, and the postures of all kinds of driving movements are distributed in a specific Euler angle range, which provides a characteristic basis for the design of subsequent recognition methods. In addition, according to the continuity of human movement, this paper also selects 90 drivers’ videos to analyze the difference in head pose between safe driving and distracted driving frame by frame. By calculating the spatial distance and sample statistics, the results provide the reference point, spatial range, and threshold of safe driving under this driving condition. Experimental results show that the average error of HPE_Resnet50 in AFLW2000 is 6.17° and that there is an average difference of 12.4° to 54.9° in the Euler angle between safe driving and nine kinds of distracted driving on SF3D.

Highlights

Introduction e World Report on RoadTraffic Injury Prevention points out that many factors have an impact on traffic safety, such as the mental state of drivers, the degree of fatigue, and whether the driver is drunk or distracted
In the field of computer vision, by inputting the head image containing the target user into the computer and combining it with image processing technology, the pose parameters of the head in space are determined based on calculation and prediction. ere are two ways of expressing this pose parameter: face orientation and Euler rotation angles [3]
Compared with the expression based on face orientation, the Euler rotation angle is more accurate and comprehensive

Summary

Head Pose Estimation

Different from the fully connected network, the core of the CNN is the convolution layer that completes image feature extraction through convolution operation [19]. The head pose estimator (HPE_Resnet) used in this paper is improved on the classical residual network. (i) Based on the routine, the head pose estimation is regarded as a typical regression problem, while network parameters are continuously optimized by the loss function to approach the label value. (ii) e range of head pose parameters is divided into several equal intervals, and the regression problem is transformed into a classification problem [21]. For (i), only regression training is carried out, the loss of the whole Euler angle is calculated, and a single mean square error is used as the loss function: loss : MSE y, y′􏼁 􏽐ni 1 yi − y′i􏼁2.

Distraction Detection

Experiment and Result

Results and Discussion

Participants number